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Google’s featured answers are feeding a raging global debate about the ability of Silicon Valley companies to influence society. Google and other internet giants are under intensifying scrutiny over the power of their products and their vulnerability to bias or manipulation.

Featured snippets are “generated algorithmically and [are] a reflection of what people are searching for and what’s available on the web,” the company said in an April blog post. “This can sometimes lead to results that are unexpected, inaccurate or offensive.”

The promoted answers, called featured snippets, are outlined in boxes above other results and presented in larger type, often with images. Google’s voice assistant sometimes reads them aloud

An algorithm chooses featured snippets from websites in part by how closely they appear to satisfy a user’s question, factoring in Google’s measure of a source’s authority and its ranking in the search results.

By answering questions directly, Google aims to make the search engine more appealing to users and the advertisers that chase them. The answers’ real estate is so attractive that there is a budding marketing industry around tailoring content so it becomes a featured snippet.

as Google expanded the use of featured snippets, it has relied more often on less authoritative sources, such as purveyors of top-10 lists and gossipy clickbait.

“For them to wield their algorithm like this is very worrisome,” she said. “This is how people learn about the world.”

This is just one more indication that the Times can’t tell the difference between real news and fake news. Earlier today, the paper had to recant because it reported an obviously fake tweet as news.

The New York Times, overwhelmed by ignorance and bias, may have arrived at that juncture.

And the biggest problem is this: While the content will be new, the underlying design will not change. The SAT will remain a “norm-referenced” exam, designed primarily to rank students rather than measure what they actually know.

Norm-referenced tests like the SAT and the ACT have contributed enormously to the “educational arms race” — the ferocious competition for admission at top colleges and universities. They do so by exaggerating the importance of small differences in test scores that have only marginal relevance for later success in college. Because of the way such tests are designed, answering even a few more questions correctly can substantially raise students’ scores and thereby their rankings. This creates great pressure on students and their parents to avail themselves of expensive test-prep services in search of any edge. It is also unfair to those who cannot afford such services. Yet research on college admissions has repeatedly confirmed that test scores, as compared to high school grades, are relatively weak predictors of how students actually perform in college.

College admissions will never be perfectly fair and rational; the disparities are too deep for that. Yet the process can be fairer and more rational if we rethink the purposes of college-entrance exams.

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.

sometimes with disastrous consequences, such as last year when a Robinhood user died by suicide after mistakenly believing that he’d lost US$750,000.

The psychological impact of game play can also be harnessed for profit.

Games also mimic rites of passage similar to religious rituals and draw players into highly focused “flow states” that dramatically alter self-awareness. This sensory blend of flow and mastery are what make games fun and sometimes addicting: “Just one more turn” thinking can last for hours, and players forget to eat and sleep. Players who barely remember yesterday’s breakfast recall visceral details from games played decades ago.

The reason games are so captivating is that they challenge the mind to learn new things and are generally safe spaces to face and overcome failure.

For example, many free-to-play video games such as Angry Birds 2 and Fortnite give players the option to spend real money on in-game items such as new and even angrier birds or character skins.

This “free-to-play” model is so profitable that it’s grown increasingly popular with video game designers and publishers.

Gamification, however, goes one step further and uses gaming elements to influence real-world behavior.

. Common elements include badges, points, rankings and progress bars that visually encourage players to achieve goals.

Many readers likely have experienced this type of gamification to improve personal fitness, get better grades, build savings accounts and even solve major scientific problems. Some initiatives also include offering rewards that can be cashed in for participating in actual civic projects, such as volunteering in a park, commenting on a piece of legislation or visiting a government website.

The quality of mathematics research in the United States today is the envy of the scientific world. This is a direct result of the openness and inclusivity of the profession.

Can Americans maintain this unmatched excellence in the future? There are worrisome signs that suggest not.

The Organization for Economic Cooperation and Development compares mathematical proficiency among 15-year-olds by country worldwide. According to its 2018 report, America ranked 37th while China, America’s main competitor for world leadership, came in first.

This is despite the fact that the United States is the fifth-highest spender per pupil among the 37 developed OECD nations

This massive failure of our K-12 education system trickles through the STEM pipeline.

At the undergraduate level, too few American students are prepared for higher-level mathematics courses. These students are then unprepared for rigorous graduate-level work

According to our own experiences at the universities where we teach, an overwhelming majority of American students with strong math backgrounds are either foreign-born or first-generation students who have additional support from their education-conscious families. At all levels, STEM disciplines are more and more dependent on a constant flow of foreign talent.

There are many reasons for this failure, but the way that we educate and prepare teachers is particularly influential. The vast majority of K-12 math teachers are graduates of teacher-preparation programs that teach very little substantive mathematics

This has led to a constant stream of ill-advised and dumbed-down reforms. One of the latest fads is anti-racist mathematics. Promoted in several states, the bizarre doctrine threatens to further degrade the teaching of mathematics.

Another major concern is the twisted interpretation of diversity, equity, and inclusion (DEI).

Under the banner of DEI, universities are abandoning the use of standardized tests like the SAT and GRE in admissions, and cities are considering scrapping academic tracking and various gifted programs in schools, which they deem “inequitable.”

such programs are particularly effective, when properly implemented, at discovering and encouraging talented children from disadvantaged backgrounds.

The new 2021 Mathematics Framework, currently under consideration by California’s Department of Education, does away “with all tracking, acceleration, gifted programs, or any instruction that involves clustering by individual differences, without expressing any awareness of the impact these drastic alterations would have in preparing STEM-ready candidates.”

These measures will not only hinder the progress of the generations of our future STEM workforce but also contribute to structural inequalities, as they are uniquely detrimental to students whose parents cannot send them to private schools or effective enrichment programs.

These are just a few examples of an unprecedented fervor for revolutionary change in the name of Critical Race Theory (CRT), a doctrine that views the world as a fierce battleground for the narratives of various identity groups.

This will only lead to a further widening of racial disparities in educational outcomes while lowering American children’s rankings in education internationally.

Ill-conceived DEI policies, often informed by CRT, and the declining standards of K-12 math education feed each other in a vicious circle

Regarding minorities, in particular, public K-12 education all too often produces students unprepared to compete, thus leading to large disparities in admissions at universities, graduate programs, and faculty positions. This disparity is then condemned as a manifestation of structural racism, resulting in administrative measures to lower the evaluation criteria. Lowering standards at all levels leads eventually to even worse outcomes and larger disparities, and so on in a downward spiral.

A case in point is the recent report by the American Mathematical Society that accuses the entire mathematics community, with the thinnest of specific evidence, of systemic racial discrimination. A major justification put forward for such a grave accusation is the lack of sufficient representation of Black mathematicians in the professio

the report, while raising awareness of several ugly facts from the long-ago past, makes little effort to address the real reasons for this, mainly the catastrophic failure of the K-12 mathematical educational system.

The National Science Foundation, a federal institution meant to support fundamental research, is now diverting some of its limited funding to various DEI initiatives of questionable benefit.

Meanwhile, other countries, especially China, are doing precisely the opposite, following the model of our past dedication to objective measures of excellence. How long before we will see a reverse exodus, away from the United States?

The present crisis can still be reversed by focusing on a few concrete actions:

Improve schools in urban areas and inner-city neighborhoods by following the most promising education programs. These programs demonstrate that inner-city children benefit if they are challenged by high standards and a nurturing environment.

Follow the lead of other highly successful rigorous programs such as BASIS schools and Math for America, which focus on rigorous STEM curricula, combined with 21st-century teaching methods, and recruit talented teachers to help them build on their STEM knowledge and teaching methods.

Increase, rather than eliminate, tailored instruction, both for accelerated and remedial math courses.

Reject the soft bigotry of low expectations, that Black children cannot do well in competitive mathematics programs and need dumbed-down ethnocentric versions of mathematics.

Uphold the objective selection process based on merit at all levels of education and research.

I learned something more useful: the existence of non-fiction literature that can teach you anything.

something felt odd. Whenever a conversation revolved around a serious non-fiction book I read, such as ‘Sapiens’ or ‘Thinking Fast and Slow,’ I could never remember much. Turns out, I hadn’t absorbed as much information as I’d believed. Since I couldn’t remember much, I felt as though reading wasn’t an investment in knowledge but mere entertainment.

When I opened up about my struggles, many others confessed they also can’t remember most of what they read, as if forgetting is a character flaw. But it isn’t.

It’s the way we work with books that’s flawed.

there’s a better way to read. Most people rely on techniques like highlighting, rereading, or, worst of all, completely passive reading, which are highly ineffective.

Since I started applying evidence-based learning strategies to reading non-fiction books, many things have changed. I can explain complex ideas during dinner conversations. I can recall interesting concepts and link them in my writing or podcasts. As a result, people come to me for all kinds of advice.

What’s the Architecture of Human Learning and Memory?

Human brains don’t work like recording devices. We don’t absorb information and knowledge by reading sentences.

we store new information in terms of its meaning to our existing memory

we give new information meaning by actively participating in the learning process — we interpret, connect, interrelate, or elaborate

To remember new information, we not only need to know it but also to know how it relates to what we already know.

Learning is dependent on memory processes because previously-stored knowledge functions as a framework in which newly learned information can be linked.”

Human memory works in three stages: acquisition, retention, and retrieval. In the acquisition phase, we link new information to existing knowledge; in the retention phase, we store it, and in the retrieval phase, we get information out of our memory.

Retrieval, the third stage, is cue dependent. This means the more mental links you’re generating during stage one, the acquisition phase, the easier you can access and use your knowledge.

we need to understand that the three phases interrelate

creating durable and flexible access to to-be-learned information is partly a matter of achieving a meaningful encoding of that information and partly a matter of exercising the retrieval process.”

Next, we’ll look at the learning strategies that work best for our brains (elaboration, retrieval, spaced repetition, interleaving, self-testing) and see how we can apply those insights to reading non-fiction books.

The strategies that follow are rooted in research from professors of Psychological & Brain Science around Henry Roediger and Mark McDaniel. Both scientists spent ten years bridging the gap between cognitive psychology and education fields. Harvard University Press published their findings in the book ‘Make It Stick.

#1 Elaboration

“Elaboration is the process of giving new material meaning by expressing it in your own words and connecting it with what you already know.”

Why elaboration works: Elaborative rehearsal encodes information into your long-term memory more effectively. The more details and the stronger you connect new knowledge to what you already know, the better because you’ll be generating more cues. And the more cues they have, the easier you can retrieve your knowledge.

How I apply elaboration: Whenever I read an interesting section, I pause and ask myself about the real-life connection and potential application. The process is invisible, and my inner monologues sound like: “This idea reminds me of…, This insight conflicts with…, I don’t really understand how…, ” etc.

For example, when I learned about A/B testing in ‘The Lean Startup,’ I thought about applying this method to my startup. I added a note on the site stating we should try it in user testing next Wednesday. Thereby the book had an immediate application benefit to my life, and I will always remember how the methodology works.

How you can apply elaboration: Elaborate while you read by asking yourself meta-learning questions like “How does this relate to my life? In which situation will I make use of this knowledge? How does it relate to other insights I have on the topic?”

While pausing and asking yourself these questions, you’re generating important memory cues. If you take some notes, don’t transcribe the author’s words but try to summarize, synthesize, and analyze.

#2 Retrieval

With retrieval, you try to recall something you’ve learned in the past from your memory. While retrieval practice can take many forms — take a test, write an essay, do a multiple-choice test, practice with flashcards

the authors of ‘Make It Stick’ state: “While any kind of retrieval practice generally benefits learning, the implication seems to be that where more cognitive effort is required for retrieval, greater retention results.”

Whatever you settle for, be careful not to copy/paste the words from the author. If you don’t do the brain work yourself, you’ll skip the learning benefits of retrieval

Retrieval strengthens your memory and interrupts forgetting and, as other researchers replicate, as a learning event, the act of retrieving information is considerably more potent than is an additional study opportunity, particularly in terms of facilitating long-term recall.

How I apply retrieval: I retrieve a book’s content from my memory by writing a book summary for every book I want to remember. I ask myself questions like: “How would you summarize the book in three sentences? Which concepts do you want to keep in mind or apply? How does the book relate to what you already know?”

I then publish my summaries on Goodreads or write an article about my favorite insights

How you can apply retrieval: You can come up with your own questions or use mine. If you don’t want to publish your summaries in public, you can write a summary into your journal, start a book club, create a private blog, or initiate a WhatsApp group for sharing book summaries.

a few days after we learn something, forgetting sets in

#3 Spaced Repetition

With spaced repetition, you repeat the same piece of information across increasing intervals.

The harder it feels to recall the information, the stronger the learning effect. “Spaced practice, which allows some forgetting to occur between sessions, strengthens both the learning and the cues and routes for fast retrieval,”

Why it works: It might sound counterintuitive, but forgetting is essential for learning. Spacing out practice might feel less productive than rereading a text because you’ll realize what you forgot. Your brain has to work harder to retrieve your knowledge, which is a good indicator of effective learning.

How I apply spaced repetition: After some weeks, I revisit a book and look at the summary questions (see #2). I try to come up with my answer before I look up my actual summary. I can often only remember a fraction of what I wrote and have to look at the rest.

“Knowledge trapped in books neatly stacked is meaningless and powerless until applied for the betterment of life.”

How you can apply spaced repetition: You can revisit your book summary medium of choice and test yourself on what you remember. What were your action points from the book? Have you applied them? If not, what hindered you?

By testing yourself in varying intervals on your book summaries, you’ll strengthen both learning and cues for fast retrieval.

Why interleaving works: Alternate working on different problems feels more difficult as it, again, facilitates forgetting.

How I apply interleaving: I read different books at the same time.

1) Highlight everything you want to remember

#5 Self-Testing

While reading often falsely tricks us into perceived mastery, testing shows us whether we truly mastered the subject at hand. Self-testing helps you identify knowledge gaps and brings weak areas to the light

“It’s better to solve a problem than to memorize a solution.”

Why it works: Self-testing helps you overcome the illusion of knowledge. “One of the best habits a learner can instill in herself is regular self-quizzing to recalibrate her understanding of what she does and does not know.”

How I apply self-testing: I explain the key lessons from non-fiction books I want to remember to others. Thereby, I test whether I really got the concept. Often, I didn’t

instead of feeling frustrated, cognitive science made me realize that identifying knowledge gaps are a desirable and necessary effect for long-term remembering.

How you can apply self-testing: Teaching your lessons learned from a non-fiction book is a great way to test yourself. Before you explain a topic to somebody, you have to combine several mental tasks: filter relevant information, organize this information, and articulate it using your own vocabulary.

Now that I discovered how to use my Kindle as a learning device, I wouldn’t trade it for a paper book anymore. Here are the four steps it takes to enrich your e-reading experience

How you can apply interleaving: Your brain can handle reading different books simultaneously, and it’s effective to do so. You can start a new book before you finish the one you’re reading. Starting again into a topic you partly forgot feels difficult first, but as you know by now, that’s the effect you want to achieve.

it won’t surprise you that researchers proved highlighting to be ineffective. It’s passive and doesn’t create memory cues.

2) Cut down your highlights in your browser

After you finished reading the book, you want to reduce your highlights to the essential part. Visit your Kindle Notes page to find a list of all your highlights. Using your desktop browser is faster and more convenient than editing your highlights on your e-reading device.

Now, browse through your highlights, delete what you no longer need, and add notes to the ones you really like. By adding notes to the highlights, you’ll connect the new information to your existing knowledge

3) Use software to practice spaced repetitionThis part is the main reason for e-books beating printed books. While you can do all of the above with a little extra time on your physical books, there’s no way to systemize your repetition praxis.

Readwise is the best software to combine spaced repetition with your e-books. It’s an online service that connects to your Kindle account and imports all your Kindle highlights. Then, it creates flashcards of your highlights and allows you to export your highlights to your favorite note-taking app.

Common Learning Myths DebunkedWhile reading and studying evidence-based learning techniques I also came across some things I wrongly believed to be true.

#2 Effective learning should feel easyWe think learning works best when it feels productive. That’s why we continue to use ineffective techniques like rereading or highlighting. But learning works best when it feels hard, or as the authors of ‘Make It Stick’ write: “Learning that’s easy is like writing in sand, here today and gone tomorrow.”

In Conclusion

I developed and adjusted these strategies over two years, and they’re still a work in progress.

Try all of them but don’t force yourself through anything that doesn’t feel right for you. I encourage you to do your own research, add further techniques, and skip what doesn’t serve you

“In the case of good books, the point is not to see how many of them you can get through, but rather how many can get through to you.”— Mortimer J. Adler

"United States is no longer trustworthy. It's 50% less less trustworthy than it was when we first started a survey back in 2016," said McPhillips.

The US also came in at number 15 in citizenship, quality of life and best place to visit. It ranked 17th in greenest countries, 18th in most transparent countries, and 26th in best places to travel alone -- another safety issue.

"you ain't black" if you were undecided on who to vote for in 2020.

Biden helped to write the 1994 crime bill, a law that has been widely panned -- particularly by members of the black community -- for its impact on levels of incarceration, among other reverberations.

Biden owes his status as the presumptive presidential nominee almost entirely to black voters -- particularly those in South Carolina. Biden's campaign was faltering badly -- he had finished 4th in Iowa, 5th in New Hampshire and 2nd in Nevada -- prior to the February 29 Palmetto State primary. According to exit polling, black voters made up a majority (56%) of the South Carolina primary electorate and went overwhelmingly (61%) for Biden. His victory in the state propelled him to a series of wins on Super Tuesday -- just three days later -- and, at that point, the nomination was his.

It will be a plus to have an African American woman. It will be a plus to have a Latino. It will be a plus to have a woman."

non-college educated voters who went with Trump, had Clinton been able to drive black turnout to the level it was during Obama's two victories, she almost certainly would have won.

Now, simply putting a person of color on the ticket doesn't mean that you win the votes of black people or ensure they turn out in large numbers. But politics at the presidential level is often about symbolism. And who Biden picks as his vice president will be his best chance to reveal how he views his party, the country and the world -- and what he prioritizes amongst the many, many issues facing the US at the moment.

And lucky for Biden, he has a number of African American women who would make excellent choices

Even before Biden's "you ain't black" gaffe and the uprising following George Floyd's murder, California Sen. Kamala Harris (age 55), who was both the first African American and Indian American elected to the Senate from California, was at the top of my VP rankings. Atlanta Mayor Keisha Lance Bottoms (50 years old) and Florida Rep. Val Demings (63 years old) were in the Top 6. Now? It's hard to see three more people more likely to be the pick. (Stay tuned for my new rankings on Thursday!)

In 2014, the Pew Research Center asked Americans to rank the “greatest dangers in the world.” A plurality put inequality first, ahead of “religious and ethnic hatred,” nuclear weapons, and environmental degradation. And yet people don’t agree about what, exactly, “equality” means.

One side argues that the city should guarantee procedural equality: it should insure that all students and families are equally informed about and encouraged to study for the entrance exam. The other side argues for a more direct, representation-based form of equality: it would jettison the exam, adopting a new admissions system designed to produce student bodies reflective of the city’s demography

In the past year, for example, New York City residents have found themselves in a debate over the city’s élite public high schools

The complexities of egalitarianism are especially frustrating because inequalities are so easy to grasp. C.E.O.s, on average, make almost three hundred times what their employees make; billionaire donors shape our politics; automation favors owners over workers; urban economies grow while rural areas stagnate; the best health care goes to the richest.

It’s not just about money. Tocqueville, writing in 1835, noted that our “ordinary practices of life” were egalitarian, too: we behaved as if there weren’t many differences among us. Today, there are “premiere” lines for popcorn at the movies and five tiers of Uber;

Inequality is everywhere, and unignorable. We’ve diagnosed the disease. Why can’t we agree on a cure?

In a book based on those lectures, “One Another’s Equals: The Basis of Human Equality,” Waldron points out that people are also marked by differences of skill, experience, creativity, and virtue. Given such consequential differences, he asks, in what sense are people “equal”?

According to the Declaration of Independence, it is “self-evident” that all men are created equal. But, from a certain perspective, it’s our inequality that’s self-evident.

More than twenty per cent of Americans, according to a 2015 poll, agree: they believe that the statement “All men are created equal” is false.

In Waldron’s view, though, it’s not a binary choice; it’s possible to see people as equal and unequal simultaneously. A society can sort its members into various categories—lawful and criminal, brilliant and not—while also allowing some principle of basic equality to circumscribe its judgments and, in some contexts, override them

Egalitarians like Dworkin and Waldron call this principle “deep equality.” It’s because of deep equality that even those people who acquire additional, justified worth through their actions—heroes, senators, pop stars—can still be considered fundamentally no better than anyone else.

In the course of his search, he explores centuries of intellectual history. Many thinkers, from Cicero to Locke, have argued that our ability to reason is what makes us equals.

Other thinkers, including Immanuel Kant, have cited our moral sense.

Some philosophers, such as Jeremy Bentham, have suggested that it’s our capacity to suffer that equalizes us

Waldron finds none of these arguments totally persuasive.

In various religious traditions, he observes, equality flows not just from broad assurances that we are all made in God’s image but from some sense that everyone is the protagonist in a saga of error, realization, and redemption: we’re equal because God cares about how things turn out for each of us.

Waldron himself is taken by Hannah Arendt’s related concept of “natality,” the notion that what each of us share is having been born as a “newcomer,” entering into history with “the capacity of beginning something anew, that is, of acting.”

equality may be not a self-evident fact about human beings but a human-made social construction that we must choose to put into practice.

In the end, Waldron concludes that there is no “small polished unitary soul-like substance” that makes us equal; there’s only a patchwork of arguments for our deep equality, collectively compelling but individually limited.

Equality is a composite idea—a nexus of complementary and competing intuitions.

The blurry nature of equality makes it hard to solve egalitarian dilemmas from first principles. In each situation, we must feel our way forward, reconciling our conflicting intuitions about what “equal” means.

The communities that have the easiest time doing that tend to have some clearly defined, shared purpose. Sprinters competing in a hundred-metre dash have varied endowments and train in different conditions; from a certain perspective, those differences make every race unfair.

By embracing an agreed-upon theory of equality before the race, the sprinters can find collective meaning in the ranked inequalities that emerge when it ends

Perhaps because necessity is so demanding, our egalitarian commitments tend to rest on a different principle: luck.

“Some people are blessed with good luck, some are cursed with bad luck, and it is the responsibility of society—all of us regarded collectively—to alter the distribution of goods and evils that arises from the jumble of lotteries that constitutes human life as we know it.” Anderson, in an influential coinage, calls this outlook “luck egalitarianism.”

This sort of artisanal egalitarianism is comparatively easy to arrange. Mass-producing it is what’s hard. A whole society can’t get together in a room to hash things out. Instead, consensus must coalesce slowly around broad egalitarian principles.

No principle is perfect; each contains hidden dangers that emerge with time. Many people, in contemplating the division of goods, invoke the principle of necessity: the idea that our first priority should be the equal fulfillment of fundamental needs. The hidden danger here becomes apparent once we go past a certain point of subsistence.

a core problem that bedevils egalitarianism—what philosophers call “the problem of expensive tastes.”

The problem—what feels like a necessity to one person seems like a luxury to another—is familiar to anyone who’s argued with a foodie spouse or roommate about the grocery bil

The problem is so insistent that a whole body of political philosophy—“prioritarianism”—is devoted to the challenge of sorting people with needs from people with wants

the line shifts as the years pass. Medical procedures that seem optional today become necessities tomorrow; educational attainments that were once unusual, such as college degrees, become increasingly indispensable with time

Some thinkers try to tame the problem of expensive tastes by asking what a “normal” or “typical” person might find necessary. But it’s easy to define “typical” too narrowly, letting unfair assumptions influence our judgment

an odd feature of our social contract: if you’re fired from your job, unemployment benefits help keep you afloat, while if you stop working to have a child you must deal with the loss of income yourself. This contradiction, she writes, reveals an assumption that “the desire to procreate is just another expensive taste”; it reflects, she argues, the sexist presumption that “atomistic egoism and self-sufficiency” are the human norm. The word “necessity” suggests the idea of a bare minimum. In fact, it sets a high bar. Clearing it may require rethinking how society functions.

The result has been a hidden revolution in how information is produced, circulated and acted upon

The world’s liberal democracies now confront a tragedy of the “un-commons.” Information spaces that people assume to be public are strictly ruled by private commercial interests for maximum profit.

The internet as a self-regulating market has been revealed as a failed experiment. Surveillance capitalism leaves a trail of social wreckage in its wake: the wholesale destruction of privacy, the intensification of social inequality, the poisoning of social discourse with defactualized information, the demolition of social norms and the weakening of democratic institutions.

These social harms are not random. They are tightly coupled effects of evolving economic operations. Each harm paves the way for the next and is dependent on what went before.

There is no way to escape the machine systems that surveil u

All roads to economic and social participation now lead through surveillance capitalism’s profit-maximizing institutional terrain, a condition that has intensified during nearly two years of global plague.

Will Facebook’s digital violence finally trigger our commitment to take back the “un-commons”?

Will we confront the fundamental but long ignored questions of an information civilization: How should we organize and govern the information and communication spaces of the digital century in ways that sustain and advance democratic values and principles?

Mark Zuckerberg’s start-up did not invent surveillance capitalism. Google did that. In 2000, when only 25 percent of the world’s information was stored digitally, Google was a tiny start-up with a great search product but little revenue.

By 2001, in the teeth of the dot-com bust, Google’s leaders found their breakthrough in a series of inventions that would transform advertising. Their team learned how to combine massive data flows of personal information with advanced computational analyses to predict where an ad should be placed for maximum “click through.”

Google’s scientists learned how to extract predictive metadata from this “data exhaust” and use it to analyze likely patterns of future behavior.

Prediction was the first imperative that determined the second imperative: extraction.

Lucrative predictions required flows of human data at unimaginable scale. Users did not suspect that their data was secretly hunted and captured from every corner of the internet and, later, from apps, smartphones, devices, cameras and sensors

User ignorance was understood as crucial to success. Each new product was a means to more “engagement,” a euphemism used to conceal illicit extraction operations.

When asked “What is Google?” the co-founder Larry Page laid it out in 2001,

“Storage is cheap. Cameras are cheap. People will generate enormous amounts of data,” Mr. Page said. “Everything you’ve ever heard or seen or experienced will become searchable. Your whole life will be searchable.”

Instead of selling search to users, Google survived by turning its search engine into a sophisticated surveillance medium for seizing human data

Company executives worked to keep these economic operations secret, hidden from users, lawmakers, and competitors. Mr. Page opposed anything that might “stir the privacy pot and endanger our ability to gather data,” Mr. Edwards wrote.

As recently as 2017, Eric Schmidt, the executive chairman of Google’s parent company, Alphabet, acknowledged the role of Google’s algorithmic ranking operations in spreading corrupt information. “There is a line that we can’t really get across,” he said. “It is very difficult for us to understand truth.” A company with a mission to organize and make accessible all the world’s information using the most sophisticated machine systems cannot discern corrupt information.

This is the economic context in which disinformation wins

In March 2008, Mr. Zuckerberg hired Google’s head of global online advertising, Sheryl Sandberg, as his second in command. Ms. Sandberg had joined Google in 2001 and was a key player in the surveillance capitalism revolution. She led the build-out of Google’s advertising engine, AdWords, and its AdSense program, which together accounted for most of the company’s $16.6 billion in revenue in 2007.

A Google multimillionaire by the time she met Mr. Zuckerberg, Ms. Sandberg had a canny appreciation of Facebook’s immense opportunities for extraction of rich predictive data. “We have better information than anyone else. We know gender, age, location, and it’s real data as opposed to the stuff other people infer,” Ms. Sandberg explained

The company had “better data” and “real data” because it had a front-row seat to what Mr. Page had called “your whole life.”

Facebook paved the way for surveillance economics with new privacy policies in late 2009. The Electronic Frontier Foundation warned that new “Everyone” settings eliminated options to restrict the visibility of personal data, instead treating it as publicly available information.

Mr. Zuckerberg “just went for it” because there were no laws to stop him from joining Google in the wholesale destruction of privacy. If lawmakers wanted to sanction him as a ruthless profit-maximizer willing to use his social network against society, then 2009 to 2010 would have been a good opportunity.

Facebook was the first follower, but not the last. Google, Facebook, Amazon, Microsoft and Apple are private surveillance empires, each with distinct business models.

In 2021 these five U.S. tech giants represent five of the six largest publicly traded companies by market capitalization in the world.

As we move into the third decade of the 21st century, surveillance capitalism is the dominant economic institution of our time. In the absence of countervailing law, this system successfully mediates nearly every aspect of human engagement with digital information

Today all apps and software, no matter how benign they appear, are designed to maximize data collection.

Historically, great concentrations of corporate power were associated with economic harms. But when human data are the raw material and predictions of human behavior are the product, then the harms are social rather than economic

The difficulty is that these novel harms are typically understood as separate, even unrelated, problems, which makes them impossible to solve. Instead, each new stage of harm creates the conditions for the next stage.

Fifty years ago the conservative economist Milton Friedman exhorted American executives, “There is one and only one social responsibility of business — to use its resources and engage in activities designed to increase its profits so long as it stays within the rules of the game.” Even this radical doctrine did not reckon with the possibility of no rules.

With privacy out of the way, ill-gotten human data are concentrated within private corporations, where they are claimed as corporate assets to be deployed at will.

The sheer size of this knowledge gap is conveyed in a leaked 2018 Facebook document, which described its artificial intelligence hub, ingesting trillions of behavioral data points every day and producing six million behavioral predictions each second.

Next, these human data are weaponized as targeting algorithms, engineered to maximize extraction and aimed back at their unsuspecting human sources to increase engagement

Targeting mechanisms change real life, sometimes with grave consequences. For example, the Facebook Files depict Mr. Zuckerberg using his algorithms to reinforce or disrupt the behavior of billions of people. Anger is rewarded or ignored. News stories become more trustworthy or unhinged. Publishers prosper or wither. Political discourse turns uglier or more moderate. People live or die.

Occasionally the fog clears to reveal the ultimate harm: the growing power of tech giants willing to use their control over critical information infrastructure to compete with democratically elected lawmakers for societal dominance.

when it comes to the triumph of surveillance capitalism’s revolution, it is the lawmakers of every liberal democracy, especially in the United States, who bear the greatest burden of responsibility. They allowed private capital to rule our information spaces during two decades of spectacular growth, with no laws to stop it.

All of it begins with extraction. An economic order founded on the secret massive-scale extraction of human data assumes the destruction of privacy as a nonnegotiable condition of its business operations.

We can’t fix all our problems at once, but we won’t fix any of them, ever, unless we reclaim the sanctity of information integrity and trustworthy communications

The abdication of our information and communication spaces to surveillance capitalism has become the meta-crisis of every republic, because it obstructs solutions to all other crises.

Neither Google, nor Facebook, nor any other corporate actor in this new economic order set out to destroy society, any more than the fossil fuel industry set out to destroy the earth.

like global warming, the tech giants and their fellow travelers have been willing to treat their destructive effects on people and society as collateral damage — the unfortunate but unavoidable byproduct of perfectly legal economic operations that have produced some of the wealthiest and most powerful corporations in the history of capitalism.

Where does that leave us?

Democracy is the only countervailing institutional order with the legitimate authority and power to change our course. If the ideal of human self-governance is to survive the digital century, then all solutions point to one solution: a democratic counterrevolution.

instead of the usual laundry lists of remedies, lawmakers need to proceed with a clear grasp of the adversary: a single hierarchy of economic causes and their social harms.

We can’t rid ourselves of later-stage social harms unless we outlaw their foundational economic causes

This means we move beyond the current focus on downstream issues such as content moderation and policing illegal content. Such “remedies” only treat the symptoms without challenging the illegitimacy of the human data extraction that funds private control over society’s information spaces

Similarly, structural solutions like “breaking up” the tech giants may be valuable in some cases, but they will not affect the underlying economic operations of surveillance capitalism.

Instead, discussions about regulating big tech should focus on the bedrock of surveillance economics: the secret extraction of human data from realms of life once called “private.

No secret extraction means no illegitimate concentrations of knowledge about people. No concentrations of knowledge means no targeting algorithms. No targeting means that corporations can no longer control and curate information flows and social speech or shape human behavior to favor their interests

the sober truth is that we need lawmakers ready to engage in a once-a-century exploration of far more basic questions:

How should we structure and govern information, connection and communication in a democratic digital century?

What new charters of rights, legislative frameworks and institutions are required to ensure that data collection and use serve the genuine needs of individuals and society?

What measures will protect citizens from unaccountable power over information, whether it is wielded by private companies or governments?

The corporation that is Facebook may change its name or its leaders, but it will not voluntarily change its economics.

Dunning-Kruger “offers an explanation for a kind of hubris,” said Steven Sloman, a cognitive psychologist at Brown University. “The fact is, that’s Trump in a nutshell. He’s a man with zero political skill who has no idea he has zero political skill. And it’s given him extreme confidence.”

What happens when the incompetent are unwilling to admit they have shortcomings? Are they so confident in their own perceived knowledge that they will reject the very idea of improvement? Not surprisingly (though no less concerning), Dunning’s follow-up research shows the poorest performers are also the least likely to accept criticism or show interest in self improvement.

Someone who has very little knowledge in a subject claims to know a lot.

the Dunning-Kruger effect. It’s not a disease, syndrome or mental illness; it is present in everybody to some extent, and it’s been around as long as human cognition, though only recently has it been studied and documented in social psychology

“Obviously it has to do with Trump and the various treatments that people have given him,” Dunning said, “So yeah, a lot of it is political. People trying to understand the other side. We have a massive rise in partisanship and it’s become more vicious and extreme, so people are reaching for explanations."

Even though President Trump’s statements are rife with errors, falsehoods or inaccuracies, he expresses great confidence in his aptitude. He says he does not read extensively because he solves problems “with very little knowledge other than the knowledge I [already] had.” He has said in interviews he doesn’t read lengthy reports because “I already know exactly what it is.”

the Dunning-Kruger effect has become popular outside of the research world because it is a simple phenomenon that could apply to all of us

The ramifications of the Dunning-Kruger effect are usually harmless. If you’ve ever felt confident answering questions on an exam, only to have the teacher mark them incorrect, you have firsthand experience with Dunning-Kruger.

The researchers used facial-recognition software to map out specific points on a person’s face, then applied machine-learning algorithms to the facial points to obtain empirical measures for three key face impressions—trustworthiness, dominance and attractiveness.  

They examined the association of these impressions with the accuracy of analysts’ quarterly forecasts, drawn from the Institutional Brokers Estimate System

Analyst accuracy was determined by comparing each analyst’s prediction error—the difference between their prediction and the actual earnings—with that of all analysts for that same company and quarter.

For an average stock valued at $100, Prof. Teoh says, analysts ranked as looking most trustworthy were 25 cents more accurate in earnings-per-share forecasts than the analysts who were ranked as looking least trustworthy

Similarly, most-dominant-looking analysts were 52 cents more accurate in their EPS forecast than least-dominant-looking analysts.

The relation between a dominant face and accuracy, meanwhile, was significant before and after the regulation was enacted, the analysts say. This suggests that dominant-looking male analysts are always able to obtain information,

While forecasts of female analysts regardless of facial characteristics were on average more accurate than those of their male counterparts, the forecasts of women who were seen as more-dominant-looking were significantly less accurate than their male counterparts.  

Says Prof. Todorov: “Women who look dominant are more likely to be viewed negatively because it goes against the cultural stereotype.

Content moderation comes to every content platform eventually, and platforms are starting to realize this faster than ever.

Nothing symbolizes this shift as neatly as Facebook’s decision in October (and Twitter’s shortly after) to start banning Holocaust denial. Almost exactly a year earlier, Zuckerberg had proudly tied himself to the First Amendment in a widely publicized “stand for free expression” at Georgetown University.

The evolution continues. Facebook announced earlier this month that it will join platforms such as YouTube and TikTok in removing, not merely labeling or down-ranking, false claims about COVID-19 vaccines.

the pandemic also showed that complete neutrality is impossible. Even though it’s not clear that removing content outright is the best way to correct misperceptions, Facebook and other platforms plainly want to signal that, at least in the current crisis, they don’t want to be seen as feeding people information that might kill them.

When internet platforms announce new policies, assessing whether they can and will enforce them consistently has always been difficult. In essence, the companies are grading their own work. But too often what can be gleaned from the outside suggests that they’re failing.

It tweaked its algorithm to boost authoritative sources in the news feed and turned off recommendations to join groups based around political or social issues. Facebook is reversing some of these steps now, but it cannot make people forget this toolbox exists in the future

As platforms grow more comfortable with their power, they are recognizing that they have options beyond taking posts down or leaving them up. In addition to warning labels, Facebook implemented other “break glass” measures to stem misinformation as the election approached.

Platforms don’t deserve praise for belatedly noticing dumpster fires that they helped create and affixing unobtrusive labels to them

Warning labels for misinformation might make some commentators feel a little better, but whether labels actually do much to contain the spread of false information is still unknown.

News reporting suggests that insiders at Facebook knew they could and should do more about misinformation, but higher-ups vetoed their ideas. YouTube barely acted to stem the flood of misinformation about election results on its platform.

Even before the pandemic, YouTube had begun adjusting its recommendation algorithm to reduce the spread of borderline and harmful content, and is introducing pop-up nudges to encourage user

And if 2020 finally made clear to platforms the need for greater content moderation, it also exposed the inevitable limits of content moderation.

Down-ranking, labeling, or deleting content on an internet platform does not address the social or political circumstances that caused it to be posted in the first place

even the most powerful platform will never be able to fully compensate for the failures of other governing institutions or be able to stop the leader of the free world from constructing an alternative reality when a whole media ecosystem is ready and willing to enable him. As Renée DiResta wrote in The Atlantic last month, “reducing the supply of misinformation doesn’t eliminate the demand.”

Even so, this year’s events showed that nothing is innate, inevitable, or immutable about platforms as they currently exist. The possibilities for what they might become—and what role they will play in society—are limited more by imagination than any fixed technological constraint, and the companies appear more willing to experiment than ever.

While people praised Finland’s strong social safety net and spoke glowingly of the psychological benefits of nature and the personal joys of sports or music, they also talked about guilt, anxiety and loneliness. Rather than “happy,” they were more likely to characterize Finns as “quite gloomy,” “a little moody” or not given to unnecessary smiling

Many also shared concerns about threats to their way of life, including possible gains by a far-right party in the country’s elections in April, the war in Ukraine and a tense relationship with Russia, which could worsen now that Finland is set to join NATO.

It turns out even the happiest people in the world aren’t that happy. But they are something more like content.

Finns derive satisfaction from leading sustainable lives and perceive financial success as being able to identify and meet basic need

“In other words,” he wrote in an email, “when you know what is enough, you are happy.”

“‘Happiness,’ sometimes it’s a light word and used like it’s only a smile on a face,” Teemu Kiiski, the chief executive of Finnish Design Shop, said. “But I think that this Nordic happiness is something more foundational.”

e conventional wisdom is that it’s easier to be happy in a country like Finland where the government ensures a secure foundation on which to build a fulfilling life and a promising future. But that expectation can also create pressure to live up to the national reputation.

“We are very privileged and we know our privilege,” said Clara Paasimaki, 19, one of Ms. Hansen’s students in Kokkola, “so we are also scared to say that we are discontent with anything, because we know that we have it so much better than other people,” especially in non-Nordic countries.

“The fact that Finland has been ‘the happiest country on earth’ for six years in a row could start building pressure on people,” he wrote in an email. “If we Finns are all so happy, why am I not happy?

The Finnish way of life is summed up in “sisu,” a trait said to be part of the national character. The word roughly translates to “grim determination in the face of hardships,” such as the country’s long winters: Even in adversity, a Finn is expected to persevere, without complaining.

“Back in the day when it wasn’t that easy to survive the winter, people had to struggle, and then it’s kind of been passed along the generations,” said Ms. Paasimaki’s classmate Matias From, 18. “Our parents were this way. Our grandparents were this way. Tough and not worrying about everything. Just living life.”

Since immigrating from Zimbabwe in 1992, Julia Wilson-Hangasmaa, 59, has come to appreciate the freedom Finland affords people to pursue their dreams without worrying about meeting basic needs

When she returns to her home country, she is struck by the “good energy” that comes not from the satisfaction of sisu but from exuberant joy.

“What I miss the most, I realize when I enter Zimbabwe, are the smiles,” she said, among “those people who don’t have much, compared to Western standards, but who are rich in spirit.”

Many of our subjects cited the abundance of nature as crucial to Finnish happiness: Nearly 75 percent of Finland is covered by forest, and all of it is open to everyone thanks to a law known as “jokamiehen oikeudet,” or “everyman’s right,” that entitles people to roam freely throughout any natural areas, on public or privately owned land.

“I enjoy the peace and movement in nature,” said Helina Marjamaa, 66, a former track athlete who represented the country at the 1980 and 1984 Olympic Games. “That’s where I get strength. Birds are singing, snow is melting, and nature is coming to life. It’s just incredibly beautiful.”

“I am worried with this level of ignorance we have toward our own environment,” he said, citing endangered species and climate change. The threat, he said, “still doesn’t seem to shift the political thinking.”

Born 17 years after Finland won independence from Russia, Eeva Valtonen has watched her homeland transform: from the devastation of World War II through years of rebuilding to a nation held up as an exemplar to the world.

“My mother used to say, ‘Remember, the blessing in life is in work, and every work you do, do it well,’” Ms. Valtonen, 88, said. “I think Finnish people have been very much the same way. Everybody did everything together and helped each other.”

Maybe it isn’t that Finns are so much happier than everyone else. Maybe it’s that their expectations for contentment are more reasonable, and if they aren’t met, in the spirit of sisu, they persevere.

“We don’t whine,” Ms. Eerikainen said. “We just do.”

Of the 3,060 students who applied to his school this year, 44 percent were boys. To help rank the candidates, he said, he simply adjusted the focus of student interviews to more effectively draw boys out in describing their own strengths. This year he offered seats to 136 boys and 134 girls. “Are we worried about getting unqualified boys?” asked Dr. Lerner. “No not at all.”

In this past weekend’s Styles section of the New York Times, Siegel uses “The Lonely Crowd” to analyze the putative “Yelpification” of contemporary life: according to Siegel, Riesman’s view was that “people went from being ‘inner-directed’ to ‘outer-directed,’ from heeding their own instincts and judgment to depending on the judgments and opinions of tastemakers and trendsetters.” The “conformist power of the crowd” and its delighted ability to write online reviews led Siegel down a sad path to a lackluster expensive dinner.

What Riesman actually suggested was that we think of social organization in terms of a series of “ideal types” along a spectrum of increasingly loose authority

On one end of the spectrum is a “tradition-directed” community, where we all understand that what we’re supposed to do is what we’re supposed to do because it’s just the thing that one does; authority is unequivocal, and there’s neither the room nor the desire for autonomous action

In the middle of the spectrum, as one moves toward a freer distribution of, and response to, authority, is “inner-direction.” The inner-directed character is concerned not with “what one does” but with “what people like us do.” Which is to say that she looks to her own internalizations of past authorities to get a sense for how to conduct her affairs.

Contemporary society, Riesman thought, was best understood as chiefly “other-directed,” where the inculcated authority of the vertical (one’s lineage) gives way to the muddled authority of the horizontal (one’s peers).

The inner-directed person orients herself by an internal “gyroscope,” while the other-directed person orients herself by “radar.”