What unconscious bias training gets wrong… and how to fix it

Good overview on the latest research and lessons. Main conclusion, no quick fix, has to be part of ongoing training and awareness:

Here’s a fact that cannot be disputed: if your name is James or Emily, you will find it easier to get a job than someone called Tariq or Adeola. Between November 2016 and December 2017, researchers sent out fake CVs and cover letters for 3,200 positions. Despite demonstrating exactly the same qualifications and experience, the “applicants” with common Pakistani or Nigerian names needed to send out 60% more applications to receive the same number of callbacks as applicants with more stereotypically British names.

Some of the people who had unfairly rejected Tariq or Adeola will have been overtly racist, and so deliberately screened people based on their ethnicity. According to a large body of psychological research, however, many will have also reacted with an implicit bias, without even being aware of the assumptions they were making.

Such findings have spawned a plethora of courses offering “unconscious bias and diversity training”, which aim to reduce people’s racist, sexist and homophobic tendencies. If you work for a large organisation, you’ve probably taken one yourself. Last year, Labour leader Keir Starmer volunteered to undergo such training after he appeared to dismiss the importance of the Black Lives Matter movement. “There is always the risk of unconscious bias, and just saying: ‘Oh well, it probably applies to other people, not me,’ is not the right thing to do,” he said. Even Prince Harry has been educating himself about his potential for implicit bias – and advising others to do the same.

Sounds sensible, doesn’t it? You remind people of their potential for prejudice so they can change their thinking and behaviour. Yet there is now a severe backlash against the very idea of unconscious bias and diversity training, with an increasing number of media articles lamenting these “woke courses” as a “useless” waste of money. The sceptics argue that there is little evidence that unconscious bias training works, leading some organisations – including the UK’s civil service – to cancel their schemes.

So what’s the truth? Is it ever possible to correct our biases? And if so, why have so many schemes failed to make a difference?

While the contents of unconscious bias and diversity training courses vary widely, most share a few core components. Participants will often be asked to complete the implicit association test (IAT), for example. By measuring people’s reaction times during a word categorisation task, an algorithm can calculate whether people have more positive or negative associations with a certain group – such as people of a different ethnicity, sexual orientation or gender. (You can try it for yourself on the Harvard website.)

After taking the IAT, participants will be debriefed about their results. They may then learn about the nature of unconscious bias and stereotypes more generally, and the consequences within the workplace, along with some suggestions to reduce the impact.

All of which sounds useful in theory. To confirm the benefits, however, you need to compare the attitudes and behaviours of employees who have taken unconscious bias and diversity training with those who have not – in much the same way that drugs are tested against a placebo.

Prof Edward Chang at Harvard Business School has led one of the most rigorous trials, delivering an hour-long online diversity course to thousands of employees at an international professional services company. Using tools like the IAT, the training was meant to educate people about sexist stereotypes and their consequences – and surveys suggest that it did change some attitudes. The participants reported greater acknowledgment of their own bias after the course, and greater support of women in the workplace, than people who had taken a more general course on “psychological safety” and “active listening”.

Unfortunately, this didn’t translate to the profound behavioural change you might expect. Three weeks after taking the course, the employees were given the chance of taking part in an informal mentoring scheme. Overall, the people who had taken the diversity course were no more likely to take on a female mentee. Six weeks after taking the course, the participants were also given the opportunity to nominate colleagues for recognition of their “excellence”. It could have been the perfect opportunity to offer some encouragement to overlooked women in the workplace. Once again, however, the people who had taken the diversity training were no more likely to nominate a female colleague than the control group.

“We did our best to design a training that would be effective,” Chang tells me. “But our results suggest that the sorts of one-off trainings that are commonplace in organisations are not particularly effective at leading to long-lasting behaviour change.”

Chang’s results chime with the broader conclusions of a recent report by Britain’s Equality and Human Rights Commission (EHRC), which examined 18 papers on unconscious bias training programmes. Overall, the authors concluded that the courses are effective at raising awareness of bias, but the evidence of long-lasting behavioural change is “limited”.

Even the value of the IAT – which is central to so many of these courses – has been subject to scrutiny. The courses tend to use shortened versions of the test, and the same person’s results can vary from week to week. So while it might be a useful educational aid to explain the concept of unconscious bias, it is wrong to present the IAT as a reliable diagnosis of underlying prejudice.

It certainly sounds damning; little wonder certain quarters of the press have been so willing to declare these courses a waste of time and money. Yet the psychologists researching their value take a more nuanced view, and fear their conclusions have been exaggerated. While it is true that many schemes have ended in disappointment, some have been more effective, and researchers believe we should learn from these successes and failures to design better interventions in the future – rather than simply dismissing them altogether.

For one thing, many of the current training schemes are simply too brief to have the desired effect. “It’s usually part of the employee induction and lasts about 30 minutes to an hour,” says Dr Doyin Atewologun, a co-author of the EHRC’s report and founding member of British Psychological Society’s diversity and inclusion at work group. “It’s just tucked away into one of the standard training materials.” We should not be surprised the lessons are soon forgotten. In general, studies have shown that diversity training can have more pronounced effects if it takes place over a longer period of time. A cynic might suspect that these short programmes are simple box-ticking exercises, but Atewologun thinks the good intentions are genuine – it’s just that the organisations haven’t been thinking critically about the level of commitment that would be necessary to bring about change, or even how to measure the desired outcomes.

Thanks to this lack of forethought, many of the existing courses may have also been too passive and theoretical. “If you are just lecturing at someone about how pervasive bias is, but you’re not giving them the tools to change, I think there can be a tendency for them to think that bias is normal and thus not something they need to work on,” says Prof Alex Lindsey at the University of Memphis. Attempts to combat bias could therefore benefit from more evidence-based exercises that increase participants’ self-reflection, alongside concrete steps for improvement.

Lindsey’s research team recently examined the benefits of a “perspective-taking” exercise, in which participants were asked to write about the challenges faced by someone within a minority group. They found that the intervention brought about lasting changes to people’s attitudes and behavioural intentions for months after the training. “We might not know exactly what it’s like to be someone of a different race, sex, religion, or sexual orientation from ourselves, but everyone, to some extent, knows what it feels like to be excluded in a social situation,” Lindsey says. “Once trainees realise that some people face that kind of ostracism on a more regular basis as a result of their demographic characteristics, I think that realisation can lead them to respond more empathetically in the future.”

Lindsey has found that you should also encourage participants to reflect on the ways their own behaviour may have been biased in the past, and to set themselves future goals during their training. Someone will be much more likely to act in an inclusive way if they decide, in advance, to challenge any inappropriate comments about a minority group, for example. This may be more powerful still, he says, if there is some kind of follow-up to check in with participants’ progress – an opportunity that the briefer courses completely miss. (Interestingly, he has found that these reflective techniques can be especially effective among people who are initially resistant to the idea of diversity training.)

More generally, these courses may often fail to bring about change because people become too defensive about the very idea that they may be prejudiced. Without excusing the biases, the courses might benefit from explaining how easily stereotypes can be absorbed – even by good, well-intentioned people – while also emphasising the individual responsibility to take action. Finally, they could teach people to recognise the possibility of “moral licensing”, in which an ostensibly virtuous act, such as attending the diversity course itself, or promoting someone from a minority, excuses a prejudiced behaviour afterwards, since you’ve already “proven” yourself to be a liberal and caring person. 

Ultimately, the psychologists I’ve spoken to all agree that organisations should stop seeing unconscious bias and diversity training as a quick fix, and instead use it as the foundation for broader organisational change.

“Anyone who has been in any type of schooling system knows that even the best two- or three-hour class is not going to change our world for ever,” says Prof Calvin Lai, who investigates implicit bias at Washington University in St Louis. “It’s not magic.” But it may act as a kind of ice-breaker, he says, helping people to be more receptive to other initiatives – such as those aimed at a more inclusive recruitment process.

Chang agrees. “Diversity training is unlikely to be an effective standalone solution,” he says. “But that doesn’t mean that it can’t be an effective component of a multipronged approach to improving diversity, equity and inclusion in organisations.”

Atewologun compares it to the public health campaigns to combat obesity and increase fitness. You can provide people with a list of the calories in different foods and the benefits of exercise, she says – but that information, alone, is unlikely to lead to significant weight loss, without continued support that will help people to act on that information. Similarly, education about biases can be a useful starting point, but it’s rather absurd to expect that ingrained habits could evaporate in a single hour of education.

“We could be a lot more explicit that it is step one,” Atewologun adds. “We need multiple levels of intervention – it’s an ongoing project.”

Source: https://www.theguardian.com/science/2021/apr/25/what-unconscious-bias-training-gets-wrong-and-how-to-fix-it

Barbara Kay: Actually, it turns out that you may be less racist than you’ve been led to believe

What Kay misses is the usefulness of the IAT for people to become more mindful of their implicit biases, and, in so doing, be more aware of their “thinking fast” mode to use Kahneman’s phrase.

It is not automatic that being more mindful or aware changes behaviour but it can play a significant role (and yes, the benefits can be overstated). Having implicit biases does not necessarily mean acting on them.

Kay did not mention whether or not she took the test. Given her biases evident in her columns, it would be interesting to know whether she took the IAT and what, if anything, she learned.

I certainly found it useful, revealing and most important, discomforting as I became more aware of the gap between my policy mind and views, and what was under the surface.

Anyone can take the test on the Project Implicit Website, hosted by Harvard U. By October 2015, more than 17 million individuals had completed it (with presumably 90-95 per cent of them then self-identifying as racist). Liberal observers love the IAT. New York Times columnist Nicholas Kristof wrote in 2015, “It’s sobering to discover that whatever you believe intellectually, you’re biased about race, gender, age or disability.” Kristof’s tone is more complacent than sober, though. For progressives, the more widespread bias can be demonstrated to be, the more justifiable institutional and state intrusions into people’s minds become.

Banaji and Greenwald have themselves made far-reaching claims for the test: the “automatic White preference expressed on the Race IAT is now established as signaling discriminatory behavior. It predicts discriminatory behavior even among research participants who earnestly (and, we believe, honestly) espouse egalitarian beliefs. …. Among research participants who describe themselves as racially egalitarian, the Race IAT has been shown, reliably and repeatedly, to predict discriminatory behavior that was observed in the research.”

Problem is, none of this can be authenticated. According to Singal, a great deal of scholarly work that takes the shine off the researchers’ claims has been ignored by the media. The IAT is not verifiable and correlates weakly with actual lived outcomes. Meta-analyses cannot examine whether IAT scores predict discriminatory behaviour accurately enough for real-world application. An individual can score high for bias on the IAT and never act in a biased manner. He can take the test twice and get two wildly different scores. After almost two decades, the researchers have never posted test-retest reliability of commonly used IATs in publication.

It’s a wonder the IAT has a shred of credibility left. In 2015 Greenwald and Banaji responded to a critic that the psychometric issues with race and ethnicity IATS “render them problematic to use to classify persons as likely to engage in discrimination,” and that “attempts to diagnostically use such measures for individuals risk undesirably high rates of erroneous classifications.” Greenwald acknowledged to Singal that “no one has yet undertaken a study of the race IAT’s test-related reliability.” In other words, the IAT is a useless tool for measuring implicit bias.

In an interesting aside, Singal points to a 2012 study published in Psychological Science by psychologist Jacquie Vorauer. As her experiment, Vorauer set white Canadians to work with aboriginal partners. Before doing so, some of the participants took an IAT that pertained to aboriginals, some took a non-race IAT and others were asked for their explicit feelings about the group. Aboriginals in the race-IAT group subsequently reported feeling less valued by their white partners as compared to aboriginals in all of the other groups. Vorauer writes, “If completing the IAT enhances caution and inhibition, reduces self-efficacy, or primes categorical thinking, the test may instead have negative effects.” As Singal notes, this “suggests some troubling possibilities.”The IAT has potentially misinformed millions of test-takers, who believe that they are likely to act, or are routinely acting, with bias against their fellow citizens. Harbouring biases is part of the human condition, and it is our right to hold them, especially those warranted by epidemiology and reason. Our actions are all that should concern our employers or the state’s legal apparatus. Any directive to submit to the IAT by the state or a state-sponsored entity like the CBC constitutes an undemocratic intrusion into the individual’s privacy.

Source: Barbara Kay: Actually, it turns out that you may be less racist than you’ve been led to believe | National Post

Will virtual body swapping reduce racism in America?

Interesting series of experiments, that take the Implicit Association Test increased mindfulness to a new level:

There are at least two ways to walk from Harlem to Soho: as a white person, and as a black person. With research on “virtual body swapping,” in which participants use a virtual reality headset to enter a body of different race, Americans can wear both pairs of shoes.

“In Harlem, they’ll see the sheer volume of police standing on a corner on just your average Saturday afternoon,” says Courtney Cogburn, an assistant professor of social work at Columbia University. Although white participants might have noticed the police anyway, their experiences as black avatars will make them better understand “the racism that’s in the air,” she says. “It’s ambient.”

Beginning in September, and partnering with Stanford University in a $250,000 research project, Cogburn will equip white people with a virtual reality headset and motion tracking suit. Participants, including supporters of Bernie Sanders, Hillary Clinton and Donald Trump, will operate a black avatar as they walk through a virtual New York City, hopefully better understanding systemic racism. Given the recent shootings in Louisiana and Minnesota, in which two white police officers each killed black citizens, Cogburn says this intervention “could certainly be applied to police.” Despite the retaliation in Dallas, where black protestors killed five white officers, the research will not introduce black people into virtual white bodies.

Cogburn’s team will be the first to take virtual body-swappers into a virtual real-world setting, she says. Since 2009, Stadford’s Virtual Human Interaction Lab has put body-swappers in a neutral setting, where they operate avatars of different races and simply look around a room. The goal has been to improve empathy. After just this five-minute experience, participants showed less white preference on an implicit association test, which measures how they associate words such as “love” and “evil” with race.

Body-swapping has proven more effective than simply imagining oneself as another race. In fact, when Stanford researchers asked students to imagine a day in the life of a black person, including attending a job interview, these participants afterward showed more white preference because they had imagined, or “activated,” stereotypes. In similar research related to age, Stanford has gotten participants to virtually enter older versions of themselves, reducing ageism and increasing motivation for financial planning.

Europeans have also used body-swapping to boost empathy between races. Mel Slater, a computer science professor specializing in virtual environments at the University of London, explains that white participants in a neutral setting quickly identify with their virtual black avatars. “People don’t go, ‘Wow, I’ve got a black body!’” he says. “They just take it. It just is … The most interesting thing is that nothing interesting happens.”

Virtual body-swapping works by tricking the participant’s brain into viewing the avatar as itself. “The brain doesn’t like uncertainty or confusion,” says Slater. “It tries to come up with answers. The simplest thing it can come up with is that it’s my body.” Slater’s team found that the empathy isn’t just temporary. One week after the body swap, participants maintained lower white preference scores, and Slater expects the effect might last even longer.

The ethics of changing people’s mindsets are unclear. Slayter says virtual body-swapping could be incorporated into games that are designed for general employee or police training. Ideally, white participants would have black avatars, unaware of the intent to increase empathy. “It’s better if the purpose isn’t explicit,” says Slater. However, “if they’re surreptitiously altering people’s attitudes, people would find ethical problems with that.”

Virtual reality holds other limitations. It can make people dizzy, and Slater doesn’t recommend it for children under age 13, as neurologists don’t yet understand how it might affect developing brains. Some say citizens could better develop empathy by spending time with real people of other races, rather than playing around with avatars. Further, in video games, the assignment of racialized avatars has proven to worsen prejudice. When the developers of a game called Rust randomly assigned white players to have black avatars, gamers posted racist rants on blogs, with one declaring, “if I’m black I’m asking for a refund.”

Cogburn says virtual body-swapping can supplement efforts to build real-world bridges, as many Americans don’t naturally spend time with people of other races. She says the real-world setting won’t worsen prejudice because the purpose of her project is to help people recognize racism, not necessarily feel it. “I’m still questioning whether empathy is the goal or if it really is possible,” she says. “What might be more important is to try to understand [racism].” Participants will have nothing at stake, not even points in a video game, as they walk through New York City—its neighbourhoods, its prejudices and its police officers. “It’s a vicarious observation of these things,” says Cogburn. “It’s not actually happening to you, and that’s the point.”

Artificial Intelligence’s White Guy Problem – The New York Times

Valid concerns regarding who designs the algorithms and how to eliminate or at least minimize bias.

Perhaps the algorithms and the people who write them should take the Implicit Association Test?

But this hand-wringing is a distraction from the very real problems with artificial intelligence today, which may already be exacerbating inequality in the workplace, at home and in our legal and judicial systems. Sexism, racism and other forms of discrimination are being built into the machine-learning algorithms that underlie the technology behind many “intelligent” systems that shape how we are categorized and advertised to.

Take a small example from last year: Users discovered that Google’s photo app, which applies automatic labels to pictures in digital photo albums, was classifying images of black people as gorillas. Google apologized; it was unintentional.

But similar errors have emerged in Nikon’s camera software, which misread images of Asian people as blinking, and in Hewlett-Packard’s web camera software, which had difficulty recognizing people with dark skin tones.

This is fundamentally a data problem. Algorithms learn by being fed certain images, often chosen by engineers, and the system builds a model of the world based on those images. If a system is trained on photos of people who are overwhelmingly white, it will have a harder time recognizing nonwhite faces.

A very serious example was revealed in an investigation published last month by ProPublica. It found that widely used software that assessed the risk of recidivism in criminals was twice as likely to mistakenly flag black defendants as being at a higher risk of committing future crimes. It was also twice as likely to incorrectly flag white defendants as low risk.

The reason those predictions are so skewed is still unknown, because the company responsible for these algorithms keeps its formulas secret — it’s proprietary information. Judges do rely on machine-driven risk assessments in different ways — some may even discount them entirely — but there is little they can do to understand the logic behind them.

Police departments across the United States are also deploying data-driven risk-assessment tools in “predictive policing” crime prevention efforts. In many cities, including New York, Los Angeles, Chicago and Miami, software analyses of large sets of historical crime data are used to forecast where crime hot spots are most likely to emerge; the police are then directed to those areas.

At the very least, this software risks perpetuating an already vicious cycle, in which the police increase their presence in the same places they are already policing (or overpolicing), thus ensuring that more arrests come from those areas. In the United States, this could result in more surveillance in traditionally poorer, nonwhite neighborhoods, while wealthy, whiter neighborhoods are scrutinized even less. Predictive programs are only as good as the data they are trained on, and that data has a complex history.

Histories of discrimination can live on in digital platforms, and if they go unquestioned, they become part of the logic of everyday algorithmic systems. Another scandal emerged recently when it was revealed that Amazon’s same-day delivery service was unavailable for ZIP codes in predominantly black neighborhoods. The areas overlooked were remarkably similar to those affected by mortgage redlining in the mid-20th century. Amazon promised to redress the gaps, but it reminds us how systemic inequality can haunt machine intelligence.

And then there’s gender discrimination. Last July, computer scientists at Carnegie Mellon University found that women were less likely than men to be shown ads on Google for highly paid jobs. The complexity of how search engines show ads to internet users makes it hard to say why this happened — whether the advertisers preferred showing the ads to men, or the outcome was an unintended consequence of the algorithms involved.

Regardless, algorithmic flaws aren’t easily discoverable: How would a woman know to apply for a job she never saw advertised? How might a black community learn that it were being overpoliced by software?

We need to be vigilant about how we design and train these machine-learning systems, or we will see ingrained forms of bias built into the artificial intelligence of the future.

Like all technologies before it, artificial intelligence will reflect the values of its creators. So inclusivity matters — from who designs it to who sits on the company boards and which ethical perspectives are included. Otherwise, we risk constructing machine intelligence that mirrors a narrow and privileged vision of society, with its old, familiar biases and stereotypes.

If we look at how systems can be discriminatory now, we will be much better placed to design fairer artificial intelligence. But that requires far more accountability from the tech community. Governments and public institutions can do their part as well: As they invest in predictive technologies, they need to commit to fairness and due process.

While machine-learning technology can offer unexpected insights and new forms of convenience, we must address the current implications for communities that have less power, for those who aren’t dominant in elite Silicon Valley circles.

Currently the loudest voices debating the potential dangers of superintelligence are affluent white men, and, perhaps for them, the biggest threat is the rise of an artificially intelligent apex predator.

But for those who already face marginalization or bias, the threats are here.

Source: Artificial Intelligence’s White Guy Problem – The New York Times

So You Flunked A Racism Test. Now What?

More on the inbuilt biases and prejudices that we all have:

You’re probably at least a little bit racist and sexist and homophobic. Most of us are.

Before you get all indignant, try taking one of the popular implicit-association tests. Created by sociologists at Harvard, the University of Washington, and the University of Virginia, they measure people’s unconscious prejudice by testing how easy — or difficult — it is for the test-takers to associate words like “good” and “bad” with images of black people versus white people, or “scientist” and “lab” with men versus women.

These tests find that — regardless of how many Pride parades they attend or how many “This is what a feminist looks like” T-shirts they own — most people trust men over women, white people over minorities, and straight people over queer people. These trends can hold true regardless of the gender, race or sexuality of the test-taker. I’m from India, and the test found that I’m biased against Asian-Americans.

There is research indicating that these types of implicit prejudices may help explain why cops are more likely to shoot unarmed black men than to shoot unarmed white men, and why employers are more likely to hire white candidates than equally qualified black candidates.

….Perhaps more important than the lasting effects of this particular approach, Paller’s findings are proof that our implicit attitudes are malleable — and maybe, just maybe, it is possible for people to let go of prejudice for good, if they want to. But it won’t be easy.

“Adults have had years and years of exposure to stereotypes,” Paller says. And biases take hold early — studies have found that kids as young as 4 and 5 show racial and gender bias. “It can take a lot of effort to reverse that.”

Paller stresses that this is very preliminary research. To confirm the results, a lot more people have to be tested. “Plus, we still don’t know if changing people’s results on the implicit-bias test translates to them acting differently toward minorities in the real world,” he notes.

The bottom line: There’s no silver bullet, says Anthony Greenwald, a social psychologist at the University of Washington who helped develop the implicit-association test. At least not yet. “But I’m open-minded,” says Greenwald, who wasn’t involved in Paller’s study. “It will be interesting to see if these results can be reproduced.”

Greenwald, who perhaps understands more about bias than just about anyone, has taken the implicit-association test himself. His results haven’t budged over the years. He’s still biased along racial and gender lines, he says, “even though I really don’t like having these biases.”

And while it may be hard to correct such inbuilt bias, it starts with being more mindful of such associations and automatic thinking.

So You Flunked A Racism Test. Now What? : Code Switch : NPR.