An article in Science takes implicit bias (and its measurement) for granted despite the problems, and suggests interventions that haven’t been shown to work

March 25, 2023 • 11:30 am

Nearly all psychologists have lost their enthusiasm for the idea of implicit bias because of its manifold problems; and the most common test for implicit bias, the IAT (implicit association test) has largely been abandoned by its users. In light of this you’d think the idea and its IAT metric would have dropped out of sight in academics. But that’s not the way it works these days. If an idea like implicit bias fits into the academic Zeitgeist, and we can actually (pretend to) measure how biased people are when they don’t even know it, then it’s full steam ahead with the idea. Ferret out everybody’s bias, because we all have it! And ignore those niggling doubts about the IAT!

This, along with the often problematic notion of “systemic racism”, which persists in all of academia, are two examples of how the authoritarian Left will cling to a concept when it’s been found empirically useless, simply because the idea comports with their ideology.  And this article in Science—one of the world’s two most prestigious science journals—buys straight into the idea of implicit bias and IAT, hardly mentioning that they’re deeply controversial and have not been found to improve race relations. The article also assumes that inequities are due to racism (another dubious conclusion), and that the racism within science is a structural racism, not maniftested by biased individuals but baked into the system. Finally, the article raises the possibility of Big Brother-like monitoring of people to catch the implicit bias that we all know they harbor. (We discussed this suggestion the other day.)

There is no science journal I know of that has not gone in this direction if it’s weighed in at all on that ideology. Science is one and Nature is another.  It’s embarrassing how the two most prestigious journals concerned with understanding nature play so fast and loose with the facts.

Click to read:

The article’s largely about bias in medicine. I’ll give a few quotes showing how embedded the idea of implicit bias is in the article, how little the author and the IAT-users recognize the weaknesses, and describe new methods of measuring implicit bias in light of the IAT’s failure (which they don’t admit). The article is long, but is so similar to others of its ilk that I’ll be brief.

Note the immediate buy-in of the concept of implicit bias below. The article begins with the story of Chastine, a patient with autoimmune disease whose steroids made her gain weight, and then, she claims, doctors would assume that her extra weight was her primary medical condition. (This could, of course, be dispelled by the patient simply telling the doctor this at the outset).:

Stories like Chastine’s are unfortunately common, say researchers who examine how implicit biases—unconscious assumptions based on skin color, gender, sexual preference, or appearance—in health care providers affect patient care. Chastine, who is Black and queer, is now channeling her troubled experience with the medical establishment to aid studies of implicit bias and identify ways to counter it. She is part of a 5-year collaboration between various departments at both the University of Washington (UW) and the University of California, San Diego (UCSD), in which a team is developing a tool to give physicians feedback in real time during patient visits—or shortly after—on what they can do to mitigate their unconscious prejudices.

Here comes the IAW as used by Janice Sabin, a researcher at the University of Washington:

Sabin used the well-known Implicit Association Test (IAT), which determines how strongly an individual associates a trait—such as race or sexual orientation—with a subjective value, such as “good” or “bad.” The quicker you match each concept to a subjective value, the greater the association and the higher your score, which broadly indicates a stronger implicit association between the trait and value.

Sabin found the doctors she tested—a few of them nonwhite—held the unconscious bias that white patients took their medication as prescribed more so than Black patients. It was one of the first studies showing health care providers had unintentional racial prejudices. “It was kind of scary because this was a concept completely foreign to [many] people at the time,” Sabin says.

As I said, the article does mention issues with the IAT, but doesn’t state that its problems (lack of replication, evidence against the unconscious nature of the bias, and the failure of the tests results to lead to effective antiracist programs) are so severe that serious psychologists have abandoned the test:

The IAT remains a standard tool for measuring implicit bias, although some have criticized it because it has to be taken several times to reveal a reliable result, as people’s scores could change every time they take it. Even when people come out neutral on race, most studies will reveal some kind of unconscious prejudice, such as an unrecognized preference of certain sexual orientations or religions.

. . . Scientists have long studied several kinds of interventions that attempt to “erase” implicit bias, but few of them have shown lasting effects. “There is a robust science around implicit bias,” Hardeman says. But, “There is no gold standard for how to intervene right now. It’s imprinted in our brains in ways that make it really hard.”

Simple interventions can dampen biases, as measured by successive IATs, but the changes are usually modest and don’t persist.

. . .Simply asking health care providers to take the IAT without providing context or tools can be counterproductive. A study in 2015 indicated that when medical students are told about their unconscious bias without direction on overcoming it, they tend to get anxious, confused, and nervous interacting with patients who belong to social groups different from their own. That’s why even a quick training on skills to mitigate implicit bias can go a long way, according to Hardeman.

But as the article says (and other articles agree) why measure bias in a way that’s counterproductive if there is no “gold standard about how to intervene” to mitigate bias? Is this all just performative action with no effect on what it hopes to change? And so researchers move on to the Big Brother tests:

That made him [Brian Wood, an infectious disease specialist] eager to take part in UnBIASED’s first experiments, which rely on cameras installed in exam rooms. The cameras in Wood’s Seattle clinic captured interactions between him and his patients, including close-ups of his and their facial features and body language. “I found quite quickly that the patient and I both forgot the cameras were there and just fell into our usual routine and conversation,” he says.

The UnBIASED team then used a type of artificial intelligence (AI) known as machine learning to analyze patterns in the recordings and identify nonverbal cues that could indicate implicit bias. In one of the clips Wood was later shown, he was talking with a patient while leaning forward with his arms crossed on the desk, body language he worries may have made him seem closed and unapproachable. “I reflected on my own as to how that body language might be felt and perceived by the patient,” he says. Wood, who hopes to improve his demeanor, says he welcomed such feedback and is eager for more.

“Reflecting on possible negative moments during a visit was not easy, but felt important and valuable,” Wood says.

The team is now working on translating the experiment’s results into feedback like “digital nudges”—such as an icon that pops onto the computer screen, a wearable device, or other mechanism telling physicians to interrupt patients less or look them in the eye more often. But the UnBIASED team still has challenges interpreting the data in the recordings. For instance, nonverbal signals are nuanced, Hartzler says. “It’s not always as simple as ‘more interruptions means bad.’”

Translation: this method doesn’t seem to work. But nevertheless, the article persists, describing other methods of measuring hidden bias, including clinical simulations of biases and “microaggressions”, constructing training exercises for physicians and nurses, and calling out people on the spot. The problem is that while the article describes a panoply of methods, with at least one “increasing their recognition of bias” in a way that lasted a year (but their own biases or other people’s?), none of the methods seem to have an effect on mitigating bias, conscious or unconscious.  Despite the failure of developing an efficacious program so far, they will persist—forever:

Getting buy-in from whole health care systems could accelerate the process. Recently, California, Michigan, Maryland, Minnesota, and Washington state passed legislation mandating implicit bias training for the medical professionals they license. And since June 2022, Massachusetts physicians are required to take implicit bias training to get a new license or get recertified to practice.

Although researchers see this as a good step, they worry mandated training will become a one-off box-checking exercise. Sustained implicit bias training for physicians should instead be the norm, some emphasize. Hospitals also need to monitor and collect data on health care outcomes for different groups in order to monitor equity, Sabin says. “You have to know where the disparities lie and then begin to work backwards from that.”

It won’t be easy, Hardeman says, noting that, at least in the United States, centuries of white supremacy and other forms of bigotry have resulted in deep-rooted stereotypes and other implicit biases. “Every single person should be thinking about doing this work,” she says. “But if they’re doing it within a system that hasn’t addressed its own biases and racism, then it’s not going to be fully effective.”

Clearly, we’re going to have mandated training for the rest of our lives (much of it involving a form of compelled speech), and all of us who aren’t people of color will be told that we harbor implicit biases and participate in white supremacy, which is now structurally built into medicine.

And yes, of course some people are biased! I can’t help but assume that these people really do mean well instead of just trying to enact an ideology that they know won’t help the situation. But perhaps they should be using methods that work, and if they can’t show they work, they shouldn’t be part of mandated training. It’s not going to make people more “inclusive” to tell them all that they’re ridden with biases they don’t even know about the invisible Klan robes we all wear.

When this kind of palaver invades all of the prestige science journals—in article after article that all say exactly the same thing—you know that we’re in for a long haul.

If you want to measure your own implicit bias for race using Harvard’s IAT, try it here.

h/t: Steve

14 thoughts on “An article in Science takes implicit bias (and its measurement) for granted despite the problems, and suggests interventions that haven’t been shown to work

  1. I used to be on the faculty of the UW medical school, but contracted my case of emeritis a little before the woke epidemic swept over academia. Watching this, I have so far tended to take it as slightly ominous commedia dell’arte. But this item goes way beyond commedia into the realm of dystopian horror fiction: “The team is now working on translating the experiment’s results into feedback like “digital nudges”—such as an icon that pops onto the computer screen, a wearable device, or other mechanism telling physicians to interrupt patients less or look them in the eye more often.” YIKES!

    However, some faint signs of a silver lining are visible, at least outside the Anglosphere: . Well, younger individuals among us know where to emigrate.

  2. “Chastine, who is Black and queer, is now channeling her troubled experience with the medical establishment to aid studies of implicit bias and identify ways to counter it.”

    So, I take it that nothing in that sentence should associate with any biases.

  3. “…this kind of palaver…” To paraphrase Seth Andrews, ideology, whether religious or political, makes one say stupid things.

  4. I just clicked on the link to the implicit bias test and am stumped by the first question (and the second to some extent). “People are the same no matter our race”. What is that asking? In all sorts of ways, I don’t think people are the same. So should I strongly disagree? Is the question designed to be somewhat ambiguous? How can my answer accurately reveal my bias if they don’t know how I understood the question?

    Help me out! I am missing something here?

  5. A meta-lesson in all of this is just how easily humans seem to be able to accept as fact things that aren’t true. Even researchers who have been trained to use scientific and statistical methods to avoid fooling themselves, nonetheless do fool themselves when dealing with topics in which they have strong emotional or ideological commitments—as seems to be the case with these authors. Once falsehoods are integrated into one’s thinking, they are difficult to remove. Hence our problem with the continued belief that IAW testing is useful and that anti-bias training helps.

    Perhaps the Science authors have unmasked their own “hidden bias” toward believing what they want to believe. Even where they equivocate, they nonetheless land back where their prior beliefs lead them. That should be a blinking red light to the authors, but it apparently isn’t.

  6. “Implicit Association” is not unlike the “spectral evidence” discussed by Cotton Mather in
    his masterwork, Wonders of the Invisible World (1693). On the other hand, even the Reverend Mather warned against placing undue weight on spectral evidence.

  7. The highly-educated, well-credentialed, and powerfully-placed cannot be wrong, especially when they are gung ho on doing good. Apparently, their educations in the sciences did not inoculate them against this and similar foolishness. Why is that? Why is reason so easily trumped by ideology and hubris? How is it that the novelists, playwrights, poets, and historians have been writing about such human follies for millennia, and yet we with all our sophisticated knowledge have been unable to break such spells?

  8. The Chastine avatar reminds me that nothing a patient says should be taken at face value without verification, especially when it is self-serving of an agenda, as here. She says now she gained weight only after starting steroids but how do we know this is true? Obese patients of any race and orientation make all kinds of excuses for their weight. Someone with a racial or sexual chip on her shoulder will “remember” her pre-treatment weight differently if it allows her to claim that her doctor is showing implicit bias about what he ascribes the current obesity to.

    Jerry is right. She could simply have reported her pre-treatment weight and that would have prevented friction (although this would still need to be verified). Of course if she had a longitudinal relationship with a doctor—surely if she has a chronic disease needing meds with many side effects, someone saw her at the outset to make the diagnosis and start the treatment— that doc would know if she was svelte or chubby then.

    Personally, if I was meeting a patient for the first time and knew only that she had a disease requiring long-term steroids, I would be very surprised if she wasn’t on the heavy side in the particular pattern we call “Cushingoid”. That would be my first thought. Now if she had a BMI of 35 with fat arms and legs and didn’t have the other side effects of steroids, I’d be thinking this is an obese woman who may well have gained some more weight from the drugs. If the patient happens to be Chastine who is a black lesbian, then I guess I’m going to get dinged for implicit bias against her intersectionality.

    The person administering this implicit bias training module who hears me say all this is going to smirk and say, “Yep. All doctors do what you did. They come up with excuses as to how they aren’t biased and why the test is wrong. Marks off for being difficult, Dr. House.”

    Which is why I’m glad I’m retired.

  9. Quite interesting. Bias hard to recgnise. Self conscious behavior helpful. Not sure all the tests and fret are helpful. Thanks for revealing issues. Agree with above glad I’m retired too

  10. I hope an addendum is in order.
    This passage quoted from the Science article (which is otherwise long on anecdote and short on detail) troubles me:

    Sabin found the doctors she tested—a few of them nonwhite—held the unconscious bias that white patients took their medication as prescribed more so than Black patients. It was one of the first studies showing health care providers had unintentional racial prejudices.

    A quick Google search of “racial differences in medication adherence” returns several pages of published articles showing (at least in their summaries) that white patients do indeed take their medication more reliably than black patients do. Or rather, rates of adherence (percentage of pills taken from a given series of prescriptions, and whether the prescriptions were filled or renewed at all are the usual measures) are higher in whites. There are lots of reasons why patients don’t adhere (“comply” was the old term, now out of fashion). Improving medication adherence is a major focus of improving quality of care. Sometimes cost is a barrier.

    So how is it to be guilty of implicit bias to be aware of a explicit fact documented in the medical literature? To game the test, are we supposed to pretend that we don’t know this to be true?

    Now, sure, we are not supposed to dislike our non-adherent patients and we shouldn’t assume that every individual black patient is non-adherent and that’s why their blood pressure or epilepsy are poorly controlled at every visit. There may also be other factors that predict non-adherence more accurately than race — you can ask, for one thing. But if there’s a fact out there, you can’t predict it doesn’t exist just so as not to be accused of implicit bias.

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