Archives For psychometrics

Author Information: Brian Martin, University of Wollongong, bmartin@uow.edu.au.

Martin, Brian. “Bad Social Science.” Social Epistemology Review and Reply Collective 8, no. 3 (2019): 6-16.

The pdf of the article gives specific page references. Shortlink: https://wp.me/p1Bfg0-47a

Image by Sanofi Pasteur via Flickr / Creative Commons

 

People untrained in social science frameworks and methods often make assumptions, observations or conclusions about the social world.[1] For example, they might say, “President Trump is a psychopath,” thereby making a judgement about Trump’s mental state. The point here is not whether this judgement is right or wrong, but whether it is based on a careful study of Trump’s thoughts and behaviour drawing on relevant expertise.

In most cases, the claim “President Trump is a psychopath” is bad psychology, in the sense that it is a conclusion reached without the application of skills in psychological diagnosis expected among professional psychologists and psychiatrists.[2] Even a non-psychologist can recognise cruder forms of bad psychology: they lack the application of standard tools in the field, such as comparison of criteria for psychopathy with Trump’s thought and behaviour.

“Bad social science” here refers to claims about society and social relationships that fall very far short of what social scientists consider good scholarship. This might be due to using false or misleading evidence, making faulty arguments, drawing unsupported conclusions or various other severe methodological, empirical or theoretical deficiencies.

In all sorts of public commentary and private conversations, examples of bad social science are legion. Instances are so common that it may seem pointless to take note of problems with ill-informed claims. However, there is value in a more systematic examination of different sorts of everyday bad social science. Such an examination can point to what is important in doing good social science and to weaknesses in assumptions, evidence and argumentation. It can also provide insights into how to defend and promote high-quality social analysis.

Here, I illustrate several facets of bad social science found in a specific public scientific controversy: the Australian vaccination debate. It is a public debate in which many partisans make claims about social dynamics, so there is ample material for analysis. In addition, because the debate is highly polarised, involves strong emotions and is extremely rancorous, it is to be expected that many deviations from calm, rational, polite discourse would be on display.

Another reason for selecting this topic is that I have been studying the debate for quite a number of years, and indeed have been drawn into the debate as a “captive of controversy.”[3] Several of the types of bad social science are found on both sides of the debate. Here, I focus mainly on pro-vaccination campaigners for reasons that will become clear.

In the following sections, I address several facets of bad social science: ad hominem attacks, not defining terms, use of limited and dubious evidence, misrepresentation, lack of reference to alternative viewpoints, lack of quality control, and drawing of unjustified conclusions. In each case, I provide examples from the Australian public vaccination debate, drawing on my experience. In a sense, selecting these topics represents an informal application of grounded theory: each of the shortcomings became evident to me through encountering numerous instances. After this, I note that there is a greater risk of deficient argumentation when defending orthodoxy.

With this background, I outline how studying bad social science can be of benefit in three ways: as a pointer to particular areas in which it is important to maintain high standards, as a toolkit for responding to attacks on social science, and as a reminder of the need to improve public understanding of social science approaches.

Ad Hominem

In the Australian vaccination debate, many partisans make adverse comments about opponents as a means of discrediting them. Social scientists recognise that ad hominem argumentation, namely attacking the person rather than dealing with what they say, is illegitimate for the purposes of making a case.

In the mid 1990s, Meryl Dorey founded the Australian Vaccination Network (AVN), which became the leading citizens’ group critical of government vaccination policy.[4] In 2009, a pro-vaccination citizens’ group called Stop the Australian Vaccination Network (SAVN) was set up with the stated aim of discrediting and shutting down the AVN.[5] SAVNers referred to Dorey with a wide range of epithets, for example “cunt.”[6]

What is interesting here is that some ad hominem attacks contain an implicit social analysis. One of them is “liar.” SAVNer Ken McLeod accused Dorey of being a liar, giving various examples.[7] However, some of these examples show only that Dorey persisted in making claims that SAVNers believed had been refuted.[8] This does not necessarily constitute lying, if lying is defined, as it often is by researchers in the area, as consciously intending to deceive.[9] To the extent that McLeod failed to relate his claims to research in the field, his application of the label “liar” constitutes bad social science.

Another term applied to vaccine critics is “babykiller.” In the Australian context, this word contains an implied social analysis, based on these premises: public questioning of vaccination policy causes some parents not to have their children vaccinated, leading to reduced vaccination rates and thence to more children dying of infectious diseases.

“Babykiller” also contains a moral judgement, namely that public critics of vaccination are culpable for the deaths of children from vaccination-preventable diseases. Few of those applying the term “babykiller” provide evidence to back up the implicit social analysis and judgement, so the label in these instances represents bad social science.

There are numerous other examples of ad hominem in the vaccination debate, on both sides. Some of them might be said to be primarily abuse, such as “cunt.” Others, though, contain an associated or implied social analysis, so to judge its quality it is necessary to assess whether the analysis conforms to conventions within social science.

Undefined terms

In social science, it is normal to define key concepts, either by explicit definitions or descriptive accounts. The point is to provide clarity when the concept is used.

One of the terms used by vaccination supporters in the Australian debate is “anti-vaxxer.” Despite the ubiquity of this term in social and mass media, I have never seen it defined. This is significant because of the considerable ambiguity involved. “Anti-vaxxer” might refer to parents who refuse all vaccines for their children and themselves, parents who have their children receive some but not all recommended vaccines, parents who express reservations about vaccination, and/or campaigners who criticise vaccination policy.

The way “anti-vaxxer” is applied in practice tends to conflate these different meanings, with the implication that any criticism of vaccination puts you in the camp of those who refuse all vaccines. The label “anti-vaxxer” has been applied to me even though I do not have a strong view about vaccination.[10]

Because of the lack of a definition or clear meaning, the term “anti-vaxxer” is a form of ad hominem and also represents bad social science. Tellingly, few social scientists studying the vaccination issue use the term descriptively.

In their publications, social scientists may not define all the terms they use because their meanings are commonly accepted in the field. Nearly always, though, some researchers pay close attention to any widely used concept.[11] When such a concept remains ill-defined, this may be a sign of bad social science — especially when it is used as a pejorative label.

Limited and Dubious Evidence

Social scientists normally seek to provide strong evidence for their claims and restrict their claims to what the evidence can support. In public debates, this caution is often disregarded.

After SAVN was formed in 2009, one of its initial claims was that the AVN believed in a global conspiracy to implant mind-control chips via vaccinations. The key piece of evidence SAVNers provided to support this claim was that Meryl Dorey had given a link to the website of David Icke, who was known to have some weird beliefs, such as that the world is ruled by shape-shifting reptilian humanoids.

The weakness of this evidence should be apparent. Just because Icke has some weird beliefs does not mean every document on his website involves adherence to weird beliefs, and just because Dorey provided a link to a document does not prove she believes in everything in the document, much less subscribes to the beliefs of the owner of the website. Furthermore, Dorey denied believing in a mind-control global conspiracy.

Finally, even if Dorey had believed in this conspiracy, this does not mean other members of the AVN, or the AVN as an organisation, believed in the conspiracy. Although the evidence was exceedingly weak, several SAVNers, after I confronted them on the matter, initially refused to back down from their claims.[12]

Misrepresentation

When studying an issue, scholars assume that evidence, sources and other material should be represented fairly. For example, a quotation from an author should fairly present the author’s views, and not be used out of context to show something different than what the author intended.

Quite a few campaigners in the Australian vaccination debate use a different approach, which might be called “gotcha”. Quotes are used to expose writers as incompetent, misguided or deluded. Views of authors are misrepresented as a means of discrediting and dismissing them.

Judy Wilyman did her PhD under my supervision and was the subject of attack for years before she graduated. On 13 January 2016, just two days after her thesis was posted online, it was the subject of a front-page story in the daily newspaper The Australian. The journalist, despite having been informed of a convenient summary of the thesis, did not mention any of its key ideas, instead claiming that it involved a conspiracy theory. Quotes from the thesis, taken out of context, were paraded as evidence of inadequacy.

This journalistic misrepresentation of Judy’s thesis was remarkably influential. It led to a cascade of hostile commentary, with hundreds of online comments on the numerous stories in The Australian, an online petition signed by thousands of people, and calls by scientists for Judy’s PhD to be revoked. In all the furore, not a single critic of her thesis posted a fair-minded summary of its contents.[13]

Alternative Viewpoints?

In high-quality social science, it is common to defend a viewpoint, but considered appropriate to examine other perspectives. Indeed, when presenting a critique, it is usual to begin with a summary of the work to be criticised.

In the Australian vaccination debate, partisans do not even attempt to present the opposing side’s viewpoint. I have never seen any campaigner provide a summary of the evidence and arguments supporting the opposition’s viewpoint. Vaccination critics present evidence and arguments that cast doubt on the government’s vaccination policy, and never try to summarise the evidence and arguments supporting it. Likewise, backers of the government’s policy never try to summarise the case against it.

There are also some intermediate viewpoints, divergent from the entrenched positions in the public debate. For example, there are some commentators who support some vaccines but not all the government-recommended ones, or who support single vaccines rather than multiple vaccines. These non-standard positions are hardly ever discussed in public by pro-vaccination campaigners.[14] More commonly, they are implicitly subsumed by the label “anti-vaxxer.”

To find summaries of arguments and evidence on both sides, it is necessary to turn to work by social scientists, and then only the few of them studying the debate without arguing for one side or the other.[15]

Quality Control

When making a claim, it makes sense to check it. Social scientists commonly do this by checking sources and/or by relying on peer review. For contemporary issues, it’s often possible to check with the person who made the claim.

In the Australian vaccination debate, there seems to be little attempt to check claims, especially when they are derogatory claims about opponents. I can speak from personal experience. Quite a number of SAVNers have made comments about my work, for example in blogs. On not a single occasion has any one of them checked with me in advance of publication.

After SAVN was formed and I started writing about free speech in the Australian vaccination debate, I sent drafts of some of my papers to SAVNers for comment. Rather than using this opportunity to send me corrections and comments, the response was to attack me, including by making complaints to my university.[16] Interestingly, the only SAVNer to have been helpful in commenting on drafts is another academic.

Another example concerns Andrew Wakefield, a gastroenterologist who was lead author of a paper in The Lancet suggesting that the possibility that the MMR triple vaccine (measles, mumps and rubella) might be linked to autism should be investigated. The paper led to a storm of media attention.

Australian pro-vaccination campaigns, and quite a few media reports, refer to Wakefield’s alleged wrongdoings, treating them as discrediting any criticism of vaccination. Incorrect statements about Wakefield are commonplace, for example that he lost his medical licence due to scientific fraud. It is a simple matter to check the facts, but apparently few do this. Even fewer take the trouble to look into the claims and counterclaims about Wakefield and qualify their statements accordingly.[17]

Drawing Conclusions

Social scientists are trained to be cautious in drawing conclusions, ensuring that they do not go beyond what can be justified from data and arguments. In addition, it is standard to include a discussion of limitations. This sort of caution is often absent in public debates.

SAVNers have claimed great success in their campaign against the AVN, giving evidence that, for example, their efforts have prevented AVN talks from being held and reduced media coverage of vaccine critics. However, although AVN operations have undoubtedly been hampered, this does not necessarily show that vaccination rates have increased or, more importantly, that public health has benefited.[18]

Defending Orthodoxy

Many social scientists undertake research in controversial areas. Some support the dominant views, some support an unorthodox position and quite a few try not to take a stand. There is no inherent problem in supporting the orthodox position, but doing so brings greater risks to the quality of research.

Many SAVNers assume that vaccination is a scientific issue and that only people with scientific credentials, for example degrees or publications in virology or epidemiology, have any credibility. This was apparent in an article by philosopher Patrick Stokes entitled “No, you’re not entitled to your opinion” that received high praise from SAVNers.[19] It was also apparent in the attack on Judy Wilyman, whose PhD was criticised because it was not in a scientific field, and because she analysed scientific claims without being a scientist. The claim that only scientists can validly criticise vaccination is easily countered.[20] The problem for SAVNers is that they are less likely to question assumptions precisely because they support the dominant viewpoint.

There is a fascinating aspect to campaigners supporting orthodoxy: they themselves frequently make claims about vaccination although they are not scientists with relevant qualifications. They do not apply their own strictures about necessary expertise to themselves. This can be explained as deriving from “honour by association,” a process parallel to guilt by association but less noticed because it is so common. In honour by association, a person gains or assumes greater credibility by being associated with a prestigious person, group or view.

Someone without special expertise who asserts a claim that supports orthodoxy implicitly takes on the mantle of the experts on the side of orthodoxy. It is only those who challenge orthodoxy who are expected to have relevant credentials. There is nothing inherently wrong with supporting the orthodox view, but it does mean there is less pressure to examine assumptions.

My initial example of bad social science was calling Donald Trump a psychopath. Suppose you said Trump has narcissistic personality disorder. This might not seem to be bad social science because it accords with the views of many psychologists. However, agreeing with orthodoxy, without accompanying deployment of expertise, does not constitute good social science any more than disagreeing with orthodoxy.

Lessons

It is all too easy to identify examples of bad social science in popular commentary. They are commonplace in political campaigning and in everyday conversations.

Being attuned to common violations of good practice has three potential benefits: as a useful reminder to maintain high standards; as a toolkit for responding to attacks on social science; and as a guide to encouraging greater public awareness of social scientific thinking and methods.

Bad Social Science as a Reminder to Maintain High Standards

Most of the kinds of bad social science prevalent in the Australian vaccination debate seldom receive extended attention in the social science literature. For example, the widely used and cited textbook Social Research Methods does not even mention ad hominem, presumably because avoiding it is so basic that it need not be discussed.

It describes five common errors in everyday thinking that social scientists should avoid: overgeneralisation, selective observation, premature closure, the halo effect and false consensus.[21] Some of these overlap with the shortcomings I’ve observed in the Australian vaccination debate. For example, the halo effect, in which prestigious sources are given more credibility, has affinities with honour by association.

The textbook The Craft of Research likewise does not mention ad hominem. In a final brief section on the ethics of research, there are a couple of points that can be applied to the vaccination debate. For example, ethical researchers “do not caricature or distort opposing views.” Another recommendation is that “When you acknowledge your readers’ alternative views, including their strongest objections and reservations,” you move towards more reliable knowledge and honour readers’ dignity.[22] Compared with the careful exposition of research methods in this and other texts, the shortcomings in public debates are seemingly so basic and obvious as to not warrant extended discussion.

No doubt many social scientists could point to the work of others in the field — or even their own — as failing to meet the highest standards. Looking at examples of bad social science can provide a reminder of what to avoid. For example, being aware of ad hominem argumentation can help in avoiding subtle denigration of authors and instead focusing entirely on their evidence and arguments. Being reminded of confirmation bias can encourage exploration of a greater diversity of viewpoints.

Malcolm Wright and Scott Armstrong examined 50 articles that cited a method in survey-based research that Armstrong had developed years earlier. They discovered that only one of the 50 studies had reported the method correctly. They recommend that researchers send drafts of their work to authors of cited studies — especially those on which the research depends most heavily — to ensure accuracy.[23] This is not a common practice in any field of scholarship but is worth considering in the interests of improving quality.

Bad Social Science as a Toolkit for Responding to Attacks

Alan Sokal wrote an intentionally incoherent article that was published in 1996 in the cultural studies journal Social Text. Numerous commentators lauded Sokal for carrying out an audacious prank that revealed the truth about cultural studies, namely that it was bunk. These commentators had not carried out relevant studies themselves, nor were most of them familiar with the field of cultural studies, including its frameworks, objects of study, methods of analysis, conclusions and exemplary pieces of scholarship.

To the extent that these commentators were uninformed about cultural studies yet willing to praise Sokal for his hoax, they were involved in a sort of bad social science. Perhaps they supported Sokal’s hoax because it agreed with their preconceived ideas, though investigation would be needed to assess this hypothesis.

Most responses to the hoax took a defensive line, for example arguing that Sokal’s conclusions were not justified. Only a few argued that interpreting the hoax as showing the vacuity of cultural studies was itself poor social science.[24] Sokal himself said it was inappropriate to draw general conclusions about cultural studies from the hoax,[25] so ironically it would have been possible to respond to attackers by quoting Sokal.

When social scientists come under attack, it can be useful to examine the evidence and methods used or cited by the attackers, and to point out, as is often the case, that they fail to measure up to standards in the field.

Encouraging Greater Public Awareness of Social Science Thinking and Methods

It is easy to communicate with like-minded scholars and commiserate about the ignorance of those who misunderstand or wilfully misrepresent social science. More challenging is to pay close attention to the characteristic ways in which people make assumptions and reason about the social world and how these ways often fall far short of the standards expected in scholarly circles.

By identifying common forms of bad social science, it may be possible to better design interventions into public discourse to encourage more rigorous thinking about evidence and argument, especially to counter spurious and ill-founded claims by partisans in public debates.

Conclusion

Social scientists, in looking at research contributions, usually focus on what is high quality: the deepest insights, the tightest arguments, the most comprehensive data, the most sophisticated analysis and the most elegant writing. This makes sense: top quality contributions offer worthwhile models to learn from and emulate.

Nevertheless, there is also a role for learning from poor quality contributions. It is instructive to look at public debates involving social issues in which people make judgements about the same sorts of matters that are investigated by social scientists, everything from criminal justice to social mores. Contributions to public debates can starkly show flaws in reasoning and the use of evidence. These flaws provide a useful reminder of things to avoid.

Observation of the Australian vaccination debate reveals several types of bad social science, including ad hominem attacks, failing to define terms, relying on dubious sources, failing to provide context, and not checking claims. The risk of succumbing to these shortcomings seems to be magnified when the orthodox viewpoint is being supported, because it is assumed to be correct and there is less likelihood of being held accountable by opponents.

There is something additional that social scientists can learn by studying contributions to public debates that have serious empirical and theoretical shortcomings. There are likely to be characteristic failures that occur repeatedly. These offer supplementary guidance for what to avoid. They also provide insight into what sort of training, for aspiring social scientists, is useful for moving from unreflective arguments to careful research.

There is also a challenge that few scholars have tackled. Given the prevalence of bad social science in many public debates, is it possible to intervene in these debates in a way that fosters greater appreciation for what is involved in good quality scholarship, and encourages campaigners to aspire to make sounder contributions?

Contact details: bmartin@uow.edu.au

References

Blume, Stuart. Immunization: How Vaccines Became Controversial. London: Reaktion Books, 2017.

Booth, Wayne C.; Gregory G. Colomb, Joseph M. Williams, Joseph Bizup and William T. FitzGerald, The Craft of Research, fourth edition. Chicago: University of Chicago Press, 2016.

Collier, David; Fernando Daniel Hidalgo and Andra Olivia Maciuceanu, “Essentially contested concepts: debates and applications,” Journal of Political Ideologies, 11(3), October 2006, pp. 211–246.

Ekman, Paul. Telling Lies: Clues to Deceit in the Marketplace, Politics, and Marriage. New York: Norton, 1985.

Hilgartner, Stephen, “The Sokal affair in context,” Science, Technology, & Human Values, 22(4), Autumn 1997, pp. 506–522.

Lee, Bandy X. The Dangerous Case of Donald Trump: 27 Psychiatrists and Mental Health Experts Assess a President. New York: St. Martin’s Press, 2017.

Martin, Brian; and Florencia Peña Saint Martin. El mobbing en la esfera pública: el fenómeno y sus características [Public mobbing: a phenomenon and its features]. In Norma González González (Coordinadora), Organización social del trabajo en la posmodernidad: salud mental, ambientes laborales y vida cotidiana (Guadalajara, Jalisco, México: Prometeo Editores, 2014), pp. 91-114.

Martin, Brian. “Debating vaccination: understanding the attack on the Australian Vaccination Network.” Living Wisdom, no. 8, 2011, pp. 14–40.

Martin, Brian. “On the suppression of vaccination dissent.” Science & Engineering Ethics. Vol. 21, No. 1, 2015, pp. 143–157.

Martin, Brian. Evidence-based campaigning. Archives of Public Health, 76, no. 54. (2018), https://doi.org/10.1186/s13690-018-0302-4.

Martin, Brian. Vaccination Panic in Australia. Sparsnäs, Sweden: Irene Publishing, 2018.

Ken McLeod, “Meryl Dorey’s trouble with the truth, part 1: how Meryl Dorey lies, obfuscates, prevaricates, exaggerates, confabulates and confuses in promoting her anti-vaccination agenda,” 2010, http://www.scribd.com/doc/47704677/Meryl-Doreys-Trouble-With-the-Truth-Part-1.

Neuman, W. Lawrence. Social Research Methods: Qualitative and Quantitative Approaches, seventh edition. Boston, MA: Pearson, 2011.

Scott, Pam; Evelleen Richards and Brian Martin, “Captives of controversy: the myth of the neutral social researcher in contemporary scientific controversies,” Science, Technology, & Human Values, Vol. 15, No. 4, Fall 1990, pp. 474–494.

Sokal, Alan D. “What the Social Text affair does and does not prove,” in Noretta Koertge (ed.), A House Built on Sand: Exposing Postmodernist Myths about Science (New York: Oxford University Press, 1998), pp. 9–22

Stokes, Patrick. “No, you’re not entitled to your opinion,” The Conversation, 5 October 2012, https://theconversation.com/no-youre-not-entitled-to-your-opinion-9978.

Wright, Malcolm, and J. Scott Armstrong, “The ombudsman: verification of citations: fawlty towers of knowledge?” Interfaces, 38 (2), March-April 2008.

[1] Thanks to Meryl Dorey, Stephen Hilgartner, Larry Neuman, Alan Sokal and Malcolm Wright for valuable feedback on drafts.

[2] For informed commentary on these issues, see Bandy X. Lee, The Dangerous Case of Donald Trump: 27 Psychiatrists and Mental Health Experts Assess a President (New York: St. Martin’s Press, 2017).

[3] Pam Scott, Evelleen Richards and Brian Martin, “Captives of controversy: the myth of the neutral social researcher in contemporary scientific controversies,” Science, Technology, & Human Values, Vol. 15, No. 4, Fall 1990, pp. 474–494.

[4] The AVN, forced to change its name in 2014, became the Australian Vaccination-skeptics Network. In 2018 it voluntarily changed its name to the Australian Vaccination-risks Network.

[5] In 2014, SAVN changed its name to Stop the Australian (Anti-)Vaccination Network.

[6] Brian Martin and Florencia Peña Saint Martin. El mobbing en la esfera pública: el fenómeno y sus características [Public mobbing: a phenomenon and its features]. In Norma González González (Coordinadora), Organización social del trabajo en la posmodernidad: salud mental, ambientes laborales y vida cotidiana (Guadalajara, Jalisco, México: Prometeo Editores, 2014), pp. 91-114.

[7] Ken McLeod, “Meryl Dorey’s trouble with the truth, part 1: how Meryl Dorey lies, obfuscates, prevaricates, exaggerates, confabulates and confuses in promoting her anti-vaccination agenda,” 2010, http://www.scribd.com/doc/47704677/Meryl-Doreys-Trouble-With-the-Truth-Part-1.

[8] Brian Martin, “Debating vaccination: understanding the attack on the Australian Vaccination Network,” Living Wisdom, no. 8, 2011, pp. 14–40, at pp. 28–30.

[9] E.g., Paul Ekman, Telling Lies: Clues to Deceit in the Marketplace, Politics, and Marriage (New York: Norton, 1985).

[10] On Wikipedia I am categorised as an “anti-vaccination activist,” a term that is not defined on the entry listing those in the category. See Brian Martin, “Persistent bias on Wikipedia: methods and responses,” Social Science Computer Review, Vol. 36, No. 3, June 2018, pp. 379–388.

[11] See for example David Collier, Fernando Daniel Hidalgo and Andra Olivia Maciuceanu, “Essentially contested concepts: debates and applications,” Journal of Political Ideologies, 11(3), October 2006, pp. 211–246.

[12] Brian Martin. “Caught in the vaccination wars (part 3)”, 23 October 2012, http://www.bmartin.cc/pubs/12hpi-comments.html.

[13] The only possible exception to this statement is Michael Brull, “Anti-vaccination cranks versus academic freedom,” New Matilda, 7 February 2016, who reproduced my own summary of the key points in the thesis relevant to Australian government vaccination policy. For my responses to the attack, see http://www.bmartin.cc/pubs/controversy.html – Wilyman, for example “Defending university integrity,” International Journal for Educational Integrity, Vol. 13, No. 1, 2017, pp. 1–14.

[14] Brian Martin, Vaccination Panic in Australia (Sparsnäs, Sweden: Irene Publishing, 2018), pp. 15–24.

[15] E.g., Stuart Blume, Immunization: How Vaccines Became Controversial (London: Reaktion Books, 2017).

[16] Brian Martin. “Caught in the vaccination wars”, 28 April 2011, http://www.bmartin.cc/pubs/11savn/.

[17] For own commentary on Wakefield, see “On the suppression of vaccination dissent,” Science & Engineering Ethics, Vol. 21, No. 1, 2015, pp. 143–157.

[18] Brian Martin. Evidence-based campaigning. Archives of Public Health, Vol. 76, article 54, 2018, https://doi.org/10.1186/s13690-018-0302-4.

[19] Patrick Stokes, “No, you’re not entitled to your opinion,” The Conversation, 5 October 2012, https://theconversation.com/no-youre-not-entitled-to-your-opinion-9978.

[20] Martin, Vaccination Panic in Australia, 292–304.

[21] W. Lawrence Neuman, Social Research Methods: Qualitative and Quantitative Approaches, seventh edition (Boston, MA: Pearson, 2011), 3–5.

[22] Wayne C. Booth, Gregory G. Colomb, Joseph M. Williams, Joseph Bizup and William T. FitzGerald, The Craft of Research, fourth edition (Chicago: University of Chicago Press, 2016), 272–273.

[23] Malcolm Wright and J. Scott Armstrong, “The ombudsman: verification of citations: fawlty towers of knowledge?” Interfaces, 38 (2), March-April 2008, 125–132.

[24] For a detailed articulation of this approach, see Stephen Hilgartner, “The Sokal affair in context,” Science, Technology, & Human Values, 22(4), Autumn 1997, pp. 506–522. Hilgartner gives numerous citations to expansive interpretations of the significance of the hoax.

[25] See for example Alan D. Sokal, “What the Social Text affair does and does not prove,” in Noretta Koertge (ed.), A House Built on Sand: Exposing Postmodernist Myths about Science (New York: Oxford University Press, 1998), pp. 9–22, at p. 11: “From the mere fact of publication of my parody, I think that not much can be deduced. It doesn’t prove that the whole field of cultural studies, or the cultural studies of science — much less the sociology of science — is nonsense.”

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

Image by Rigoberto Garcia via Flickr / Creative Commons

 

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.’”[1]

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.”[2] 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.”[3]

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.”[4] 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.[5]

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.”[6]

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).[7]

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.”[8] In fact, greater bias was actually found with higher (NAP) and (BOP) levels.[9] 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.[10]

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.”[11]

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.”[12] 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.[13]

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.”[14]

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.”[15] 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.”[16]

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.[17]

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.[18] 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.[19]

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.”[20] 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

References

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.

[1] Glaser, Jack and Knowles, Eric D. (2007). “Implicit Motivation to Control Prejudice,” in Journal of Experimental Social Psychology 44, p. 165.

[2] Glaser, Jack and Knowles, Eric D. (2007), p. 167.

[3] Glaser, Jack and Knowles, Eric D. (2007), p. 170.

[4] Glaser, Jack and Knowles, Eric D. (2007), p. 168.

[5] Glaser, Jack and Knowles, Eric D. (2007), p. 168.

[6] Glaser, Jack and Knowles, Eric D. (2007), p. 169.

[7] 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).

[8] Glaser, Jack and Knowles, Eric D. (2007), p. 169.

[9] Glaser, Jack and Knowles, Eric D. (2007), p. 169.

[10] 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.

[11] Moskowitz, Gordon and Li, Peizhong. (2011). “Egalitarian Goals Trigger Stereotype Inhibition,” in Journal of Experimental Social Psychology 47, p. 106.

[12] 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.

[13] 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

[14] Moskowitz, Gordon and Li, Peizhong (2011), p. 108.

[15] Blair, I. V., Ma, J. E., & Lenton, A. P. (2001), p. 838.

[16] 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.

[17] 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.

[18] 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.

[19] 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.

[20] Alcoff, Linda. (2010). “Epistemic Identities,” in Episteme 7 (2), p. 132.

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): 1-16.

Kamili Posey’s article will be posted over two instalments. The pdf of the article gives specific page references, and includes the entire essay. Shortlink: https://wp.me/p1Bfg0-41m

Image by Walt Stoneburner via Flickr / Creative Commons

 

If you consider the recent philosophical literature on implicit bias research, then you would be forgiven for thinking that the problem of successful interventions into implicit bias fall into the category of things that are resolved. If you consider the recent social psychological literature on interventions into implicit bias, then you would come away with a similar impression. The claim is that implicit bias is epistemically harmful because we profess to believing one thing while our implicit attitudes tell a different story.

Strategy Models and Discrepancy Models

Implicit bias is socially harmful because it maps onto our real-world discriminatory practices, e.g., workplace discrimination, health disparities, racist police shootings, and identity-prejudicial public policies. Consider the results of Greenwald et al.’s (1998) Implicit Association Test. Consider also the results of Correll et. al’s (2002) “Shooter Bias.” If cognitive interventions are possible, and specifically implicit cognitive interventions, then they can help knowers implicitly manage automatic stereotype activation. Do these interventions lead to real-world reductions of bias?

Linda Alcoff (2010) notes that it is difficult to see how implicit, nonvolitional biases (e.g., those at the root of social and epistemic ills like race-based police shootings) can be remedied by explicit epistemic practices.[1] I would follow this by noting that it is equally difficult to see how nonvolitional biases can be remedied by implicit epistemic practices as well.

Jennifer Saul (2017) responds to Alcoff’s (2010) query by pointing to social psychological experiments conducted by Margo Monteith (1993), Jack Glaser and Eric D. Knowles (2007), Gordon B. Moskowitz and Peizhong Li (2011), Saaid A. Mendoza et al. (2010), Irene V. Blair et al. (2001), and Kerry Kawakami et al. (2005).[2] These studies suggest that implicit self-regulation of implicit bias is possible. Saul notes that philosophers with objections like Alcoff’s, and presumably like mine, should “not just to reflect upon the problem from the armchair – at the very least, one should use one’s laptop to explore the internet for effective interventions.”[3]

But I think this recrimination rings rather hollow. How entitled are we to extrapolate from social psychological studies in the manner that Saul advocates? How entitled are we to assumes the epistemic superiority of scientific research on racism, sexism, etc. over the phenomenological reporting of marginalized knowers? Lastly, how entitled are we to claims about the real-world applicability of these study results?[4] My guess is that the devil is in the details. My guess is also that social psychologists have not found the silver bullet for remedying implicit bias. But let’s follow Saul’s suggestion and not just reflect from the armchair.

A caveat: the following analysis is not intended to be an exhaustive or thorough refutation of what is ultimately a large body social psychological literature. Instead, it is intended to cast a bit of doubt on how these models are used by philosophers as successful remedies for implicit bias. It is intended to cast doubt a bit of doubt on the idea that remedies for racist, sexist, homophobic, and transphobic discrimination are merely a training session or reflective exercise away.

This type of thinking devalues the very real experiences of those who live through racism, sexism, homophobia, and transphobia. It devalues how pervasive these experiences are in American society and the myriad ways in which the effects of discrimination seep into marrow of marginalized bodies and marginalized communities. Worse still, it implies that marginalized knowers who claim, “You don’t understand my experiences!” are compelled to contend with the hegemonic role of “Science” that continues to speak over their own voices and about their own lives.[5] But again, back to the studies.

Four Methods of Remedy

I break up the above studies into four intuitive model types: (1) strategy models, (2) discrepancy models, (3) (IAT) models, and (4) egalitarian goal models. (I am not a social scientist, so the operative word here is “intuitive.”) Let’s first consider Kawakami et al. (2005) and Mendoza et al. (2010) as examples of strategy models. Kawakami et al. used Devine and Monteith’s (1993) notion of a negative stereotype as a “bad habit” that a knower needs to “kick” to model strategies that aid in the inhibition of automatic stereotype activation, or the inhibition of “increased cognitive accessibility of characteristics associated with a particular group.”[6]

In a previous study, Kawakami et al. (2000) asked research participants presented with photographs of black individuals and white individuals with stereotypical traits and non-stereotypical traits listed under each photograph to respond “No” to stereotypical traits and “Yes” to non-stereotypical traits.[7] The study found that “participants who were extensively trained to negate racial stereotypes initially also demonstrated stereotype activation, this effect was eliminated by the extensive training.

Furthermore, Kawakami et al. found that practice effects of this type lasted up to 24 h following the training.”[8] Kawakami et al. (2005) used this training model to ground an experiment aimed at strategies for reducing stereotype activation in the preference of men over women for leadership roles in managerial positions. Despite the training, they found that there was “no difference between Nonstereotypic Association Training and No Training conditions…participants were indeed attempting to choose the best candidate overall, in these conditions there was an overall pattern of discrimination against women relative to men in recommended hiring for a managerial position (Glick, 1991; Rudman & Glick, 1999)” [emphasis mine].[9]

Substantive conclusions are difficult to make by a single study but one critical point is how learning occurred in the training but improved stereotype inhibition did not occur. What, exactly, are we to make of this result? Kawakami et al. (2005) claimed that “similar levels of bias in both the Training and No Training conditions implicates the influence of correction processes that limit the effectiveness of training.”[10] That is, they attributed the lack of influence of corrective processes on a variety of contributing factors that limited the effectiveness of the strategy itself.

Notice, however, that this does not implicate the strategy as a failed one. Most notably Kawakami et al. found that “when people have the time and opportunity to control their responses [they] may be strongly shaped by personal values and temporary motivations, strategies aimed at changing the automatic activation of stereotypes will not [necessarily] result in reduced discrimination.”[11]

This suggests that although the strategies failed to reduce stereotype activation they may still be helpful in limited circumstances “when impressions are more deliberative.”[12] One wonders under what conditions such impressions can be more deliberative? More than that, how useful are such limited-condition strategies for dealing with everyday life and every day automatic stereotype activation?

Mendoza et al. (2010) tested the effectiveness of “implementation intentions” as a strategy to reduce the activation or expression of implicit stereotypes using the Shooter Task.[13] They tested both “distraction-inhibiting” implementation intentions and “response-facilitating” implementation intentions. Distraction-inhibiting intentions are strategies “designed to engage inhibitory control,” such as inhibiting the perception of distracting or biasing information, while “response-facilitating” intentions are strategies designed to enhance goal attainment by focusing on specific goal-directed actions.[14]

In the first study, Mendoza et al. asked participants to repeat the on-screen phrase, “If I see a person, then I will ignore his race!” in their heads and then type the phrase into the computer. This resulted in study participants having a reduced number of errors in the Shooter Task. But let’s come back to if and how we might be able to extrapolate from these results. The second study compared a simple-goal strategy with an implementation intention strategy.

Study participants in the simple-goal strategy group were asked to follow the strategy, “I will always shoot a person I see with a gun!” and “I will never shoot a person I see with an object!” Study participants in the implementation intention strategy group were asked to use a conditional, if-then, strategy instead: “If I see a person with an object, then I will not shoot!” Mendoza et al. found that a response-facilitating implementation intention “enhanced controlled processing but did not affect automatic stereotyping processing,” while a distraction-inhibiting implementation intention “was associated with an increase in controlled processing and a decrease in automatic stereotyping processes.”[15]

How to Change Both Action and Thought

Notice that if the goal is to reduce automatic stereotype activation through reflexive control that only a distraction-inhibiting strategy achieved the desired effect. Notice also how the successful use of a distraction-inhibiting strategy may require a type of “non-messy” social environment unachievable outside of a laboratory experiment.[16] Or, as Mendoza et al. (2010) rightly note: “The current findings suggest that the quick interventions typically used in psychological experiments may be more effective in modulating behavioral responses or the temporary accessibility of stereotypes than in undoing highly edified knowledge structures.”[17]

The hope, of course, is that distraction-inhibiting strategies can help dominant knowers reduce automatic stereotype activation and response-facilitated strategies can help dominant knowers internalize controlled processing such that negative bias and stereotyping can be (one day) reflexively controlled as well. But these are only hopes. The only thing that we can rightly conclude from these results is that if we ask a dominant knower to focus on an internal command, they will do so. The result is that the activation of negative bias fails to occur.

This does not mean that the knower has reduced their internalized negative biases and prejudices or that they can continue to act on the internal commands in the future (in fact, subsequent studies reveal the effects are short-lived[18]). As Mendoza et al. also note: “In psychometric terms, these strategies are designed to enhance accuracy without necessarily affecting bias. That is, a person may still have a tendency to associate Black people with violence and thus be more likely to shoot unarmed Blacks than to shoot unarmed Whites.”[19] Despite hope for these strategies, there is very little to support their real-world applicability.

Hunting for Intuitive Hypocrisies

I would extend a similar critique to Margot Monteith’s (1993) discrepancy model. Monteith’s (1993) often cited study uses two experiments to investigate prejudice related discrepancies in the behaviors of low-prejudice (LP) and high-prejudice (HP) individuals and the ability to engage in self-regulated prejudice reduction. In the first experiment, (LP) and (HP) heterosexual study participants were asked to evaluate two law school applications, one for an implied gay applicant and one for an implied heterosexual applicant. Study participants “were led to believe that they had evaluated a gay law school applicant negatively because of his sexual orientation;” they were tricked into a “discrepancy-activated condition” or a condition that was at odds with their believed prejudicial state.[20]

All of the study participants were then told that the applications were identical and that those who had rejected the gay applicant had done so because of the applicant’s sexual orientation. It is important to note that the applicants qualifications were not, in fact, identical. The gay applicant’s application materials were made to look worse than the heterosexual applicant’s materials. This was done to compel the rejection of the applicant.

Study participants were then provided a follow-up questionnaire and essay allegedly written by a professor who wanted to know (a) “why people often have difficulty avoiding negative responses toward gay men,” and (b) “how people can eliminate their negative responses toward gay men.”[21] Researchers asked study participants to record their reactions to the faculty essay and write down as much they could remember about what they read. They were then told about the deception in the experiment and told why such deception was incorporated into the study.

Monteith (1993) found that “low and high prejudiced subjects alike experienced discomfort after violating their personal standards for responding to a gay man, but only low prejudiced subjects experienced negative self-directed affect.”[22] Low prejudiced, (LP), “discrepancy-activated subjects,” also spent more time reading the faculty essay and “showed superior recall for the portion of the essay concerning why prejudice-related discrepancies arise.”[23]

The “discrepancy experience” generated negative self-directed affect, or guilt, for (LP) study participants with the hope that the guilt would (a) “motivate discrepancy reduction (e.g., Rokeach, 1973)” and (b) “serve to establish strong cues for punishment (cf. Gray, 1982).”[24] The idea here is that the experiment results point to the existence of a self-regulatory mechanism that can replace automatic stereotype activation with “belief-based responses;” however, “it is important to note that the initiation of self-regulatory mechanisms is dependent on recognizing and interpreting one’s responses as discrepant from one’s personal beliefs.”[25]

The discrepancy between what one is shown to believe and what one professes to believe (whether real or manufactured, as in the experiment) is aimed at getting knowers to engage in heightened self-focus due to negative self-directed affect. The goal of Monteith’s (1993) study is that self-directed affect would lead to a kind of corrective belief-making process that is both less prejudicial and future-directed.

But if it’s guilt that’s doing the psychological work in these cases, then it’s not clear that knowers wouldn’t find other means of assuaging such feelings. Why wouldn’t it be the case that generating negative self-directed affect would point a knower toward anything they deem necessary to restore a more positive sense of self? To this, Monteith made the following concession:

Steele (1988; Steele & Liu, 1983) contended that restoration of one’s self-image after a discrepancy experience may not entail discrepancy reduction if other opportunities for self-affirmation are available. For example, Steele (1988) suggested that a smoker who wants to quit might spend more time with his or her children to resolve the threat to the self-concept engendered by the psychological inconsistency created by smoking. Similarly, Tesser and Cornell (1991) found that different behaviors appeared to feed into a general “self-evaluation reservoir.” It follows that prejudice-related discrepancy experiences may not facilitate the self-regulation of prejudiced responses if other means to restoring one’s self-regard are available [emphasis mine].[26]

Additionally, she noted that even if individuals are committed to the reducing or “unlearning” automatic stereotyping, they “may become frustrated and disengage from the self-regulatory cycle, abandoning their goal to eliminate prejudice-like responses.”[27] Cognitive exhaustion, or cognitive depletion, can occur after intergroup exchanges as well. This may make it even less likely that a knower will continue to feel guilty, and to use that guilt to inhibit the activation of negative stereotypes when they find themselves struggling cognitively. Conversely, there is also the issue of a kind of lab-based, or experiment-based, cognitive priming. I pick up with this idea along with the final two models of implicit interventions in the next part.

Contact details: Kamili.Posey@kbcc.cuny.edu

References

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.

[1] Alcoff, Linda. (2010). “Epistemic Identities,” in Episteme 7 (2), p. 132.

[2] 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.

[3] Saul, Jennifer (2017), p. 466.

[4] 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.

[5] I owe this critical point in its entirety to the work of Lacey Davidson and her presentation, “When Testimony Isn’t Enough: Implicit Bias Research as Epistemic Injustice” at the Feminist Epistemologies, Methodologies, Metaphysics, and Science Studies (FEMMSS) conference in Corvallis, Oregon in 2018. Davidson notes that the work of philosophers of race and critical race theorists often takes a backseat to the projects of philosophers of social science who engage with the science of racialized attitudes as opposed to the narratives and/or testimonies of those with lived experiences of racism. Davidson describes this as a type of epistemic injustice against philosophers of race and critical race theorists. She also notes that philosophers of race and critical race theorists are often people of color while the philosophers of social science are often white. This dimension of analysis is important but unexplored. Davidson’s work highlights how epistemic injustice operates within the academy to perpetuate systems of racism and oppression under the guise of “good science.” Her arguments was inspired by the work of Jeanine Weekes Schroer on the problematic nature of current research on stereotype threat and implicit bias in “Giving Them Something They Can Feel: On the Strategy of Scientizing the Phenomenology of Race and Racism,” Knowledge Cultures 3(1), 2015.

[6] 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. See also: 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.

[7] Kawakami et al. (2005), p. 69. See also: 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.

[8] Kawakami et al. (2005), p. 69.

[9] Kawakami et al. (2005), p. 73.

[10] Kawakami et al. (2005), p. 73.

[11] Kawakami et al. (2005), p. 74.

[12] Kawakami et al. (2005), p. 74.

[13] The Shooter Task refers to a computer simulation experiment where images of black and white males appear on a screen holding a gun or a non-gun object. Study participants are given a short response time and tasked with pressing a button, or “shooting” armed images versus unarmed images. Psychological studies have revealed a “shooter bias” in the tendency to shoot black, unarmed males more often than unarmed white males. See: 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.

[14] 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..

[15] Mendoza, Saaid, Gollwitzer, Peter, and Amodio, David (2010), p. 520.

[16] A “messy environment” presents additional challenges to studies like the one discussed here. As Kees Keizer, Siegwart Lindenberg, and Linda Steg (2008) claim in “The Spreading of Disorder,” people are more likely to violate social rules when they see that others are violating the rules as well. I can only imagine that this is applicable to epistemic rules as well. I mention this here to suggest that the “cleanliness” of the social environment of social psychological studies such as the one by Mendoza, Saaid, Gollwitzer, Peter, and Amodio, David (2010) presents an additional obstacle in extrapolating the resulting behaviors of research participants to the public-at-large. Short of mass hypnosis, how could the strategies used in these experiments, strategies that are predicated on the noninterference of other destabilizing factors, be meaningfully applied to everyday life? There is a tendency in the philosophical literature on implicit bias and stereotype threat to outright ignore the limited applicability of much of this research in order to make critical claims about interventions into racist, sexist, homophobic, and transphobic behaviors. Philosophers would do well to recognize the complexity of these issues and to be more cautious about the enthusiastic endorsement of experimental results.

[17] Mendoza, Saaid, Gollwitzer, Peter, and Amodio, David (2010), p. 520.

[18] 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.

[19] Mendoza, Saaid, Gollwitzer, Peter, and Amodio, David (2010), p. 520.

[20] 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.

[21] Monteith (1993), p. 474.

[22] Monteith (1993), p. 475.

[23] Monteith (1993), p. 477.

[24] Monteith (1993), p. 477.

[25] Monteith (1993), p. 477.

[26] Monteith (1993), p. 482.

[27] Monteith (1993), p. 483.