Author Information: Kamili Posey, Kingsborough College, Kamili.Posey@kbcc.cuny.edu.
Posey, Kamili. “Scientism in the Philosophy of Implicit Bias Research.” Social Epistemology Review and Reply Collective 7, no. 10 (2018): 1-15.
Kamili Posey’s article was posted over two instalments. You can read the first here, but the pdf of the article includes the entire piece, and gives specific page references. Shortlink: https://wp.me/p1Bfg0-41k
In the previous piece, I outlined some concerns with philosophers, and particularly philosophers of social science, assuming the success of implicit interventions into implicit bias. Motivated by a pointed note by Jennifer Saul (2017), I aimed to briefly go through some of the models lauded as offering successful interventions and, in essence, “get out of the armchair.”
(IAT) Models and Egalitarian Goal Models
In this final piece, I go through the last two models, Glaser and Knowles’ (2007) and Blair et al.’s (2001) (IAT) models and Moskowitz and Li’s (2011) egalitarian goal model. I reiterate that this is not an exhaustive analysis of such models nor is it intended as a criticism of experiments pertaining to implicit bias. Mostly, I am concerned that the science is interesting but that the scientism – the application of tentative results to philosophical projects – is less so. It is from this point that I proceed.
Like Mendoza et al.’s (2010) implementation intentions, Glaser and Knowles’ (2007) (IMCP) aims to capture implicit motivations that are capable of inhibiting automatic stereotype activation. Glaser and Knowles measure (IMCP) in terms of an implicit negative attitude toward prejudice, or (NAP), and an implicit belief that oneself is prejudiced, or (BOP). This is done by retooling the (IAT) to fit both (NAP) and (BOP): “To measure NAP we constructed an IAT that pairs the categories ‘prejudice’ and ‘tolerance’ with the categories ‘bad’ and ‘good.’ BOP was assessed with an IAT pairing ‘prejudiced’ and ‘tolerant’ with ‘me’ and ‘not me.’”
Study participants were then administered the Shooter Task, the (IMCP) measures, and the Race Prejudice (IAT) and Race-Weapons Stereotype (RWS) tests in a fixed order. They predicted that (IMCP) as an implicit goal for those high in (IMCP) “should be able to short-circuit the effect of implicit anti-Black stereotypes on automatic anti-Black behavior.” The results seemed to suggest that this was the case. Glaser and Knowles found that study participants who viewed prejudice as particularly bad “[showed] no relationship between implicit stereotypes and spontaneous behavior.”
There are a few considerations missing from the evaluation of the study results. First, with regard to the Shooter Task, Glaser and Knowles (2007) found that “the interaction of target race by object type, reflecting the Shooter Bias, was not statistically significant.” That is, the strength of the relationship that Correll et al. (2002) found between study participants and the (high) likelihood that they would “shoot” at black targets was not found in the present study. Additionally, they note that they “eliminated time pressure” from the task itself. Although it was not suggested that this impacted the usefulness of the measure of Shooter Bias, it is difficult to imagine that it did not do so. To this, they footnote the following caveat:
Variance in the degree and direction of the stereotype endorsement points to one reason for our failure to replicate Correll et. al’s (2002) typically robust Shooter Bias effect. That is, our sample appears to have held stereotypes linking Blacks and weapons/aggression/danger to a lesser extent than did Correll and colleagues’ participants. In Correll et al. (2002, 2003), participants one SD below the mean on the stereotype measure reported an anti-Black stereotype, whereas similarly low scorers on our RWS IAT evidenced a stronger association between Whites and weapons. Further, the adaptation of the Shooter Task reported here may have been less sensitive than the procedure developed by Correll and colleagues. In the service of shortening and simplifying the task, we used fewer trials, eliminated time pressure and rewards for speed and accuracy, and presented only one background per trial.
Glaser and Knowles claimed that the interaction of the (RWS) with the Shooter Task results proved “significant,” however, if the Shooter Bias failed to materialize (in the standard Correll et al. way) with study participants, it is difficult to see how the (RWS) was measuring anything except itself, generally speaking. This is further complicated by the fact that the interaction between the Shooter Bias and the (RWS) revealed “a mild reverse stereotype associating Whites with weapons (d = -0.15) and a strong stereotype associating Blacks with weapons (d = 0.83), respectively.”
Recall that Glaser and Knowles (2007) aimed to show that participants high in (IMCP) would be able to inhibit implicit anti-black stereotypes and thus inhibit automatic anti-black behaviors. Using (NAP) and (BOP) as proxies for implicit control, participants high in (NAP) and moderate in (BOP) – as those with moderate (BOP) will be motivated to avoid bias – should show the weakest association between (RWS) and Shooter Bias. Instead, the lowest levels of Shooter Bias were seen in “low NAP, high BOP, and low RWS” study participants, or those who do not disapprove of prejudice, would describe themselves as prejudiced, and also showed lowest levels of (RWS).
They noted that neither “NAP nor BOP alone was significantly related to the Shooter Bias,” but “the influence of RWS on Shooter Bias remained significant.” In fact, greater bias was actually found with higher (NAP) and (BOP) levels. This bias seemed to map on to the initial results of the Shooter Task results. It is most likely that (RWS) was the most important measure in this study for assessing implicit bias, not, as the study claimed, for assessing implicit motivation to control prejudice.
What Kind of Bias?
It is also not clear that the (RWS) was not capturing explicit bias instead of implicit bias in this study. At the point at which study participants were tasked with the (RWS), automatic stereotype activation may have been inhibited just in virtue of study participants involvement in the Shooter Task and (IAT) assessments regarding race-related prejudice. That is, race-sensitivity was brought to consciousness in the sequencing of the test process.
Although we cannot get into the heads of the study participants, this counter explanation seems a compelling possibility. That is, that the sequential tasks involved in the study captured study participants’ ability to increase focus and increase conscious attention to the race-related (IAT) test. Additionally, it is possible that some study participants could both cue and follow their own conscious internal commands, “If I see a black face, I won’t judge!” Consider that this is exactly how implementation intentions work.
Consider that this is also how Armageddon chess and other speed strategy games work. In Park et al.’s (2008) follow-up study on (IMCP) and cognitive depletion, they retreat somewhat from their initial claims about the implicit nature of (IMCP):
We cannot state for certain that our measure of IMCP reflects a purely nonconscious construct, nor that differential speed to “shoot” Black armed men vs. White armed men in a computer simulation reflects purely automatic processes. Most likely, the underlying stereotypes, goals, and behavioral responses represent a blend of conscious and nonconscious influences…Based on the results of the present study and those of Glaser and Knowles (2008), it would be premature to conclude that IMCP is a purely and wholly automatic construct, meeting the “four horsemen” criteria (Bargh, 1990). Specifically, it is not yet clear whether high IMCP participants initiate control of prejudice without intention; whether implicit control of prejudice can itself be inhibited, if for some reason someone wanted to; nor whether IMCP-instigated control of spontaneous bias occurs without awareness.
If the (IMCP) potentially measures low-level conscious attention, this makes the question of what implicit measurements actually measure in the context of sequential tasks all the more important. In the two final examples, Blair et al.’s (2001) study on the use of counterstereotype imagery and Moskowitz and Li’s (2011) study on the use of counterstereotype egalitarian goals, we are again confronted with the issue of sequencing. In the study by Moskowitz and Li, study participants were asked to write down an example of a time when “they failed to live up to the ideal specified by an egalitarian goal, and to do so by relaying an event relating to African American men.”
They were then given a series of computerized LDTs (lexicon decision tasks) and primes involving photographs of black and white faces and stereotypical and non-stereotypical attributes of black people (crime, lazy, stupid, nervous, indifferent, nosy). Over a series of four experiments, Moskowitz and Li found that when egalitarian goals were “accessible,” study participants were able to successfully generate stereotype inhibition. Blair et al. asked study participants to use counterstereotypical (CS) gender imagery over a series of five experiments, e.g., “Think of a strong, capable woman,” and then administered a series of implicit measures, including the (IAT).
Similar to Moskowitz and Li (2011), Blair et al. (2001) found that (CS) gender imagery was successful in reducing implicit gender stereotypes leaving “little doubt that the CS mental imagery per se was responsible for diminishing implicit stereotypes.” In both cases, the study participants were explicitly called upon to focus their attention on experiences and imagery pertaining to negative stereotypes before the implicit measures, i.e., tasks, were administered. Again it is not clear that the implicit measures measured the supposed target.
In the case of Moskowitz and Li’s (2011) experiment, the study participants began by relating moments in their lives where they failed to live up to their goals. However, those goals can only be understood within a particular social and political framework where holding negatively prejudicial beliefs about African-American men is often explicitly judged harshly, even if not implicitly so. Given this, we might assume that the study participants were compelled into a negative affective state. But does this matter? As suggested by the study by Monteith (1993), and later study by Amodio et. al (2007), guilt can be a powerful tool.
Questions of Guilt
If guilt was produced during the early stages of the experiment, it may have also participated in the inhibition of stereotype activation. Moskowitz and Li (2011) noted that “during targeted questioning in the debriefing, no participants expressed any conscious intent to inhibit stereotypes on the task, nor saw any of the tasks performed during the computerized portion of the experiment as related to the egalitarian goals they had undermined earlier in the session.”
But guilt does not have to be conscious for it to produce effects. The guilt produced by recalling a moment of negative bias could be part and parcel of a larger feeling of moral failure. Moskowitz and Li needed to adequately disambiguate competing implicit motivations for stereotype inhibition before arriving at a definitive conclusion. This, I think, is a limitation of the study.
However, the same case could be made for (CS) imagery. Blair et al. (2001) noted that it is, in fact, possible that they too have missed competing motivations and competing explanations for stereotype inhibition. Particularly, they suggested that by emphasizing counterstereotyping the researchers “may have communicated the importance of avoiding stereotypes and increased their motivation to do so.” Still, the researchers dismissed that this would lead to better (faster, more accurate) performance of the (IAT), but that is merely asserting that the (IAT) must measure exactly what the (IAT) claims that it does. Fast, accurate, and conscious measures are excluded from that claim. Complicated internal motivations are excluded from that claim.
But on what grounds? Consider Fielder et al.’s (2006) argument that the (IAT) is susceptible to faking and strategic processing, or Brendl et al.’s (2001) argument that it is not possible to infer a single cause from (IAT) results, or Fazio and Olson’s (2003) claim “the IAT has little to do with what is automatically activated in response to a given stimulus.”
These studies call into question the claim that implicit measures like the (IAT) can measure implicit bias in the clear, problem-free manner that is often suggested in the literature. Implicit interventions into implicit bias that utilize the (IAT) are difficult to support for this reason. Implicit interventions that utilize sequential (IAT) tasks are also difficult to support for this reason. Of course, this is also live debate and the problems I have discussed here are far from the only ones that plague this type of research.
That said, when it comes to this research we are too often left wondering if the measure itself is measuring the right thing. Are we capturing implicit bias or some other socially generated phenomenon? Are the measured changes we see in study results reflecting the validity of the instrument or the cognitive maneuverings of study participants? These are all critical questions that need sussing out. The temporary result is that the target conclusion that implicit interventions will lead to reductions in real-world discrimination will move further away. We find evidence of this conclusion in Forscher et al.’s (2018) meta-analysis of 492 implicit interventions:
We found little evidence that changes in implicit measures translated into changes in explicit measures and behavior, and we observed limitations in the evidence base for implicit malleability and change. These results produce a challenge for practitioners who seek to address problems that are presumed to be caused by automatically retrieved associations, as there was little evidence showing that change in implicit measures will result in changes for explicit measures or behavior…Our results suggest that current interventions that attempt to change implicit measures will not consistently change behavior in these domains. These results also produce a challenge for researchers who seek to understand the nature of human cognition because they raise new questions about the causal role of automatically retrieved associations…To better understand what the results mean, future research should innovate with more reliable and valid implicit, explicit, and behavioral tasks, intensive manipulations, longitudinal measurement of outcomes, heterogeneous samples, and diverse topics of study.
Finally, what I take to be behind Alcoff’s (2010) critical question at the beginning of this piece is a kind of skepticism about how individuals can successfully tackle implicit bias through either explicit or implicit practices without the support of the social spaces, communities, and institutions that give shape to our social lives. Implicit bias is related to the culture one is in and the stereotypes it produces. So instead of insisting on changing people to reduce stereotyping, what if we insisted on changing the culture?
As Alcoff notes: “We must be willing to explore more mechanisms for redress, such as extensive educational reform, more serious projects of affirmative action, and curricular mandates that would help to correct the identity prejudices built up out of faulty narratives of history.” This is an important point. It is a point that philosophers who work on implicit bias would do well to take seriously.
Science may not give us the way out of racism, sexism, and gender discrimination. At the moment, it may only give us tools for seeing ourselves a bit more clearly. Further claims about implicit interventions appear as willful scientism. They reinforce the belief that science can cure all of our social and political ills. But this is magical thinking.
Contact details: Kamili.Posey@kbcc.cuny.edu
Alcoff, Linda. (2010). “Epistemic Identities,” in Episteme 7 (2), p. 132.
Amodio, David M., Devine, Patricia G., and Harmon-Jones, Eddie. (2007). “A Dynamic Model of Guilt: Implications for Motivation and Self-Regulation in the Context of Prejudice,” in Psychological Science 18(6), pp. 524-30.
Blair, I. V., Ma, J. E., & Lenton, A. P. (2001). “Imagining Stereotypes Away: The Moderation of Implicit Stereotypes Through Mental Imagery,” in Journal of Personality and Social Psychology, 81:5, p. 837.
Correll, Joshua, Bernadette Park, Bernd Wittenbrink, and Charles M. Judd. (2002). “The Police Officer’s Dilemma: Using Ethnicity to Disambiguate Potentially Threatening Individuals,” in Journal of Personality and Social Psychology, Vol. 83, No. 6, 1314–1329.
Devine, P. G., & Monteith, M. J. (1993). “The Role of Discrepancy-Associated Affect in Prejudice Reduction,” in Affect, Cognition and Stereotyping: Interactive Processes in Group Perception, eds., D. M. Mackie & D. L. Hamilton. San Diego: Academic Press, pp. 317–344.
Forscher, Patrick S., Lai, Calvin K., Axt, Jordan R., Ebersole, Charles R., Herman, Michelle, Devine, Patricia G., and Nosek, Brian A. (August 13, 2018). “A Meta-Analysis of Procedures to Change Implicit Measures.” [Preprint]. Retrieved from https://doi.org/10.31234/osf.io/dv8tu.
Glaser, Jack and Knowles, Eric D. (2007). “Implicit Motivation to Control Prejudice,” in Journal of Experimental Social Psychology 44, p. 165.
Kawakami, K., Dovidio, J. F., Moll, J., Hermsen, S., & Russin, A. (2000). “Just Say No (To Stereotyping): Effects Of Training in Negation of Stereotypic Associations on Stereotype Activation,” in Journal of Personality and Social Psychology, 78, 871–888.
Kawakami, K., Dovidio, J. F., and van Kamp, S. (2005). “Kicking the Habit: Effects of Nonstereotypic Association Training and Correction Processes on Hiring Decisions,” in Journal of Experimental Social Psychology 41:1, pp. 68-69.
Greenwald, Anthony G., Banaji, Mahzarin R., and Nosek, Brian A. (2015). “Statistically Small Effects of the Implicit Association Test Can Have Societally Large Effects,” in Journal of Personality and Social Psychology, Vol. 108, No. 4, pp. 553-561.
Mendoza, Saaid, Gollwitzer, Peter, and Amodio, David. (2010). “Reducing the Expression of Implicit Stereotypes: Reflexive Control through Implementation Intentions,” in Personality and Social Psychology Bulletin 36:4, p. 513-514.
Monteith, Margo. (1993). “Self-Regulation of Prejudiced Responses: Implications for Progress in Prejudice-Reduction Efforts,” in Journal of Personality and Social Psychology 65:3, p. 472.
Moskowitz, Gordon and Li, Peizhong. (2011). “Egalitarian Goals Trigger Stereotype Inhibition,” in Journal of Experimental Social Psychology 47, p. 106.
Oswald, F. L., Mitchell, G., Blanton, H., Jaccard, J., and Tetlock, P. E. (2013). “Predicting Ethnic and Racial Discrimination: A Meta-Analysis of IAT Criterion Studies,” in Journal of Personality and Social Psychology, Vol. 105, pp. 171-192
Oswald, F. L., Mitchell, G., Blanton, H., Jaccard, J., and Tetlock, P. E. (2015). “Using the IAT to Predict Ethnic and Racial Discrimination: Small Effect Sizes of Unknown Societal Significance,” in Journal of Personality and Social Psychology, Vol. 108, No. 4, pp. 562-571.
Saul, Jennifer. (2017). “Implicit Bias, Stereotype Threat, and Epistemic Injustice,” in The Routledge Handbook of Epistemic Injustice, eds. Ian James Kidd, José Medina, and Gaile Pohlhaus, Jr. [Google Books Edition] New York: Routledge.
Webb, Thomas L., Sheeran, Paschal, and Pepper, John. (2012). “Gaining Control Over Responses to Implicit Attitude Tests: Implementation Intentions Engender Fast Responses on Attitude-Incongruent Trials,” in British Journal of Social Psychology 51, pp. 13-32.
 Glaser, Jack and Knowles, Eric D. (2007). “Implicit Motivation to Control Prejudice,” in Journal of Experimental Social Psychology 44, p. 165.
 Glaser, Jack and Knowles, Eric D. (2007), p. 167.
 Glaser, Jack and Knowles, Eric D. (2007), p. 170.
 Glaser, Jack and Knowles, Eric D. (2007), p. 168.
 Glaser, Jack and Knowles, Eric D. (2007), p. 168.
 Glaser, Jack and Knowles, Eric D. (2007), p. 169.
 Glaser, Jack and Knowles, Eric D. (2007), p. 169. Of this “rogue” group, Glaser and Knowles note: “This group had, on average, a negative RWS (i.e., rather than just a low bias toward Blacks, they tended to associate Whites more than Blacks with weapons; see footnote 4). If these reversed stereotypes are also uninhibited, they should yield reversed Shooter Bias, as observed here” (169).
 Glaser, Jack and Knowles, Eric D. (2007), p. 169.
 Glaser, Jack and Knowles, Eric D. (2007), p. 169.
 Sang Hee Park, Jack Glaser, and Eric D. Knowles. (2008). “Implicit Motivation to Control Prejudice Moderates the Effect of Cognitive Depletion on Unintended Discrimination,” in Social Cognition, Vol. 26, No. 4, p. 416.
 Moskowitz, Gordon and Li, Peizhong. (2011). “Egalitarian Goals Trigger Stereotype Inhibition,” in Journal of Experimental Social Psychology 47, p. 106.
 Blair, I. V., Ma, J. E., & Lenton, A. P. (2001). “Imagining Stereotypes Away: The Moderation of Implicit Stereotypes Through Mental Imagery,” in Journal of Personality and Social Psychology, 81:5, p. 837.
 Amodio, David M., Devine, Patricia G., and Harmon-Jones, Eddie. (2007). “A Dynamic Model of Guilt: Implications for Motivation and Self-Regulation in the Context of Prejudice,” in Psychological Science 18(6), pp. 524-30
 Moskowitz, Gordon and Li, Peizhong (2011), p. 108.
 Blair, I. V., Ma, J. E., & Lenton, A. P. (2001), p. 838.
 Fielder, Klaus, Messner, Claude, Bluemke, Matthias. (2006). “Unresolved problems with the ‘I’, the ‘A’, and the ‘T’: A logical and Psychometric Critique of the Implicit Association Test (IAT),” in European Review of Social Psychology, 12, pp. 74-147. Brendl, C. M., Markman, A. B., & Messner, C. (2001). “How Do Indirect Measures of Evaluation Work? Evaluating the Inference of Prejudice in the Implicit Association Test,” in Journal of Personality and Social Psychology, 81(5), pp. 760-773. Fazio, R. H., and Olson, M. A. (2003). “Implicit Measures in Social Cognition Research: Their Meaning and Uses,” in Annual Review of Psychology 54, pp. 297-327.
 There is significant debate over the issue of whether the implicit bias that (IAT) tests measure translate into real-world discriminatory behavior. This is a complex and compelling issue. It is also an issue that could render moot the (IAT) as an implicit measure of anything full stop. Anthony G. Greenwald, Mahzarin R. Banaji, and Brian A. Nosek (2015) write: “IAT measures have two properties that render them problematic to use to classify persons as likely to engage in discrimination. Those two properties are modest test–retest reliability (for the IAT, typically between r = .5 and r = .6; cf., Nosek et al., 2007) and small to moderate predictive validity effect sizes. Therefore, attempts to diagnostically use such measures for individuals risk undesirably high rates of erroneous classifications. These problems of limited test-retest reliability and small effect sizes are maximal when the sample consists of a single person (i.e., for individual diagnostic use), but they diminish substantially as sample size increases. Therefore, limited reliability and small to moderate effect sizes are not problematic in diagnosing system-level discrimination, for which analyses often involve large samples” (557). However, Oswald et al. (2013) argue that “IAT scores correlated strongly with measures of brain activity but relatively weakly with all other criterion measures in the race domain and weakly with all criterion measures in the ethnicity domain. IATs, whether they were designed to tap into implicit prejudice or implicit stereotypes, were typically poor predictors of the types of behavior, judgments, or decisions that have been studied as instances of discrimination, regardless of how subtle, spontaneous, controlled, or deliberate they were. Explicit measures of bias were also, on average, weak predictors of criteria in the studies covered by this meta-analysis, but explicit measures performed no worse than, and sometimes better than, the IATs for predictions of policy preferences, interpersonal behavior, person perceptions, reaction times, and microbehavior. Only for brain activity were correlations higher for IATs than for explicit measures…but few studies examined prediction of brain activity using explicit measures. Any distinction between the IATs and explicit measures is a distinction that makes little difference, because both of these means of measuring attitudes resulted in poor prediction of racial and ethnic discrimination” (182-183). For further details about this debate, see: Oswald, F. L., Mitchell, G., Blanton, H., Jaccard, J., and Tetlock, P. E. (2013). “Predicting Ethnic and Racial Discrimination: A Meta-Analysis of IAT Criterion Studies,” in Journal of Personality and Social Psychology, Vol. 105, pp. 171-192 and Greenwald, Anthony G., Banaji, Mahzarin R., and Nosek, Brian A. (2015). “Statistically Small Effects of the Implicit Association Test Can Have Societally Large Effects,” in Journal of Personality and Social Psychology, Vol. 108, No. 4, pp. 553-561.
 See: Oswald, F. L., Mitchell, G., Blanton, H., Jaccard, J., and Tetlock, P. E. (2015). “Using the IAT to Predict Ethnic and Racial Discrimination: Small Effect Sizes of Unknown Societal Significance,” in Journal of Personality and Social Psychology, Vol. 108, No. 4, pp. 562-571.
 Forscher, Patrick S., Lai, Calvin K., Axt, Jordan R., Ebersole, Charles R., Herman, Michelle, Devine, Patricia G., and Nosek, Brian A. (August 13, 2018). “A Meta-Analysis of Procedures to Change Implicit Measures.” [Preprint]. Retrieved from https://doi.org/10.31234/osf.io/dv8tu.
 Alcoff, Linda. (2010). “Epistemic Identities,” in Episteme 7 (2), p. 132.
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