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Confirmation bias shows up most blatantly in our current political divide, where each side seems unable to allow that the other side is right about anything.

The whole idea of cognitive biases and faulty heuristics—the shortcuts and rules of thumb by which we make judgments and predictions—was more or less invented in the 1970s by Amos Tversky and Daniel Kahneman

versky died in 1996. Kahneman won the 2002 Nobel Prize in Economics for the work the two men did together, which he summarized in his 2011 best seller, Thinking, Fast and Slow. Another best seller, last year’s The Undoing Project, by Michael Lewis, tells the story of the sometimes contentious collaboration between Tversky and Kahneman

Another key figure in the field is the University of Chicago economist Richard Thaler. One of the biases he’s most linked with is the endowment effect, which leads us to place an irrationally high value on our possessions.

In an experiment conducted by Thaler, Kahneman, and Jack L. Knetsch, half the participants were given a mug and then asked how much they would sell it for. The average answer was $5.78. The rest of the group said they would spend, on average, $2.21 for the same mug. This flew in the face of classic economic theory, which says that at a given time and among a certain population, an item has a market value that does not depend on whether one owns it or not. Thaler won the 2017 Nobel Prize in Economics.

“The question that is most often asked about cognitive illusions is whether they can be overcome. The message … is not encouraging.”

that’s not so easy in the real world, when we’re dealing with people and situations rather than lines. “Unfortunately, this sensible procedure is least likely to be applied when it is needed most,” Kahneman writes. “We would all like to have a warning bell that rings loudly whenever we are about to make a serious error, but no such bell is available.”

At least with the optical illusion, our slow-thinking, analytic mind—what Kahneman calls System 2—will recognize a Müller-Lyer situation and convince itself not to trust the fast-twitch System 1’s perception

Kahneman and others draw an analogy based on an understanding of the Müller-Lyer illusion, two parallel lines with arrows at each end. One line’s arrows point in; the other line’s arrows point out. Because of the direction of the arrows, the latter line appears shorter than the former, but in fact the two lines are the same length.

Because biases appear to be so hardwired and inalterable, most of the attention paid to countering them hasn’t dealt with the problematic thoughts, judgments, or predictions themselves

Is it really impossible, however, to shed or significantly mitigate one’s biases? Some studies have tentatively answered that question in the affirmative.

what if the person undergoing the de-biasing strategies was highly motivated and self-selected? In other words, what if it was me?

Over an apple pastry and tea with milk, he told me, “Temperament has a lot to do with my position. You won’t find anyone more pessimistic than I am.”

I met with Kahneman

“I see the picture as unequal lines,” he said. “The goal is not to trust what I think I see. To understand that I shouldn’t believe my lying eyes.” That’s doable with the optical illusion, he said, but extremely difficult with real-world cognitive biases.

In this context, his pessimism relates, first, to the impossibility of effecting any changes to System 1—the quick-thinking part of our brain and the one that makes mistaken judgments tantamount to the Müller-Lyer line illusion

he most effective check against them, as Kahneman says, is from the outside: Others can perceive our errors more readily than we can.

“slow-thinking organizations,” as he puts it, can institute policies that include the monitoring of individual decisions and predictions. They can also require procedures such as checklists and “premortems,”

A premortem attempts to counter optimism bias by requiring team members to imagine that a project has gone very, very badly and write a sentence or two describing how that happened. Conducting this exercise, it turns out, helps people think ahead.

“My position is that none of these things have any effect on System 1,” Kahneman said. “You can’t improve intuition.

Perhaps, with very long-term training, lots of talk, and exposure to behavioral economics, what you can do is cue reasoning, so you can engage System 2 to follow rules. Unfortunately, the world doesn’t provide cues. And for most people, in the heat of argument the rules go out the window.

Kahneman describes an even earlier Nisbett article that showed subjects’ disinclination to believe statistical and other general evidence, basing their judgments instead on individual examples and vivid anecdotes. (This bias is known as base-rate neglect.)

over the years, Nisbett had come to emphasize in his research and thinking the possibility of training people to overcome or avoid a number of pitfalls, including base-rate neglect, fundamental attribution error, and the sunk-cost fallacy.

Nisbett’s second-favorite example is that economists, who have absorbed the lessons of the sunk-cost fallacy, routinely walk out of bad movies and leave bad restaurant meals uneaten.

When Nisbett asks the same question of students who have completed the statistics course, about 70 percent give the right answer. He believes this result shows, pace Kahneman, that the law of large numbers can be absorbed into System 2—and maybe into System 1 as well, even when there are minimal cues.

about half give the right answer: the law of large numbers, which holds that outlier results are much more frequent when the sample size (at bats, in this case) is small. Over the course of the season, as the number of at bats increases, regression to the mean is inevitabl

When Nisbett has to give an example of his approach, he usually brings up the baseball-phenom survey. This involved telephoning University of Michigan students on the pretense of conducting a poll about sports, and asking them why there are always several Major League batters with .450 batting averages early in a season, yet no player has ever finished a season with an average that high.

we’ve tested Michigan students over four years, and they show a huge increase in ability to solve problems. Graduate students in psychology also show a huge gain.”

, “I know from my own research on teaching people how to reason statistically that just a few examples in two or three domains are sufficient to improve people’s reasoning for an indefinitely large number of events.”

isbett suggested another factor: “You and Amos specialized in hard problems for which you were drawn to the wrong answer. I began to study easy problems, which you guys would never get wrong but untutored people routinely do … Then you can look at the effects of instruction on such easy problems, which turn out to be huge.”

Nisbett suggested that I take “Mindware: Critical Thinking for the Information Age,” an online Coursera course in which he goes over what he considers the most effective de-biasing skills and concepts. Then, to see how much I had learned, I would take a survey he gives to Michigan undergraduates. So I did.

he course consists of eight lessons by Nisbett—who comes across on-screen as the authoritative but approachable psych professor we all would like to have had—interspersed with some graphics and quizzes. I recommend it. He explains the availability heuristic this way: “People are surprised that suicides outnumber homicides, and drownings outnumber deaths by fire. People always think crime is increasing” even if it’s not.

When I finished the course, Nisbett sent me the survey he and colleagues administer to Michigan undergrads

It contains a few dozen problems meant to measure the subjects’ resistance to cognitive biases

I got it right. Indeed, when I emailed my completed test, Nisbett replied, “My guess is that very few if any UM seniors did as well as you. I’m sure at least some psych students, at least after 2 years in school, did as well. But note that you came fairly close to a perfect score.”

Nevertheless, I did not feel that reading Mindware and taking the Coursera course had necessarily rid me of my biases

For his part, Nisbett insisted that the results were meaningful. “If you’re doing better in a testing context,” he told me, “you’ll jolly well be doing better in the real world.”

The New York–based NeuroLeadership Institute offers organizations and individuals a variety of training sessions, webinars, and conferences that promise, among other things, to use brain science to teach participants to counter bias. This year’s two-day summit will be held in New York next month; for $2,845, you could learn, for example, “why are our brains so bad at thinking about the future, and how do we do it better?”

Philip E. Tetlock, a professor at the University of Pennsylvania’s Wharton School, and his wife and research partner, Barbara Mellers, have for years been studying what they call “superforecasters”: people who manage to sidestep cognitive biases and predict future events with far more accuracy than the pundits

One of the most important ingredients is what Tetlock calls “the outside view.” The inside view is a product of fundamental attribution error, base-rate neglect, and other biases that are constantly cajoling us into resting our judgments and predictions on good or vivid stories instead of on data and statistics

In 2006, seeking to prevent another mistake of that magnitude, the U.S. government created the Intelligence Advanced Research Projects Activity (iarpa), an agency designed to use cutting-edge research and technology to improve intelligence-gathering and analysis. In 2011, iarpa initiated a program, Sirius, to fund the development of “serious” video games that could combat or mitigate what were deemed to be the six most damaging biases: confirmation bias, fundamental attribution error, the bias blind spot (the feeling that one is less biased than the average person), the anchoring effect, the representativeness heuristic, and projection bias (the assumption that everybody else’s thinking is the same as one’s own).

most promising are a handful of video games. Their genesis was in the Iraq War

Together with collaborators who included staff from Creative Technologies, a company specializing in games and other simulations, and Leidos, a defense, intelligence, and health research company that does a lot of government work, Morewedge devised Missing. Some subjects played the game, which takes about three hours to complete, while others watched a video about cognitive bias. All were tested on bias-mitigation skills before the training, immediately afterward, and then finally after eight to 12 weeks had passed.

“The literature on training suggests books and classes are fine entertainment but largely ineffectual. But the game has very large effects. It surprised everyone.”

he said he saw the results as supporting the research and insights of Richard Nisbett. “Nisbett’s work was largely written off by the field, the assumption being that training can’t reduce bias,

even the positive results reminded me of something Daniel Kahneman had told me. “Pencil-and-paper doesn’t convince me,” he said. “A test can be given even a couple of years later. But the test cues the test-taker. It reminds him what it’s all about.”

Morewedge told me that some tentative real-world scenarios along the lines of Missing have shown “promising results,” but that it’s too soon to talk about them.

In the future, I will monitor my thoughts and reactions as best I can

In it, Baudrillard argues that there are four phases of the image - a faithful depiction of that which really is, an unfaithful depiction of that which really is, a depiction that covers up for the fact that there is nothing which is actually being depicted, and the simulacra, which exists in a human culture of such universal equivalency that no one has the grounding necessary to know what “reality” might even be outside of equivalencies, outside of depiction

at this stage, the litbro really exists precisely within Baudrillard’s concepts - it is a representation of someone else’s representation, a second-hand depiction, an archetype that is now developed fully through reference to itself rather than to some underlying reality. Baudrillard said that the development of simulacra is “a question of substituting the signs of the real for the real.”

Another example you often hear is the 1950s diner, the joint that has the neon signs and the art deco styling and the mini jukeboxes at the tables. This classic bit of Americana is not, in fact, based on what diners were like in the 1950s; it’s someone’s idea of what 1950s diners were like, which then spread mimetically from the actual physical 1950s diners that had been built to films and television, which then acted as “proof” that the imaginary diners were real, creating a social expectation of what a diner looks like that diner owners then felt pressure to fulfill…. Eventually most people came to believe that this is what diners were like in the 1950s. The point, though, is not that this is an act of deception. The point is that the consumerist reality in which these restaurants exists obliterates any belief in a true or false depiction. (No one cares whether the classic 1950s diner actually depicts a historical truth, really

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.

“My first and biggest wish is that we had known early that Covid-19 was airborne,”

, “Once you’ve realized that, it informs an entirely different strategy for protection.” Masking, ventilation and air cleaning become key, as well as avoiding high-risk encounters with strangers, he says.

Instead of washing our produce and wearing hand-sewn cloth masks, we could have made sure to avoid superspreader events and worn more-effective N95 masks or their equivalent. “We could have made more of an effort to develop and distribute N95s to everyone,” says Dr. Volckens. “We could have had an Operation Warp Speed for masks.”

We didn’t realize how important clear, straight talk would be to maintaining public trust. If we had, we could have explained the biological nature of a virus and warned that Covid-19 would change in unpredictable ways.  

We didn’t know how difficult it would be to get the basic data needed to make good public-health and medical decisions. If we’d had the data, we could have more effectively allocated scarce resources

In the face of a pandemic, he says, the public needs an early basic and blunt lesson in virology

and mutates, and since we’ve never seen this particular virus before, we will need to take unprecedented actions and we will make mistakes, he says.

Since the public wasn’t prepared, “people weren’t able to pivot when the knowledge changed,”

By the time the vaccines became available, public trust had been eroded by myriad contradictory messages—about the usefulness of masks, the ways in which the virus could be spread, and whether the virus would have an end date.

, the absence of a single, trusted source of clear information meant that many people gave up on trying to stay current or dismissed the different points of advice as partisan and untrustworthy.

“The science is really important, but if you don’t get the trust and communication right, it can only take you so far,”

people didn’t know whether it was OK to visit elderly relatives or go to a dinner party.

Doctors didn’t know what medicines worked. Governors and mayors didn’t have the information they needed to know whether to require masks. School officials lacked the information needed to know whether it was safe to open schools.

Had we known that even a mild case of Covid-19 could result in long Covid and other serious chronic health problems, we might have calculated our own personal risk differently and taken more care.

just months before the outbreak of the pandemic, the Council of State and Territorial Epidemiologists released a white paper detailing the urgent need to modernize the nation’s public-health system still reliant on manual data collection methods—paper records, phone calls, spreadsheets and faxes.

While the U.K. and Israel were collecting and disseminating Covid case data promptly, in the U.S. the CDC couldn’t. It didn’t have a centralized health-data collection system like those countries did, but rather relied on voluntary reporting by underfunded state and local public-health systems and hospitals.

doctors and scientists say they had to depend on information from Israel, the U.K. and South Africa to understand the nature of new variants and the effectiveness of treatments and vaccines. They relied heavily on private data collection efforts such as a dashboard at Johns Hopkins University’s Coronavirus Resource Center that tallied cases, deaths and vaccine rates globally.

For much of the pandemic, doctors, epidemiologists, and state and local governments had no way to find out in real time how many people were contracting Covid-19, getting hospitalized and dying

To solve the data problem, Dr. Ranney says, we need to build a public-health system that can collect and disseminate data and acts like an electrical grid. The power company sees a storm coming and lines up repair crews.

If we’d known how damaging lockdowns would be to mental health, physical health and the economy, we could have taken a more strategic approach to closing businesses and keeping people at home.

t many doctors say they were crucial at the start of the pandemic to give doctors and hospitals a chance to figure out how to accommodate and treat the avalanche of very sick patients.

The measures reduced deaths, according to many studies—but at a steep cost.

The lockdowns didn’t have to be so harmful, some scientists say. They could have been more carefully tailored to protect the most vulnerable, such as those in nursing homes and retirement communities, and to minimize widespread disruption.

Lockdowns could, during Covid-19 surges, close places such as bars and restaurants where the virus is most likely to spread, while allowing other businesses to stay open with safety precautions like masking and ventilation in place.  

The key isn’t to have the lockdowns last a long time, but that they are deployed earlier,

If England’s March 23, 2020, lockdown had begun one week earlier, the measure would have nearly halved the estimated 48,600 deaths in the first wave of England’s pandemic

If the lockdown had begun a week later, deaths in the same period would have more than doubled

It is possible to avoid lockdowns altogether. Taiwan, South Korea and Hong Kong—all countries experienced at handling disease outbreaks such as SARS in 2003 and MERS—avoided lockdowns by widespread masking, tracking the spread of the virus through testing and contact tracing and quarantining infected individuals.

With good data, Dr. Ranney says, she could have better managed staffing and taken steps to alleviate the strain on doctors and nurses by arranging child care for them.

Early in the pandemic, public-health officials were clear: The people at increased risk for severe Covid-19 illness were older, immunocompromised, had chronic kidney disease, Type 2 diabetes or serious heart conditions

t had the unfortunate effect of giving a false sense of security to people who weren’t in those high-risk categories. Once case rates dropped, vaccines became available and fear of the virus wore off, many people let their guard down, ditching masks, spending time in crowded indoor places.

it has become clear that even people with mild cases of Covid-19 can develop long-term serious and debilitating diseases. Long Covid, whose symptoms include months of persistent fatigue, shortness of breath, muscle aches and brain fog, hasn’t been the virus’s only nasty surprise

In February 2022, a study found that, for at least a year, people who had Covid-19 had a substantially increased risk of heart disease—even people who were younger and had not been hospitalized

respiratory conditions.

Some scientists now suspect that Covid-19 might be capable of affecting nearly every organ system in the body. It may play a role in the activation of dormant viruses and latent autoimmune conditions people didn’t know they had

 A blood test, he says, would tell people if they are at higher risk of long Covid and whether they should have antivirals on hand to take right away should they contract Covid-19.

If the risks of long Covid had been known, would people have reacted differently, especially given the confusion over masks and lockdowns and variants? Perhaps. At the least, many people might not have assumed they were out of the woods just because they didn’t have any of the risk factors.