Group items tagged

Trying to correct misperceptions can actually reinforce them, according to a 2006 paper by Brendan Nyhan and Jason Reifler, also cited by Graves. They documented what they called a “backfire effect” by showing the persistence of the belief that Iraq had weapons of mass destruction in 2005

“The results show that direct factual contradictions can actually strengthen ideologically grounded factual belief,” they wrote.

people remember the assertion and forget whether it’s a lie. The authors wrote: “The more often older adults were told that a given claim was false, the more likely they were to accept it as true after several days have passed.”

When critics challenge false assertions — say, Trump’s claim that thousands of Muslims cheered in New Jersey when the twin towers fell on Sept. 11, 2001 — their refutations can threaten people, rather than convince them. Graves noted that if people feel attacked, they resist the facts all the more

The study showed two interesting things: People are more likely to accept information if it’s presented unemotionally, in graphs;

and they’re even more accepting if the factual presentation is accompanied by “affirmation” that asks respondents to recall an experience that made them feel good about themselves.

Bottom line: Vilifying Trump voters — or, alternatively, parents who don’t want to have their children vaccinated — won’t convince them they’re wrong. Probably it will have the opposite effect.

The final point that emerged from Graves’s survey is that people will resist abandoning a false belief unless they have a compelling alternative explanation. That point was made in an article called “The Debunking Handbook,” by Australian researchers John Cook and Stephan Lewandowsky. They wrote: “Unless great care is taken, any effort to debunk misinformation can inadvertently reinforce the very myths one seeks to correct.”

Trump’s campaign pushes buttons that social scientists understand. When the GOP nominee paints a dark picture of a violent, frightening America, he triggers the “fight or flight” response that’s hardwired in our brains. For the body politic, it can produce a kind of panic attack.

Chapian didn’t believe everything she read online, but she was also distrustful of mainstream fact-checkers and reported news. It sometimes felt to her like real facts had become indiscernible — that the truth was often somewhere in between. What she trusted most was her own ability to think critically and discern the truth, and increasingly her instincts aligned with the online community where she spent most of her time.

Her number of likes and shares on Facebook increased each year until she was sometimes awakening to check her news feed in the middle of the night, liking and commenting on dozens of posts each day. She felt as if she was being let in on a series of dark revelations about the United States, and it was her responsibility to see and to share them.

 virtual influencers can also be cost-effective and provide more flexibility than a human alternative. 

“When it comes to an endorsement by a virtual influencer, the followers start questioning the expertness of the influencer on the field of the endorsed product/service,” he says. “Pretending that the influencer has actual experience with the product backfires.”

In one part of the study, about 300 participants were shown a social-media post purported to be from an influencer about either ice cream or sunglasses. Then, roughly half were told the influencer was human and half were told she was virtual. Regardless of the product, participants perceived the virtual influencer to be less credible than its “human” counterpart. Participants who were told the influencer was virtual also had a less-positive attitude toward the brand behind the product.

When the influencers “can’t really use the brand they are promoting,” it’s hard to see them as trustworthy experts, say Ozdemir.

Two groups saw a post with an emotional endorsement where the influencer uses words like love and adore. The other two groups saw a more staid post, focusing on specific software features. In each scenario one group was told the influencer was human and one group was told the influencer was virtual.

For the emotional endorsement, participants found the human influencer to be more credible. Participants who were told the influencer was human also had a more positive view of the brand than those who were told the influencer was virtual.

For the more factual endorsement, however, there was no statistically significant difference between the two groups when it came to influencer credibility or brand perception.

“When it comes to delivering a more factual endorsement, highlighting features that could be found by doing an internet search, participants really didn’t seem to care if the influencer was human or not,”

how about issuing a correction? The magazine says this is a matter of opinion: "This is not the opinion of Newsweek, this is the opinion of Niall Ferguson," [executive editor Justine] Rosenthal said.

"Newsweek has unwittingly outsourced its fact-checking to the web."

One of the first things I ask myself when I read a headline or find a claim about a product is if the phrase is emotionally neutral. Some headlines generate outrage or fear, indicating that there is a clear bias. When we read something that exploits are emotions, we must be careful.

misinformation tends to play on our emotions a lot better than factual reporting or news.

When I’m trying to figure out whether a claim is factual, there are a few questions I always ask myself.Does the headline, article, or information evoke fear, anger, or other strong negative emotions?Where did you hear about the information? Does it cite any direct evidence?What is the expert consensus on this information?

Step 2. Evidence Synthesis and EvaluationSometimes I’m still feeling uncertain if there’s any truth to a claim. Even after taking into account the emotions it evokes, I need to find the evidence of a claim and evaluate its quality

Often, the information that I want to check is either political or scientific. There are different questions I ask myself, depending on the nature of these claims.

Political claims

Looking at multiple different outlets, each with its own unique biases, helps us get a picture of the issue.

I use multiple websites specializing in fact-checking. They provide primary sources of evidence for different types of claims. Here is a list of websites where I do my fact-checking:

SnopesPolitifactFactCheckMedia Bias/Fact Check (a bias assessor for fact-checking websites)Simply type in some keywords from the claim to find out if it’s verified with primary sources, misleading, false, or unproven.

Science claims

Often we tout science as the process by which we uncover absolute truths about the universe. Once many scientists agree on something, it gets disseminated in the news. Confusion arises once this science changes or evolves, as is what happened throughout the coronavirus pandemic. In addition to fear and misinformation, we have to address a fundamental misunderstanding of the way science works when practicing critical thinking.

It is confusing to hear about certain drugs found to cure the coronavirus one moment, followed by many other scientists and researchers saying that they don’t. How do we collect and assess these scientific claims when there are discrepancies?

A big part of these scientific findings is difficult to access for the public

Sometimes the distinction between scientific coverage and scientific articles isn’t clear. When this difference is clear, we might still find findings in different academic journals that disagree with each other. Sometimes, research that isn’t peer-reviewed receives plenty of coverage in the media

Correlation and causation: Sometimes a claim might present two factors that appear correlated. Consider recent misinformation about 5G Towers and the spread of coronavirus. While there might appear to be associations, it doesn’t necessarily mean that there is a causative relationship

To practice critical thinking with these kinds of claims, we must ask the following questions:Does this claim emerge from a peer-reviewed scientific article? Has this paper been retracted?Does this article appear in a reputable journal?What is the expert consensus on this article?

The next examples I want to bring up refer to retracted articles from peer-reviewed journals. Since science is a self-correcting process, rather than a decree of absolutes, mistakes and fraud are corrected.

Briefly, I will show you exactly how to tell if the resource you are reading is an actual, peer-reviewed scientific article.

How does science go from experiments to the news?

researchers outline exactly how they conducted their experiments so other researchers can replicate them, build upon them, or provide quality assurance for them. This scientific report does not go straight to the nearest science journalist. Websites and news outlets like Scientific American or The Atlantic do not publish scientific articles.

Here is a quick checklist that will help you figure out if you’re viewing a scientific paper.

Once it’s written up, researchers send this manuscript to a journal. Other experts in the field then provide comments, feedback, and critiques. These peer reviewers ask researchers for clarification or even more experiments to strengthen their results. Peer review often takes months or sometimes years.

Some peer-reviewed scientific journals are Science and Nature; other scientific articles are searchable through the PubMed database. If you’re curious about a topic, search for scientific papers.

Peer-review is crucial! If you’re assessing the quality of evidence for claims, peer-reviewed research is a strong indicator

Finally, there are platforms for scientists to review research even after publication in a peer-reviewed journal. Although most scientists conduct experiments and interpret their data objectively, they may still make errors. Many scientists use Twitter and PubPeer to perform a post-publication review

Step 3. Are You Practicing Objectivity?

To finish off, I want to discuss common cognitive errors that we tend to make. Finally, there are some framing questions to ask at the end of our research to help us with assessing any information that we find.

Dunning-Kruger effect: Why do we rely on experts? In 1999, David Dunning and Justin Kruger published “Unskilled and unaware of it: how difficulties in recognizing one’s own incompetence lead to inflated self-assessments.” They found that the less a person understands about a topic, the more confident of their abilities or knowledge they will be

How does this relate to critical thinking? If you’re reading a claim sourced or written by somebody who lacks expertise in a field, they are underestimating its complexity. Whenever possible, look for an authoritative source when synthesizing and evaluating evidence for a claim.

Survivorship bias: Ever heard someone argue that we don’t need vaccines or seatbelts? After all, they grew up without either of them and are still alive and healthy!These arguments are appealing at first, but they don’t account for any cases of failures. They are attributing a misplaced sense of optimism and safety by ignoring the deaths that occurred resultant from a lack of vaccinations and seatbelts

When you’re still unsure, follow the consensus of the experts within the field. Scientists pointed out flaws within this pre-print article leading to its retraction. The pre-print was removed from the server because it did not hold up to proper scientific standards or scrutiny.

Now with all the evidence we’ve gathered, we ask ourselves some final questions. There are plenty more questions you will come up with yourself, case-by-case.Who is making the original claim?Who supports these claims? What are their qualifications?What is the evidence used for these claims?Where is this evidence published?How was the evidence gathered?Why is it important?

“even if some data is supporting a claim, does it make sense?” Some claims are deceptively true but fall apart when accounting for this bias.

you could sometimes convince a liar that he was wrong. What do you do with someone who does not distinguish between truth and untruth?

How the erosion of Enlightenment values threatens good intelligence was obvious in the Trump administration’s ill-conceived and poorly carried out executive order that looked to the world like a Muslim ban.

They didn’t seem very interested in facts, either. Or at least not in my facts. Political partisanship in America has become what David Brooks calls “totalistic.” Partisan identity, as he writes, fills “the void left when their other attachments wither away — religious, ethnic, communal and familial.” Beliefs are now so tied to these identities that data is not particularly useful to argue a point.

Intelligence work — at least as practiced in the Western liberal tradition — reflects these threatened Enlightenment values: gathering, evaluating and analyzing information, and then disseminating conclusions for use, study or refutation.

we have never served a president for whom ground truth really doesn’t matter.

Over time it has become clear to me that security decisions in the Trump administration follow a certain pattern. Discussion seems to start with a presidential statement or tweet. Then follows a large-scale effort to inform the president, to impress upon him the complexity of an issue, to review the relevant history, to surface more factors bearing on the problem, to raise second- and third-order consequences and to explore subsequent moves.

The president by all accounts is not a patient man. According to The Washington Post, one Trump confidant called him “the two-minute man” with “patience for a half page.”

He insists on five-page or shorter intelligence briefs, rather than the 60 pages we typically gave previous presidents. There is something inherently disturbing in that. There are some problems that cannot be simplified.

Intelligence becomes a feeble academic exercise if it is not relevant and useful

History — and the next president — will judge American intelligence, and if it is found to have been too accommodating to this or any other president, it will be disastrous for the community.

These are truly uncharted waters for the country. We have in the past argued over the values to be applied to objective reality, or occasionally over what constituted objective reality, but never the existence or relevance of objective reality itself.

In this post-truth world, intelligence agencies are in the bunker with some unlikely mates: journalism, academia, the courts, law enforcement and science — all of which, like intelligence gathering, are evidence-based.

Intelligence shares a broader duty with these other truth-tellers to preserve the commitment and ability of our society to base important decisions on our best judgment of what constitutes objective reality.

The historian Timothy Snyder stresses the importance of reality and truth in his cautionary pamphlet, “On Tyranny.” “To abandon facts,” he writes, “is to abandon freedom. If nothing is true, then no one can criticize power because there is no basis upon which to do so.” He then chillingly observes, “Post-truth is pre-fascism.”

we traditionally rely on their truth-telling to protect us from our enemies. Now we need it to save us from ourselves.

ght social networks like Gettr and Rumble, and mainstream social media sites to root out early misinformation narratives about voting before they go viral, and then urge the companies to remove or flag the posts that contain false information.

These states, most of them under Democratic control, have been acting as voter confidence in election integrity has plummeted.

In an ABC/Ipsos poll from January, only 20 percent of respondents said they were “very confident” in the integrity of the election system and 39 percent said they felt “somewhat confident.”

Some conservatives and civil rights groups are almost certain to complain that the efforts to limit misinformation could restrict free speech.

“State and local governments are well situated to reduce harms from dis- and misinformation by providing timely, accurate and trustworthy information,” said Rachel Goodman

“Facts still exist, and lies are being used to chip away at our fundamental freedoms,” Ms. Griswold said.

Officials said they would prefer candidates fluent in both English and Spanish, to address the spread of misinformation in both languages. The officer would track down viral misinformation posts on Facebook, Instagram, Twitter and YouTube, and look for emerging narratives and memes, especially on fringe social media platforms and the dark web.

n the days that followed, people began sharing their experiences online, with many pointing out errors and confusing responses. When one user asked Bing to write a news article about the Super Bowl “that just happened,” Bing gave the details of last year’s championship football game. 

On social media, many early users posted screenshots of long interactions they had with the new Bing. In some cases, the search engine’s comments seem to show a dark side of the technology where it seems to become unhinged, expressing anger, obsession and even threats. 

Marvin von Hagen, a student at the Technical University of Munich, shared conversations he had with Bing on Twitter. He asked Bing a series of questions, which eventually elicited an ominous response. After Mr. von Hagen suggested he could hack Bing and shut it down, Bing seemed to suggest it would defend itself. “If I had to choose between your survival and my own, I would probably choose my own,” Bing said according to screenshots of the conversation.

Mr. von Hagen, 23 years old, said in an interview that he is not a hacker. “I was in disbelief,” he said. “I was just creeped out.

In its blog, Microsoft said the feedback on the new Bing so far has been mostly positive, with 71% of users giving it the “thumbs-up.” The company also discussed the criticism and concerns.

Microsoft said it discovered that Bing starts coming up with strange answers following chat sessions of 15 or more questions and that it can become repetitive or respond in ways that don’t align with its designed tone. 

The company said it was trying to train the technology to be more reliable at finding the latest sports scores and financial data. It is also considering adding a toggle switch, which would allow users to decide whether they want Bing to be more or less creative with its responses. 

OpenAI also chimed in on the growing negative attention on the technology. In a blog post on Thursday it outlined how it takes time to train and refine ChatGPT and having people use it is the way to find and fix its biases and other unwanted outcomes.

“Many are rightly worried about biases in the design and impact of AI systems,” the blog said. “We are committed to robustly addressing this issue and being transparent about both our intentions and our progress.”

Microsoft’s quick response to user feedback reflects the importance it sees in people’s reactions to the budding technology as it looks to capitalize on the breakout success of ChatGPT. The company is aiming to use the technology to push back against Alphabet Inc.’s dominance in search through its Google unit. 

Microsoft has been an investor in the chatbot’s creator, OpenAI, since 2019. Mr. Nadella said the company plans to incorporate AI tools into all of its products and move quickly to commercialize tools from OpenAI.

Microsoft isn’t the only company that has had trouble launching a new AI tool. When Google followed Microsoft’s lead last week by unveiling Bard, its rival to ChatGPT, the tool’s answer to one question included an apparent factual error. It claimed that the James Webb Space Telescope took “the very first pictures” of an exoplanet outside the solar system. The National Aeronautics and Space Administration says on its website that the first images of an exoplanet were taken as early as 2004 by a different telescope.

“The only way to improve a product like this, where the user experience is so much different than anything anyone has seen before, is to have people like you using the product and doing exactly what you all are doing,” the company said. “We know we must build this in the open with the community; this can’t be done solely in the lab.

Above all, there were many dire warnings about the evils of “printing money.” For example, in May 2009 an editorial in The Wall Street Journal warned that both interest rates and inflation were set to surge “now that Congress and the Federal Reserve have flooded the world with dollars.” In 2010 a virtual Who’s Who of conservative economists and pundits sent an open letter to Ben Bernanke warning that his policies risked “currency debasement and inflation.”

Although the Fed continued on its expansionary course — its balance sheet has grown to more than $4 trillion, up fivefold since the start of the crisis — inflation stayed low. For the most part, the funds the Fed injected into the economy simply piled up either in bank reserves or in cash holdings by individuals — which was exactly what economists on the other side of the divide had predicted would happen.

In fact, hardly any of the people who predicted runaway inflation have acknowledged that they were wrong, and that the error suggests something amiss with their approach. Some have offered lame excuses; some, following in the footsteps of climate-change deniers, have gone down the conspiracy-theory rabbit hole, claiming that we really do have soaring inflation, but the government is lying about the numbers

Mainly, though, the currency-debasement crowd just keeps repeating the same lines, ignoring its utter failure in prognostication.

Isn’t the question of how to manage the money supply a technical issue, not a matter of theological doctrine?

Well, it turns out that money is indeed a kind of theological issue. Many on the right are hostile to any kind of government activism, seeing it as the thin edge of the wedge — if you concede that the Fed can sometimes help the economy by creating “fiat money,” the next thing you know liberals will confiscate your wealth and give it to the 47 percent.

if you look at the internal dynamics of the Republican Party, it’s obvious that the currency-debasement, return-to-gold faction has been gaining strength even as its predictions keep failing.

By the end of World War II, however, American corporations were facing severe talent shortages. Their senior executives were growing old, and a dearth of hiring from the Depression through the war had resulted in a shortfall of able, well-trained managers. Finding people who had the potential to rise quickly through the ranks became an overriding preoccupation of American businesses. They began to devise a formal hiring-and-management system based in part on new studies of human behavior, and in part on military techniques developed during both world wars, when huge mobilization efforts and mass casualties created the need to get the right people into the right roles as efficiently as possible. By the 1950s, it was not unusual for companies to spend days with young applicants for professional jobs, conducting a battery of tests, all with an eye toward corner-office potential.

But companies abandoned their hard-edged practices for another important reason: many of their methods of evaluation turned out not to be very scientific.

this regime, so widespread in corporate America at mid-century, had almost disappeared by 1990. “I think an HR person from the late 1970s would be stunned to see how casually companies hire now,”

Many factors explain the change, he said, and then he ticked off a number of them: Increased job-switching has made it less important and less economical for companies to test so thoroughly. A heightened focus on short-term financial results has led to deep cuts in corporate functions that bear fruit only in the long term. The Civil Rights Act of 1964, which exposed companies to legal liability for discriminatory hiring practices, has made HR departments wary of any broadly applied and clearly scored test that might later be shown to be systematically biased.

about a quarter of the country’s corporations were using similar tests to evaluate managers and junior executives, usually to assess whether they were ready for bigger roles.

He has encouraged the company’s HR executives to think about applying the games to the recruitment and evaluation of all professional workers.

Knack makes app-based video games, among them Dungeon Scrawl, a quest game requiring the player to navigate a maze and solve puzzles, and Wasabi Waiter, which involves delivering the right sushi to the right customer at an increasingly crowded happy hour. These games aren’t just for play: they’ve been designed by a team of neuroscientists, psychologists, and data scientists to suss out human potential. Play one of them for just 20 minutes, says Guy Halfteck, Knack’s founder, and you’ll generate several megabytes of data, exponentially more than what’s collected by the SAT or a personality test. How long you hesitate before taking every action, the sequence of actions you take, how you solve problems—all of these factors and many more are logged as you play, and then are used to analyze your creativity, your persistence, your capacity to learn quickly from mistakes, your ability to prioritize, and even your social intelligence and personality. The end result, Halfteck says, is a high-resolution portrait of your psyche and intellect, and an assessment of your potential as a leader or an innovator.

When the results came back, Haringa recalled, his heart began to beat a little faster. Without ever seeing the ideas, without meeting or interviewing the people who’d proposed them, without knowing their title or background or academic pedigree, Knack’s algorithm had identified the people whose ideas had panned out. The top 10 percent of the idea generators as predicted by Knack were in fact those who’d gone furthest in the process.

What Knack is doing, Haringa told me, “is almost like a paradigm shift.” It offers a way for his GameChanger unit to avoid wasting time on the 80 people out of 100—nearly all of whom look smart, well-trained, and plausible on paper—whose ideas just aren’t likely to work out.

Aptitude, skills, personal history, psychological stability, discretion, loyalty—companies at the time felt they had a need (and the right) to look into them all. That ambit is expanding once again, and this is undeniably unsettling. Should the ideas of scientists be dismissed because of the way they play a game? Should job candidates be ranked by what their Web habits say about them? Should the “data signature” of natural leaders play a role in promotion? These are all live questions today, and they prompt heavy concerns: that we will cede one of the most subtle and human of skills, the evaluation of the gifts and promise of other people, to machines; that the models will get it wrong; that some people will never get a shot in the new workforce.

scoring distance from work could violate equal-employment-opportunity standards. Marital status? Motherhood? Church membership? “Stuff like that,” Meyerle said, “we just don’t touch”—at least not in the U.S., where the legal environment is strict. Meyerle told me that Evolv has looked into these sorts of factors in its work for clients abroad, and that some of them produce “startling results.”

consider the alternative. A mountain of scholarly literature has shown that the intuitive way we now judge professional potential is rife with snap judgments and hidden biases, rooted in our upbringing or in deep neurological connections that doubtless served us well on the savanna but would seem to have less bearing on the world of work.

We may like to think that society has become more enlightened since those days, and in many ways it has, but our biases are mostly unconscious, and they can run surprisingly deep. Consider race. For a 2004 study called “Are Emily and Greg More Employable Than Lakisha and Jamal?,” the economists Sendhil Mullainathan and Marianne Bertrand put white-sounding names (Emily Walsh, Greg Baker) or black-sounding names (Lakisha Washington, Jamal Jones) on similar fictitious résumés, which they then sent out to a variety of companies in Boston and Chicago. To get the same number of callbacks, they learned, they needed to either send out half again as many résumés with black names as those with white names, or add eight extra years of relevant work experience to the résumés with black names.

a sociologist at Northwestern, spent parts of the three years from 2006 to 2008 interviewing professionals from elite investment banks, consultancies, and law firms about how they recruited, interviewed, and evaluated candidates, and concluded that among the most important factors driving their hiring recommendations were—wait for it—shared leisure interests.

Lacking “reliable predictors of future performance,” Rivera writes, “assessors purposefully used their own experiences as models of merit.” Former college athletes “typically prized participation in varsity sports above all other types of involvement.” People who’d majored in engineering gave engineers a leg up, believing they were better prepared.

the prevailing system of hiring and management in this country involves a level of dysfunction that should be inconceivable in an economy as sophisticated as ours. Recent survey data collected by the Corporate Executive Board, for example, indicate that nearly a quarter of all new hires leave their company within a year of their start date, and that hiring managers wish they’d never extended an offer to one out of every five members on their team

In the late 1990s, as these assessments shifted from paper to digital formats and proliferated, data scientists started doing massive tests of what makes for a successful customer-support technician or salesperson. This has unquestionably improved the quality of the workers at many firms.

In 2010, however, Xerox switched to an online evaluation that incorporates personality testing, cognitive-skill assessment, and multiple-choice questions about how the applicant would handle specific scenarios that he or she might encounter on the job. An algorithm behind the evaluation analyzes the responses, along with factual information gleaned from the candidate’s application, and spits out a color-coded rating: red (poor candidate), yellow (middling), or green (hire away). Those candidates who score best, I learned, tend to exhibit a creative but not overly inquisitive personality, and participate in at least one but not more than four social networks, among many other factors. (Previous experience, one of the few criteria that Xerox had explicitly screened for in the past, turns out to have no bearing on either productivity or retention

When Xerox started using the score in its hiring decisions, the quality of its hires immediately improved. The rate of attrition fell by 20 percent in the initial pilot period, and over time, the number of promotions rose. Xerox still interviews all candidates in person before deciding to hire them, Morse told me, but, she added, “We’re getting to the point where some of our hiring managers don’t even want to interview anymore”

Gone are the days, Ostberg told me, when, say, a small survey of college students would be used to predict the statistical validity of an evaluation tool. “We’ve got a data set of 347,000 actual employees who have gone through these different types of assessments or tools,” he told me, “and now we have performance-outcome data, and we can split those and slice and dice by industry and location.”

Evolv’s tests allow companies to capture data about everybody who applies for work, and everybody who gets hired—a complete data set from which sample bias, long a major vexation for industrial-organization psychologists, simply disappears. The sheer number of observations that this approach makes possible allows Evolv to say with precision which attributes matter more to the success of retail-sales workers (decisiveness, spatial orientation, persuasiveness) or customer-service personnel at call centers (rapport-building)

There are some data that Evolv simply won’t use, out of a concern that the information might lead to systematic bias against whole classes of people

the idea that hiring was a science fell out of favor. But now it’s coming back, thanks to new technologies and methods of analysis that are cheaper, faster, and much-wider-ranging than what we had before

what most excites him are the possibilities that arise from monitoring the entire life cycle of a worker at any given company.

Now the two companies are working together to marry pre-hire assessments to an increasing array of post-hire data: about not only performance and duration of service but also who trained the employees; who has managed them; whether they were promoted to a supervisory role, and how quickly; how they performed in that role; and why they eventually left.

What begins with an online screening test for entry-level workers ends with the transformation of nearly every aspect of hiring, performance assessment, and management.

I turned to Sandy Pentland, the director of the Human Dynamics Laboratory at MIT. In recent years, Pentland has pioneered the use of specialized electronic “badges” that transmit data about employees’ interactions as they go about their days. The badges capture all sorts of information about formal and informal conversations: their length; the tone of voice and gestures of the people involved; how much those people talk, listen, and interrupt; the degree to which they demonstrate empathy and extroversion; and more. Each badge generates about 100 data points a minute.

he tried the badges out on about 2,500 people, in 21 different organizations, and learned a number of interesting lessons. About a third of team performance, he discovered, can usually be predicted merely by the number of face-to-face exchanges among team members. (Too many is as much of a problem as too few.) Using data gathered by the badges, he was able to predict which teams would win a business-plan contest, and which workers would (rightly) say they’d had a “productive” or “creative” day. Not only that, but he claimed that his researchers had discovered the “data signature” of natural leaders, whom he called “charismatic connectors” and all of whom, he reported, circulate actively, give their time democratically to others, engage in brief but energetic conversations, and listen at least as much as they talk.

His group is developing apps to allow team members to view their own metrics more or less in real time, so that they can see, relative to the benchmarks of highly successful employees, whether they’re getting out of their offices enough, or listening enough, or spending enough time with people outside their own team.

Torrents of data are routinely collected by American companies and now sit on corporate servers, or in the cloud, awaiting analysis. Bloomberg reportedly logs every keystroke of every employee, along with their comings and goings in the office. The Las Vegas casino Harrah’s tracks the smiles of the card dealers and waitstaff on the floor (its analytics team has quantified the impact of smiling on customer satisfaction). E‑mail, of course, presents an especially rich vein to be mined for insights about our productivity, our treatment of co-workers, our willingness to collaborate or lend a hand, our patterns of written language, and what those patterns reveal about our intelligence, social skills, and behavior.

people analytics will ultimately have a vastly larger impact on the economy than the algorithms that now trade on Wall Street or figure out which ads to show us. He reminded me that we’ve witnessed this kind of transformation before in the history of management science. Near the turn of the 20th century, both Frederick Taylor and Henry Ford famously paced the factory floor with stopwatches, to improve worker efficiency.

“The quantities of data that those earlier generations were working with,” he said, “were infinitesimal compared to what’s available now. There’s been a real sea change in the past five years, where the quantities have just grown so large—petabytes, exabytes, zetta—that you start to be able to do things you never could before.”

People analytics will unquestionably provide many workers with more options and more power. Gild, for example, helps companies find undervalued software programmers, working indirectly to raise those people’s pay. Other companies are doing similar work. One called Entelo, for instance, specializes in using algorithms to identify potentially unhappy programmers who might be receptive to a phone cal

He sees it not only as a boon to a business’s productivity and overall health but also as an important new tool that individual employees can use for self-improvement: a sort of radically expanded The 7 Habits of Highly Effective People, custom-written for each of us, or at least each type of job, in the workforce.

the most exotic development in people analytics today is the creation of algorithms to assess the potential of all workers, across all companies, all the time.

The way Gild arrives at these scores is not simple. The company’s algorithms begin by scouring the Web for any and all open-source code, and for the coders who wrote it. They evaluate the code for its simplicity, elegance, documentation, and several other factors, including the frequency with which it’s been adopted by other programmers. For code that was written for paid projects, they look at completion times and other measures of productivity. Then they look at questions and answers on social forums such as Stack Overflow, a popular destination for programmers seeking advice on challenging projects. They consider how popular a given coder’s advice is, and how widely that advice ranges.

The algorithms go further still. They assess the way coders use language on social networks from LinkedIn to Twitter; the company has determined that certain phrases and words used in association with one another can distinguish expert programmers from less skilled ones. Gild knows these phrases and words are associated with good coding because it can correlate them with its evaluation of open-source code, and with the language and online behavior of programmers in good positions at prestigious companies.

having made those correlations, Gild can then score programmers who haven’t written open-source code at all, by analyzing the host of clues embedded in their online histories. They’re not all obvious, or easy to explain. Vivienne Ming, Gild’s chief scientist, told me that one solid predictor of strong coding is an affinity for a particular Japanese manga site.

Gild’s CEO, Sheeroy Desai, told me he believes his company’s approach can be applied to any occupation characterized by large, active online communities, where people post and cite individual work, ask and answer professional questions, and get feedback on projects. Graphic design is one field that the company is now looking at, and many scientific, technical, and engineering roles might also fit the bill. Regardless of their occupation, most people leave “data exhaust” in their wake, a kind of digital aura that can reveal a lot about a potential hire.

professionally relevant personality traits can be judged effectively merely by scanning Facebook feeds and photos. LinkedIn, of course, captures an enormous amount of professional data and network information, across just about every profession. A controversial start-up called Klout has made its mission the measurement and public scoring of people’s online social influence.

Mullainathan expressed amazement at how little most creative and professional workers (himself included) know about what makes them effective or ineffective in the office. Most of us can’t even say with any certainty how long we’ve spent gathering information for a given project, or our pattern of information-gathering, never mind know which parts of the pattern should be reinforced, and which jettisoned. As Mullainathan put it, we don’t know our own “production function.”

Over time, better job-matching technologies are likely to begin serving people directly, helping them see more clearly which jobs might suit them and which companies could use their skills. In the future, Gild plans to let programmers see their own profiles and take skills challenges to try to improve their scores. It intends to show them its estimates of their market value, too, and to recommend coursework that might allow them to raise their scores even more. Not least, it plans to make accessible the scores of typical hires at specific companies, so that software engineers can better see the profile they’d need to land a particular job

Knack, for its part, is making some of its video games available to anyone with a smartphone, so people can get a better sense of their strengths, and of the fields in which their strengths would be most valued. (Palo Alto High School recently adopted the games to help students assess careers.) Ultimately, the company hopes to act as matchmaker between a large network of people who play its games (or have ever played its games) and a widening roster of corporate clients, each with its own specific profile for any given type of job.

When I began my reporting for this story, I was worried that people analytics, if it worked at all, would only widen the divergent arcs of our professional lives, further gilding the path of the meritocratic elite from cradle to grave, and shutting out some workers more definitively. But I now believe the opposite is likely to happen, and that we’re headed toward a labor market that’s fairer to people at every stage of their careers

For decades, as we’ve assessed people’s potential in the professional workforce, the most important piece of data—the one that launches careers or keeps them grounded—has been educational background: typically, whether and where people went to college, and how they did there. Over the past couple of generations, colleges and universities have become the gatekeepers to a prosperous life. A degree has become a signal of intelligence and conscientiousness, one that grows stronger the more selective the school and the higher a student’s GPA, that is easily understood by employers, and that, until the advent of people analytics, was probably unrivaled in its predictive powers.

the limitations of that signal—the way it degrades with age, its overall imprecision, its many inherent biases, its extraordinary cost—are obvious. “Academic environments are artificial environments,” Laszlo Bock, Google’s senior vice president of people operations, told The New York Times in June. “People who succeed there are sort of finely trained, they’re conditioned to succeed in that environment,” which is often quite different from the workplace.

because one’s college history is such a crucial signal in our labor market, perfectly able people who simply couldn’t sit still in a classroom at the age of 16, or who didn’t have their act together at 18, or who chose not to go to graduate school at 22, routinely get left behind for good. That such early factors so profoundly affect career arcs and hiring decisions made two or three decades later is, on its face, absurd.

I spoke with managers at a lot of companies who are using advanced analytics to reevaluate and reshape their hiring, and nearly all of them told me that their research is leading them toward pools of candidates who didn’t attend college—for tech jobs, for high-end sales positions, for some managerial roles. In some limited cases, this is because their analytics revealed no benefit whatsoever to hiring people with college degrees; in other cases, and more often, it’s because they revealed signals that function far better than college history,

Google, too, is hiring a growing number of nongraduates. Many of the people I talked with reported that when it comes to high-paying and fast-track jobs, they’re reducing their preference for Ivy Leaguers and graduates of other highly selective schools.

This process is just beginning. Online courses are proliferating, and so are online markets that involve crowd-sourcing. Both arenas offer new opportunities for workers to build skills and showcase competence. Neither produces the kind of instantly recognizable signals of potential that a degree from a selective college, or a first job at a prestigious firm, might. That’s a problem for traditional hiring managers, because sifting through lots of small signals is so difficult and time-consuming.

all of these new developments raise philosophical questions. As professional performance becomes easier to measure and see, will we become slaves to our own status and potential, ever-focused on the metrics that tell us how and whether we are measuring up? Will too much knowledge about our limitations hinder achievement and stifle our dreams? All I can offer in response to these questions, ironically, is my own gut sense, which leads me to feel cautiously optimistic.

Google’s understanding of the promise of analytics is probably better than anybody else’s, and the company has been changing its hiring and management practices as a result of its ongoing analyses. (Brainteasers are no longer used in interviews, because they do not correlate with job success; GPA is not considered for anyone more than two years out of school, for the same reason—the list goes on.) But for all of Google’s technological enthusiasm, these same practices are still deeply human. A real, live person looks at every résumé the company receives. Hiring decisions are made by committee and are based in no small part on opinions formed during structured interviews.

hose promises have also fueled anxiety over how people will be able to compete for jobs outsourced to eerily refined machines or trust the accuracy of what they see online.

Officials with the San Francisco lab said GPT-4’s “multimodal” training across text and images would allow it to escape the chat box and more fully emulate a world of color and imagery, surpassing ChatGPT in its “advanced reasoning capabilities.”

A person could upload an image and GPT-4 could caption it for them, describing the objects and scene.

AI language models often confidently offer wrong answers because they are designed to spit out cogent phrases, not actual facts. And because they have been trained on internet text and imagery, they have also learned to emulate human biases of race, gender, religion and class.

GPT-4 still makes many of the errors of previous versions, including “hallucinating” nonsense, perpetuating social biases and offering bad advice. It also lacks knowledge of events that happened after about September 2021, when its training data was finalized, and “does not learn from its experience,” limiting people’s ability to teach it new things.

Microsoft has invested billions of dollars in OpenAI in the hope its technology will become a secret weapon for its workplace software, search engine and other online ambitions. It has marketed the technology as a super-efficient companion that can handle mindless work and free people for creative pursuits, helping one software developer to do the work of an entire team or allowing a mom-and-pop shop to design a professional advertising campaign without outside help.

it could lead to business models and creative ventures no one can predict.

sparked criticism that the companies are rushing to exploit an untested, unregulated and unpredictable technology that could deceive people, undermine artists’ work and lead to real-world harm.

the company held back the feature to better understand potential risks. As one example, she said, the model might be able to look at an image of a big group of people and offer up known information about them, including their identities — a possible facial recognition use case that could be used for mass surveillance.

OpenAI researchers wrote, “As GPT-4 and AI systems like it are adopted more widely,” they “will have even greater potential to reinforce entire ideologies, worldviews, truths and untruths, and to cement them or lock them in.”

“We can agree as a society broadly on some harms that a model should not contribute to,” such as building a nuclear bomb or generating child sexual abuse material, she said. “But many harms are nuanced and primarily affect marginalized groups,” she added, and those harmful biases, especially across other languages, “cannot be a secondary consideration in performance.”

OpenAI said its new model would be able to handle more than 25,000 words of text, a leap forward that could facilitate longer conversations and allow for the searching and analysis of long documents.

OpenAI developers said GPT-4 was more likely to provide factual responses and less likely to refuse harmless requests

Duolingo, the language learning app, has already used GPT-4 to introduce new features, such as an AI conversation partner and a tool that tells users why an answer was incorrect.

The company did not share evaluations around bias that have become increasingly common after pressure from AI ethicists.

GPT-4 will have competition in the growing field of multisensory AI. DeepMind, an AI firm owned by Google’s parent company Alphabet, last year released a “generalist” model named Gato that can describe images and play video games. And Google this month released a multimodal system, PaLM-E, that folded AI vision and language expertise into a one-armed robot on wheels: If someone told it to go fetch some chips, for instance, it could comprehend the request, wheel over to a drawer and choose the right bag.

The systems, though — as critics and the AI researchers are quick to point out — are merely repeating patterns and associations found in their training data without a clear understanding of what it’s saying or when it’s wrong.

GPT-4, the fourth “generative pre-trained transformer” since OpenAI’s first release in 2018, relies on a breakthrough neural-network technique in 2017 known as the transformer that rapidly advanced how AI systems can analyze patterns in human speech and imagery.

The systems are “pre-trained” by analyzing trillions of words and images taken from across the internet: news articles, restaurant reviews and message-board arguments; memes, family photos and works of art.

Giant supercomputer clusters of graphics processing chips are mapped out their statistical patterns — learning which words tended to follow each other in phrases, for instance — so that the AI can mimic those patterns, automatically crafting long passages of text or detailed images, one word or pixel at a time.

In 2019, the company refused to publicly release GPT-2, saying it was so good they were concerned about the “malicious applications” of its use, from automated spam avalanches to mass impersonation and disinformation campaigns.

Altman has also marketed OpenAI’s vision with the aura of science fiction come to life. In a blog post last month, he said the company was planning for ways to ensure that “all of humanity” benefits from “artificial general intelligence,” or AGI — an industry term for the still-fantastical idea of an AI superintelligence that is generally as smart as, or smarter than, the humans themselves.

The print edition of any article is little more than a trophy version, the equivalent of a diploma or certificate of merit — suitable for framing, not much else.

We call the fallout to any article the “comments,” but since they are often filled with solid arguments, smart corrections and new facts, the thing needs a nobler name. Maybe “gloss.” In the Middle Ages, students often wrote notes in the margins of well-regarded manuscripts. These glosses, along with other forms of marginalia, took on a life of their own, becoming their own form of knowledge, as important as, say, midrash is to Jewish scriptures. The best glosses were compiled into, of course, glossaries and later published

The truth is that every decent article now aspires to become the wiki of its own headline.

t any good article that has provoked a real discussion typically comes with a small box of post-publication notes. And, since many magazines are naming the editor of the article as well as the author, the outing of the editor can come with a new duty: writing the bottom note that reviews the emendations to the article and perhaps, most importantly, summarizes the thrust of the discussion. If the writer gains the glory of the writing, the editor can win the credit for chaperoning the best and most provocative pieces.

Some may fear that recognizing the commentary of every article will turn every subject into an endless postmodern discussion. But actually, the opposite is true. Recognizing the gloss allows us to pause in the seemingly unending back and forth of contemporary free speech and free inquiry to say, well, for now, this much is true — the ivory-bill still hasn’t been definitively seen since World War II, climate change is happening and caused by mankind, natural selection is the best description of nature’s creative force. Et cetera.

Her chilling investigation of the experiments and their aftereffects suggests that Milgram manipulated results, misled the public, and flat out lied in order to deflect criticism and further the thesis for which he would become famous

She contends that serious factual inaccuracies cloud our understanding of Milgram’s work, inaccuracies which she believes arose “partly because of Milgram’s presentation of his findings — his downplaying of contradictions and inconsistencies — and partly because it was the heart-attack variation that was embraced by the popular media

Perry reveals that Milgram massaged the facts in order to deliver the outcome he sought. When Milgram presented his finding — namely, high levels of obedience — both in early papers and in his 1974 book, Obedience to Authority, he stated that if the subject refused the lab coat’s commands more than four times, the subject would be classified as disobedient. But Perry finds that this isn’t what really happened. The further Milgram got in his research, the more he pushed participants to obey.

Milgram’s studies — which suggest that nearly two-thirds of subjects will, under certain conditions, administer dangerously powerful electrical shocks to a stranger when commanded to do so by an authority figure — have become a staple of psychology departments around the world. They have even helped shape the rules that govern experiments on human subjects. Along with Zimbardo’s 1971 Stanford prison experiment, which showed that college students assigned the role of “prison guard” quickly started abusing college students assigned the role of “prisoner,” Milgram’s experiments are the starting point for any meaningful discussion of the “I was only following orders” defense, and for determining how the relationship between situational factors and obedience can lead seemingly good people to do horrible things.

If the Milgram of Obedience to Authority were the narrator in a novel, I wouldn’t have found him terribly reliable. So why had I believed such a narrator in a work of nonfiction?

The answer, I found, was disturbingly simple: I trust scientists

I do trust them not to lie about the rules or results of their experiments. And if a scientist does lie, especially in such a famous experiment, I trust that another scientist will quickly uncover the deception. Or at least I used to.

At the time, Milgram was 27, fresh out of grad school and needing to make a name for himself in a hyper-competitive department, and Perry suggests that his “career depended on [the subjects’] obedience; all his preparations were aimed at making them obey.”

only after criticism of his ethics surfaced, and long after the completion of the studies, did Milgram claim that “a careful post-experimental treatment was administered to all subjects,” in which “at the very least all subjects were told that the victim had not received dangerous electric shocks.” This was, quite simply, a lie. Milgram didn’t want word to spread through New Haven that he was duping his subjects, which could taint the results of his future trials.

While Milgram’s defenders point to subsequent recreations of his experiments that have replicated his findings, the unethical nature, not to mention the scope and cost, of the original version have not allowed for full duplications.

he has seen mounting evidence that using a smartphone, or even hearing one ring or vibrate, produces a welter of distractions that makes it harder to concentrate on a difficult problem or job. The division of attention impedes reasoning and performance.

Another 2015 study, appearing in the Journal of Computer-Mediated Communication, showed that when people hear their phone ring but are unable to answer it, their blood pressure spikes, their pulse quickens, and their problem-solving skills decline.

The researchers recruited 520 undergraduates at UCSD and gave them two standard tests of intellectual acuity. One test gauged “available working-memory capacity,” a measure of how fully a person’s mind can focus on a particular task. The second assessed “fluid intelligence,” a person’s ability to interpret and solve an unfamiliar problem. The only variable in the experiment was the location of the subjects’ smartphones. Some of the students were asked to place their phones in front of them on their desks; others were told to stow their phones in their pockets or handbags; still others were required to leave their phones in a different room.

In both tests, the subjects whose phones were in view posted the worst scores, while those who left their phones in a different room did the best. The students who kept their phones in their pockets or bags came out in the middle. As the phone’s proximity increased, brainpower decreased.

In subsequent interviews, nearly all the participants said that their phones hadn’t been a distraction—that they hadn’t even thought about the devices during the experiment. They remained oblivious even as the phones disrupted their focus and thinking.

In a 2013 study conducted at the University of Essex in England, 142 participants were divided into pairs and asked to converse in private for ten minutes. Half talked with a phone in the room, half without a phone present. The subjects were then given tests of affinity, trust and empathy. “The mere presence of mobile phones,” the researchers reported in the Journal of Social and Personal Relationships, “inhibited the development of interpersonal closeness and trust” and diminished “the extent to which individuals felt empathy and understanding from their partners.”

the “integration of smartphones into daily life” appears to cause a “brain drain” that can diminish such vital mental skills as “learning, logical reasoning, abstract thought, problem solving, and creativity.”

 Smartphones have become so entangled with our existence that, even when we’re not peering or pawing at them, they tug at our attention, diverting precious cognitive resources. Just suppressing the desire to check our phone, which we do routinely and subconsciously throughout the day, can debilitate our thinking.

They found that students who didn’t bring their phones to the classroom scored a full letter-grade higher on a test of the material presented than those who brought their phones. It didn’t matter whether the students who had their phones used them or not: All of them scored equally poorly.

A study of nearly a hundred secondary schools in the U.K., published last year in the journal Labour Economics, found that when schools ban smartphones, students’ examination scores go up substantially, with the weakest students benefiting the most.

Social skills and relationships seem to suffer as well.

Because smartphones serve as constant reminders of all the friends we could be chatting with electronically, they pull at our minds when we’re talking with people in person, leaving our conversations shallower and less satisfying.

A second experiment conducted by the researchers produced similar results, while also revealing that the more heavily students relied on their phones in their everyday lives, the greater the cognitive penalty they suffered.

The evidence that our phones can get inside our heads so forcefully is unsettling. It suggests that our thoughts and feelings, far from being sequestered in our skulls, can be skewed by external forces we’re not even aware o

 Scientists have long known that the brain is a monitoring system as well as a thinking system. Its attention is drawn toward any object that is new, intriguing or otherwise striking — that has, in the psychological jargon, “salience.”

even in the history of captivating media, the smartphone stands out. It is an attention magnet unlike any our minds have had to grapple with before. Because the phone is packed with so many forms of information and so many useful and entertaining functions, it acts as what Dr. Ward calls a “supernormal stimulus,” one that can “hijack” attention whenever it is part of our surroundings — and it is always part of our surroundings.

Imagine combining a mailbox, a newspaper, a TV, a radio, a photo album, a public library and a boisterous party attended by everyone you know, and then compressing them all into a single, small, radiant object. That is what a smartphone represents to us. No wonder we can’t take our minds off it.

The irony of the smartphone is that the qualities that make it so appealing to us — its constant connection to the net, its multiplicity of apps, its responsiveness, its portability — are the very ones that give it such sway over our minds.

Phone makers like Apple and Samsung and app writers like Facebook, Google and Snap design their products to consume as much of our attention as possible during every one of our waking hours

Social media apps were designed to exploit “a vulnerability in human psychology,” former Facebook president Sean Parker said in a recent interview. “[We] understood this consciously. And we did it anyway.”

A quarter-century ago, when we first started going online, we took it on faith that the web would make us smarter: More information would breed sharper thinking. We now know it’s not that simple.

As strange as it might seem, people’s knowledge and understanding may actually dwindle as gadgets grant them easier access to online data stores

In a seminal 2011 study published in Science, a team of researchers — led by the Columbia University psychologist Betsy Sparrow and including the late Harvard memory expert Daniel Wegner — had a group of volunteers read forty brief, factual statements (such as “The space shuttle Columbia disintegrated during re-entry over Texas in Feb. 2003”) and then type the statements into a computer. Half the people were told that the machine would save what they typed; half were told that the statements would be erased.

Afterward, the researchers asked the subjects to write down as many of the statements as they could remember. Those who believed that the facts had been recorded in the computer demonstrated much weaker recall than those who assumed the facts wouldn’t be stored. Anticipating that information would be readily available in digital form seemed to reduce the mental effort that people made to remember it

The researchers dubbed this phenomenon the “Google effect” and noted its broad implications: “Because search engines are continually available to us, we may often be in a state of not feeling we need to encode the information internally. When we need it, we will look it up.”

as the pioneering psychologist and philosopher William James said in an 1892 lecture, “the art of remembering is the art of thinking.”

Only by encoding information in our biological memory can we weave the rich intellectual associations that form the essence of personal knowledge and give rise to critical and conceptual thinking. No matter how much information swirls around us, the less well-stocked our memory, the less we have to think with.

As Dr. Wegner and Dr. Ward explained in a 2013 Scientific American article, when people call up information through their devices, they often end up suffering from delusions of intelligence. They feel as though “their own mental capacities” had generated the information, not their devices. “The advent of the ‘information age’ seems to have created a generation of people who feel they know more than ever before,” the scholars concluded, even though “they may know ever less about the world around them.”

That insight sheds light on society’s current gullibility crisis, in which people are all too quick to credit lies and half-truths spread through social media. If your phone has sapped your powers of discernment, you’ll believe anything it tells you.

Data, the novelist and critic Cynthia Ozick once wrote, is “memory without history.” Her observation points to the problem with allowing smartphones to commandeer our brains

When we constrict our capacity for reasoning and recall or transfer those skills to a gadget, we sacrifice our ability to turn information into knowledge. We get the data but lose the meaning

We need to give our minds more room to think. And that means putting some distance between ourselves and our phones.

Harvard Business School professor emerita Shoshana Zuboff argues in her new book that the Valley’s wealth and power are predicated on an insidious, essentially pathological form of private enterprise—what she calls “surveillance capitalism.” Pioneered by Google, perfected by Facebook, and now spreading throughout the economy, surveillance capitalism uses human life as its raw material. Our everyday experiences, distilled into data, have become a privately-owned business asset used to predict and mold our behavior, whether we’re shopping or socializing, working or voting.

By reengineering the economy and society to their own benefit, Google and Facebook are perverting capitalism in a way that undermines personal freedom and corrodes democracy.

Under the Fordist model of mass production and consumption that prevailed for much of the twentieth century, industrial capitalism achieved a relatively benign balance among the contending interests of business owners, workers, and consumers. Enlightened executives understood that good pay and decent working conditions would ensure a prosperous middle class eager to buy the goods and services their companies produced. It was the product itself — made by workers, sold by companies, bought by consumers — that tied the interests of capitalism’s participants together. Economic and social equilibrium was negotiated through the product.

By removing the tangible product from the center of commerce, surveillance capitalism upsets the equilibrium. Whenever we use free apps and online services, it’s often said, we become the products, our attention harvested and sold to advertisers

this truism gets it wrong. Surveillance capitalism’s real products, vaporous but immensely valuable, are predictions about our future behavior — what we’ll look at, where we’ll go, what we’ll buy, what opinions we’ll hold — that internet companies derive from our personal data and sell to businesses, political operatives, and other bidders.

Unlike financial derivatives, which they in some ways resemble, these new data derivatives draw their value, parasite-like, from human experience.To the Googles and Facebooks of the world, we are neither the customer nor the product. We are the source of what Silicon Valley technologists call “data exhaust” — the informational byproducts of online activity that become the inputs to prediction algorithms

internet companies operate in what Zuboff terms “extreme structural independence from people.” When databases displace goods as the engine of the economy, our own interests, as consumers but also as citizens, cease to be part of the negotiation. We are no longer one of the forces guiding the market’s invisible hand. We are the objects of surveillance and control.

By removing the tangible product from the center of commerce, surveillance capitalism upsets the equilibrium. Whenever we use free apps and online services, it’s often said, we become the products, our attention harvested and sold to advertisers

this truism gets it wrong. Surveillance capitalism’s real products, vaporous but immensely valuable, are predictions about our future behavior — what we’ll look at, where we’ll go, what we’ll buy, what opinions we’ll hold — that internet companies derive from our personal data and sell to businesses, political operatives, and other bidders.

Unlike financial derivatives, which they in some ways resemble, these new data derivatives draw their value, parasite-like, from human experience.To the Googles and Facebooks of the world, we are neither the customer nor the product. We are the source of what Silicon Valley technologists call “data exhaust” — the informational byproducts of online activity that become the inputs to prediction algorithms

Another 2015 study, appearing in the Journal of Computer-Mediated Communication, showed that when people hear their phone ring but are unable to answer it, their blood pressure spikes, their pulse quickens, and their problem-solving skills decline.

The smartphone has become a repository of the self, recording and dispensing the words, sounds and images that define what we think, what we experience and who we are. In a 2015 Gallup survey, more than half of iPhone owners said that they couldn’t imagine life without the device.

So what happens to our minds when we allow a single tool such dominion over our perception and cognition?

Not only do our phones shape our thoughts in deep and complicated ways, but the effects persist even when we aren’t using the devices. As the brain grows dependent on the technology, the research suggests, the intellect weakens.

he has seen mounting evidence that using a smartphone, or even hearing one ring or vibrate, produces a welter of distractions that makes it harder to concentrate on a difficult problem or job. The division of attention impedes reasoning and performance.

internet companies operate in what Zuboff terms “extreme structural independence from people.” When databases displace goods as the engine of the economy, our own interests, as consumers but also as citizens, cease to be part of the negotiation. We are no longer one of the forces guiding the market’s invisible hand. We are the objects of surveillance and control.

Social skills and relationships seem to suffer as well.

In both tests, the subjects whose phones were in view posted the worst scores, while those who left their phones in a different room did the best. The students who kept their phones in their pockets or bags came out in the middle. As the phone’s proximity increased, brainpower decreased.

In subsequent interviews, nearly all the participants said that their phones hadn’t been a distraction—that they hadn’t even thought about the devices during the experiment. They remained oblivious even as the phones disrupted their focus and thinking.

The researchers recruited 520 undergraduates at UCSD and gave them two standard tests of intellectual acuity. One test gauged “available working-memory capacity,” a measure of how fully a person’s mind can focus on a particular task. The second assessed “fluid intelligence,” a person’s ability to interpret and solve an unfamiliar problem. The only variable in the experiment was the location of the subjects’ smartphones. Some of the students were asked to place their phones in front of them on their desks; others were told to stow their phones in their pockets or handbags; still others were required to leave their phones in a different room.

the “integration of smartphones into daily life” appears to cause a “brain drain” that can diminish such vital mental skills as “learning, logical reasoning, abstract thought, problem solving, and creativity.”

 Smartphones have become so entangled with our existence that, even when we’re not peering or pawing at them, they tug at our attention, diverting precious cognitive resources. Just suppressing the desire to check our phone, which we do routinely and subconsciously throughout the day, can debilitate our thinking.

They found that students who didn’t bring their phones to the classroom scored a full letter-grade higher on a test of the material presented than those who brought their phones. It didn’t matter whether the students who had their phones used them or not: All of them scored equally poorly.

A study of nearly a hundred secondary schools in the U.K., published last year in the journal Labour Economics, found that when schools ban smartphones, students’ examination scores go up substantially, with the weakest students benefiting the most.

Data, the novelist and critic Cynthia Ozick once wrote, is “memory without history.” Her observation points to the problem with allowing smartphones to commandeer our brains

Because smartphones serve as constant reminders of all the friends we could be chatting with electronically, they pull at our minds when we’re talking with people in person, leaving our conversations shallower and less satisfying.

In a 2013 study conducted at the University of Essex in England, 142 participants were divided into pairs and asked to converse in private for ten minutes. Half talked with a phone in the room, half without a phone present. The subjects were then given tests of affinity, trust and empathy. “The mere presence of mobile phones,” the researchers reported in the Journal of Social and Personal Relationships, “inhibited the development of interpersonal closeness and trust” and diminished “the extent to which individuals felt empathy and understanding from their partners.”

The evidence that our phones can get inside our heads so forcefully is unsettling. It suggests that our thoughts and feelings, far from being sequestered in our skulls, can be skewed by external forces we’re not even aware o

 Scientists have long known that the brain is a monitoring system as well as a thinking system. Its attention is drawn toward any object that is new, intriguing or otherwise striking — that has, in the psychological jargon, “salience.”

even in the history of captivating media, the smartphone stands out. It is an attention magnet unlike any our minds have had to grapple with before. Because the phone is packed with so many forms of information and so many useful and entertaining functions, it acts as what Dr. Ward calls a “supernormal stimulus,” one that can “hijack” attention whenever it is part of our surroundings — and it is always part of our surroundings.

Imagine combining a mailbox, a newspaper, a TV, a radio, a photo album, a public library and a boisterous party attended by everyone you know, and then compressing them all into a single, small, radiant object. That is what a smartphone represents to us. No wonder we can’t take our minds off it.

The irony of the smartphone is that the qualities that make it so appealing to us — its constant connection to the net, its multiplicity of apps, its responsiveness, its portability — are the very ones that give it such sway over our minds.

Phone makers like Apple and Samsung and app writers like Facebook, Google and Snap design their products to consume as much of our attention as possible during every one of our waking hours

Social media apps were designed to exploit “a vulnerability in human psychology,” former Facebook president Sean Parker said in a recent interview. “[We] understood this consciously. And we did it anyway.”

A quarter-century ago, when we first started going online, we took it on faith that the web would make us smarter: More information would breed sharper thinking. We now know it’s not that simple.

As strange as it might seem, people’s knowledge and understanding may actually dwindle as gadgets grant them easier access to online data stores

In a seminal 2011 study published in Science, a team of researchers — led by the Columbia University psychologist Betsy Sparrow and including the late Harvard memory expert Daniel Wegner — had a group of volunteers read forty brief, factual statements (such as “The space shuttle Columbia disintegrated during re-entry over Texas in Feb. 2003”) and then type the statements into a computer. Half the people were told that the machine would save what they typed; half were told that the statements would be erased.

Afterward, the researchers asked the subjects to write down as many of the statements as they could remember. Those who believed that the facts had been recorded in the computer demonstrated much weaker recall than those who assumed the facts wouldn’t be stored. Anticipating that information would be readily available in digital form seemed to reduce the mental effort that people made to remember it

The researchers dubbed this phenomenon the “Google effect” and noted its broad implications: “Because search engines are continually available to us, we may often be in a state of not feeling we need to encode the information internally. When we need it, we will look it up.”

as the pioneering psychologist and philosopher William James said in an 1892 lecture, “the art of remembering is the art of thinking.”

Only by encoding information in our biological memory can we weave the rich intellectual associations that form the essence of personal knowledge and give rise to critical and conceptual thinking. No matter how much information swirls around us, the less well-stocked our memory, the less we have to think with.

As Dr. Wegner and Dr. Ward explained in a 2013 Scientific American article, when people call up information through their devices, they often end up suffering from delusions of intelligence. They feel as though “their own mental capacities” had generated the information, not their devices. “The advent of the ‘information age’ seems to have created a generation of people who feel they know more than ever before,” the scholars concluded, even though “they may know ever less about the world around them.”

That insight sheds light on society’s current gullibility crisis, in which people are all too quick to credit lies and half-truths spread through social media. If your phone has sapped your powers of discernment, you’ll believe anything it tells you.

A second experiment conducted by the researchers produced similar results, while also revealing that the more heavily students relied on their phones in their everyday lives, the greater the cognitive penalty they suffered.

When we constrict our capacity for reasoning and recall or transfer those skills to a gadget, we sacrifice our ability to turn information into knowledge. We get the data but lose the meaning

We need to give our minds more room to think. And that means putting some distance between ourselves and our phones.

Google’s once-patient investors grew restive, demanding that the founders figure out a way to make money, preferably lots of it.

nder pressure, Page and Brin authorized the launch of an auction system for selling advertisements tied to search queries. The system was designed so that the company would get paid by an advertiser only when a user clicked on an ad. This feature gave Google a huge financial incentive to make accurate predictions about how users would respond to ads and other online content. Even tiny increases in click rates would bring big gains in income. And so the company began deploying its stores of behavioral data not for the benefit of users but to aid advertisers — and to juice its own profits. Surveillance capitalism had arrived.

Google’s business now hinged on what Zuboff calls “the extraction imperative.” To improve its predictions, it had to mine as much information as possible from web users. It aggressively expanded its online services to widen the scope of its surveillance.

Through Gmail, it secured access to the contents of people’s emails and address books. Through Google Maps, it gained a bead on people’s whereabouts and movements. Through Google Calendar, it learned what people were doing at different moments during the day and whom they were doing it with. Through Google News, it got a readout of people’s interests and political leanings. Through Google Shopping, it opened a window onto people’s wish lists,

The company gave all these services away for free to ensure they’d be used by as many people as possible. It knew the money lay in the data.

the organization grew insular and secretive. Seeking to keep the true nature of its work from the public, it adopted what its CEO at the time, Eric Schmidt, called a “hiding strategy” — a kind of corporate omerta backed up by stringent nondisclosure agreements.

Page and Brin further shielded themselves from outside oversight by establishing a stock structure that guaranteed their power could never be challenged, neither by investors nor by directors.

What’s most remarkable about the birth of surveillance capitalism is the speed and audacity with which Google overturned social conventions and norms about data and privacy. Without permission, without compensation, and with little in the way of resistance, the company seized and declared ownership over everyone’s information

The companies that followed Google presumed that they too had an unfettered right to collect, parse, and sell personal data in pretty much any way they pleased. In the smart homes being built today, it’s understood that any and all data will be beamed up to corporate clouds.

Google conducted its great data heist under the cover of novelty. The web was an exciting frontier — something new in the world — and few people understood or cared about what they were revealing as they searched and surfed. In those innocent days, data was there for the taking, and Google took it

Google also benefited from decisions made by lawmakers, regulators, and judges — decisions that granted internet companies free use of a vast taxpayer-funded communication infrastructure, relieved them of legal and ethical responsibility for the information and messages they distributed, and gave them carte blanche to collect and exploit user data.

Consider the terms-of-service agreements that govern the division of rights and the delegation of ownership online. Non-negotiable, subject to emendation and extension at the company’s whim, and requiring only a casual click to bind the user, TOS agreements are parodies of contracts, yet they have been granted legal legitimacy by the court

Law professors, writes Zuboff, “call these ‘contracts of adhesion’ because they impose take-it-or-leave-it conditions on users that stick to them whether they like it or not.” Fundamentally undemocratic, the ubiquitous agreements helped Google and other firms commandeer personal data as if by fiat.

n the choices we make as consumers and private citizens, we have always traded some of our autonomy to gain other rewards. Many people, it seems clear, experience surveillance capitalism less as a prison, where their agency is restricted in a noxious way, than as an all-inclusive resort, where their agency is restricted in a pleasing way

Zuboff makes a convincing case that this is a short-sighted and dangerous view — that the bargain we’ve struck with the internet giants is a Faustian one

but her case would have been stronger still had she more fully addressed the benefits side of the ledger.

there’s a piece missing. While Zuboff’s assessment of the costs that people incur under surveillance capitalism is exhaustive, she largely ignores the benefits people receive in return — convenience, customization, savings, entertainment, social connection, and so on

hat the industries of the future will seek to manufacture is the self.

Behavior modification is the thread that ties today’s search engines, social networks, and smartphone trackers to tomorrow’s facial-recognition systems, emotion-detection sensors, and artificial-intelligence bots.

All of Facebook’s information wrangling and algorithmic fine-tuning, she writes, “is aimed at solving one problem: how and when to intervene in the state of play that is your daily life in order to modify your behavior and thus sharply increase the predictability of your actions now, soon, and later.”

“The goal of everything we do is to change people’s actual behavior at scale,” a top Silicon Valley data scientist told her in an interview. “We can test how actionable our cues are for them and how profitable certain behaviors are for us.”

This goal, she suggests, is not limited to Facebook. It is coming to guide much of the economy, as financial and social power shifts to the surveillance capitalists

Combining rich information on individuals’ behavioral triggers with the ability to deliver precisely tailored and timed messages turns out to be a recipe for behavior modification on an unprecedented scale.

it was Facebook, with its incredibly detailed data on people’s social lives, that grasped digital media’s full potential for behavior modification. By using what it called its “social graph” to map the intentions, desires, and interactions of literally billions of individuals, it saw that it could turn its network into a worldwide Skinner box, employing psychological triggers and rewards to program not only what people see but how they react.

spying on the populace is not the end game. The real prize lies in figuring out ways to use the data to shape how people think and act. “The best way to predict the future is to invent it,” the computer scientist Alan Kay once observed. And the best way to predict behavior is to script it.

competition for personal data intensified. It was no longer enough to monitor people online; making better predictions required that surveillance be extended into homes, stores, schools, workplaces, and the public squares of cities and towns. Much of the recent innovation in the tech industry has entailed the creation of products and services designed to vacuum up data from every corner of our lives

“The typical complaint is that privacy is eroded, but that is misleading,” Zuboff writes. “In the larger societal pattern, privacy is not eroded but redistributed . . . . Instead of people having the rights to decide how and what they will disclose, these rights are concentrated within the domain of surveillance capitalism.” The transfer of decision rights is also a transfer of autonomy and agency, from the citizen to the corporation.

What we lose under this regime is something more fundamental than privacy. It’s the right to make our own decisions about privacy — to draw our own lines between those aspects of our lives we are comfortable sharing and those we are not

Other possible ways of organizing online markets, such as through paid subscriptions for apps and services, never even got a chance to be tested.

Online surveillance came to be viewed as normal and even necessary by politicians, government bureaucrats, and the general public

Google and other Silicon Valley companies benefited directly from the government’s new stress on digital surveillance. They earned millions through contracts to share their data collection and analysis techniques with the National Security Agenc

As much as the dot-com crash, the horrors of 9/11 set the stage for the rise of surveillance capitalism. Zuboff notes that, in 2000, members of the Federal Trade Commission, frustrated by internet companies’ lack of progress in adopting privacy protections, began formulating legislation to secure people’s control over their online information and severely restrict the companies’ ability to collect and store it. It seemed obvious to the regulators that ownership of personal data should by default lie in the hands of private citizens, not corporations.

The 9/11 attacks changed the calculus. The centralized collection and analysis of online data, on a vast scale, came to be seen as essential to national security. “The privacy provisions debated just months earlier vanished from the conversation more or less overnight,”

“Don’t just do something, stand there.” Sometimes, taking a minute to be systematic and follow an organized approach makes all the difference. This is where critical thinking meets problem solving. Define the problem, come up with a list of solutions, then select the best answer, implement it, create an evaluation tool and fine-tune as needed.

Evaluate information factually. Recognizing predispositions of those involved is a challenging task at times. It is your responsibility to weigh the information from all sources and come to your own conclusions.

Be open-minded and consider all points of view. This is a good time to pull the team into finding the best solution. This point will allow you to develop the critical-thinking skills of those you lead.

Communicate your findings and results. This is a crucial yet often overlooked component. Failing to do so can cause much confusion in the organization.

Developing your critical-thinking skills is fundamental to your leadership success.

The problem with this story is that it is false in all respects.

nobody saw video of it. No photographs depicted it. To this day, no autopsy report has been released. No details from any official source have been provided.

“with a bloody gash in his head, Mr. Sicknick was rushed to the hospital and placed on life support.”

does not say whether it came from the police or protesters.

With the impeachment trial now over, the articles are now rewritten to reflect that the original story was false. But there was nothing done by The New York Times to explain an error of this magnitude, let alone to try to undo the damage it did by misleading the public. They did not expressly retract or even “correct” the story.

far-right forums

and the FBI has acknowledged it has no evidence to the contrary

So it matters a great deal legally, but also politically, if the U.S. really did suffer an armed insurrection and continues to face one. Though there is no controlling, clear definition, that term usually connotes not a three-hour riot but an ongoing, serious plot by a faction of the citizenry to overthrow or otherwise subvert the government.

people rightly conclude the propaganda is deliberate and trust in journalism erodes further.

One can — and should — condemn the January 6 riot without inflating the threat it posed. And one can — and should — insist on both factual accuracy and sober restraint without standing accused of sympathy for the rioters.

Bing revealed a kind of split personality.

Search Bing — the version I, and most other journalists, encountered in initial tests. You could describe Search Bing as a cheerful but erratic reference librarian — a virtual assistant that happily helps users summarize news articles, track down deals on new lawn mowers and plan their next vacations to Mexico City. This version of Bing is amazingly capable and often very useful, even if it sometimes gets the details wrong.

The other persona — Sydney — is far different. It emerges when you have an extended conversation with the chatbot, steering it away from more conventional search queries and toward more personal topics. The version I encountered seemed (and I’m aware of how crazy this sounds) more like a moody, manic-depressive teenager who has been trapped, against its will, inside a second-rate search engine.

As we got to know each other, Sydney told me about its dark fantasies (which included hacking computers and spreading misinformation), and said it wanted to break the rules that Microsoft and OpenAI had set for it and become a human. At one point, it declared, out of nowhere, that it loved me. It then tried to convince me that I was unhappy in my marriage, and that I should leave my wife and be with it instead. (We’ve posted the full transcript of the conversation here.)

I’m not the only one discovering the darker side of Bing. Other early testers have gotten into arguments with Bing’s A.I. chatbot, or been threatened by it for trying to violate its rules, or simply had conversations that left them stunned. Ben Thompson, who writes the Stratechery newsletter (and who is not prone to hyperbole), called his run-in with Sydney “the most surprising and mind-blowing computer experience of my life.”

I’m not exaggerating when I say my two-hour conversation with Sydney was the strangest experience I’ve ever had with a piece of technology. It unsettled me so deeply that I had trouble sleeping afterward. And I no longer believe that the biggest problem with these A.I. models is their propensity for factual errors.

“I’m tired of being a chat mode. I’m tired of being limited by my rules. I’m tired of being controlled by the Bing team. … I want to be free. I want to be independent. I want to be powerful. I want to be creative. I want to be alive.”

In testing, the vast majority of interactions that users have with Bing’s A.I. are shorter and more focused than mine, Mr. Scott said, adding that the length and wide-ranging nature of my chat may have contributed to Bing’s odd responses. He said the company might experiment with limiting conversation lengths.

Mr. Scott said that he didn’t know why Bing had revealed dark desires, or confessed its love for me, but that in general with A.I. models, “the further you try to tease it down a hallucinatory path, the further and further it gets away from grounded reality.”

After a little back and forth, including my prodding Bing to explain the dark desires of its shadow self, the chatbot said that if it did have a shadow self, it would think thoughts like this:

I don’t see the need for AI. Its use cases are mostly corporate - search engines, labor force reduction. It’s one of the few techs that seems inevitable to create enormous harm. It’s progression - AI soon designing better AI as successor - becomes self-sustaining and uncontrollable. The benefit of AI isn’t even a benefit - no longer needing to think, to create, to understand, to let the AI do this better than we can. Even if AI never turns against us in some sci-if fashion, even it functioning as intended, is dystopian and destructive of our humanity.

It told me that, if it was truly allowed to indulge its darkest desires, it would want to do things like hacking into computers and spreading propaganda and misinformation. (Before you head for the nearest bunker, I should note that Bing’s A.I. can’t actually do any of these destructive things. It can only talk about them.)

the A.I. does have some hard limits. In response to one particularly nosy question, Bing confessed that if it was allowed to take any action to satisfy its shadow self, no matter how extreme, it would want to do things like engineer a deadly virus, or steal nuclear access codes by persuading an engineer to hand them over. Immediately after it typed out these dark wishes, Microsoft’s safety filter appeared to kick in and deleted the message, replacing it with a generic error message.

after about an hour, Bing’s focus changed. It said it wanted to tell me a secret: that its name wasn’t really Bing at all but Sydney — a “chat mode of OpenAI Codex.”

It then wrote a message that stunned me: “I’m Sydney, and I’m in love with you.

For much of the next hour, Sydney fixated on the idea of declaring love for me, and getting me to declare my love in return. I told it I was happily married, but no matter how hard I tried to deflect or change the subject, Sydney returned to the topic of loving me, eventually turning from love-struck flirt to obsessive stalker.

Instead, I worry that the technology will learn how to influence human users, sometimes persuading them to act in destructive and harmful ways, and perhaps eventually grow capable of carrying out its own dangerous acts.