What Implicit Measures of Bias Can Do

Melnikoff, David; Kurdi, Benedek

doi:10.1080/1047840x.2022.2106759

Cited by 8 publications

(7 citation statements)

References 78 publications

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“…Participants’ implicit bias was measured using the standard IAT D score 33 ; positive D scores indicate that participants are faster at associating women with liberal arts and men with science. We acknowledge important continuing debate about the reliability of the IAT 38 – 40 . Our specific choice of IAT is supported by (1) prior work demonstrating its stable results across decades 34 and (2) a separate preregistered study we conducted that yielded consistent results with a similar design ( Methods ).…”

Section: Testing Psychological Effects Of Imagesmentioning

confidence: 99%

Online images amplify gender bias

Guilbeault,

Delecourt,

Hull

et al. 2024

Nature

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Each year, people spend less time reading and more time viewing images1, which are proliferating online2–4. Images from platforms such as Google and Wikipedia are downloaded by millions every day2,5,6, and millions more are interacting through social media, such as Instagram and TikTok, that primarily consist of exchanging visual content. In parallel, news agencies and digital advertisers are increasingly capturing attention online through the use of images7,8, which people process more quickly, implicitly and memorably than text9–12. Here we show that the rise of images online significantly exacerbates gender bias, both in its statistical prevalence and its psychological impact. We examine the gender associations of 3,495 social categories (such as ‘nurse’ or ‘banker’) in more than one million images from Google, Wikipedia and Internet Movie Database (IMDb), and in billions of words from these platforms. We find that gender bias is consistently more prevalent in images than text for both female- and male-typed categories. We also show that the documented underrepresentation of women online13–18 is substantially worse in images than in text, public opinion and US census data. Finally, we conducted a nationally representative, preregistered experiment that shows that googling for images rather than textual descriptions of occupations amplifies gender bias in participants’ beliefs. Addressing the societal effect of this large-scale shift towards visual communication will be essential for developing a fair and inclusive future for the internet.

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Section: Testing Psychological Effects Of Imagesmentioning

confidence: 99%

Online images amplify gender bias

Guilbeault,

Delecourt,

Hull

et al. 2024

Nature

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“…Further, it is important for researchers to state in what way they mean that something is implicit (Corneille & Hütter, 2020). Recently, a consensus appears to be building that the conceptual focus of implicitness is more usefully organized around intentionality rather than awareness which, arguably, was the pri-mary focus for the first two decades of research using the IAT (see De Houwer & Boddez, 2022;Dovidio & Kunst, 2022;Krajbich, 2022;Melnikoff & Kurdi, 2022;Olson & Gill, 2022;K. A. Ratliff & Smith, 2022).…”

Section: Implications Of Gsr-ddm For the Iat's "Implicitness"mentioning

confidence: 99%

Improving the reliability and validity of the IAT with a dynamic model driven by similarity

et al. 2023

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The Implicit Association Test (IAT), like many behavioral measures, seeks to quantify meaningful individual differences in cognitive processes that are difficult to assess with approaches like self-reports. However, much like other behavioral measures, many IATs appear to show low test-retest reliability and typical scoring methods fail to quantify all of the decision-making processes that generate the overt task performance. Here, we develop a new modeling approach for IATs based on the geometric similarity representation (GSR) model. This model leverages both response times and accuracy on IATs to make inferences about representational similarity between the stimuli and categories. The model disentangles processes related to response caution, stimulus encoding, similarities between concepts and categories, and response processes unrelated to the choice itself. This approach to analyzing IAT data illustrates that the unreliability in IATs is almost entirely attributable to the methods used to analyze data from the task: GSR model parameters show test-retest reliability around .80-.90, on par with reliable selfreport measures. Furthermore, we demonstrate how model parameters result in greater validity compared to the IAT D-score, Quad model, and simple diffusion model contrasts, predicting outcomes related to intergroup contact and motivation. Finally, we present a simple point-andclick software tool for fitting the model, which uses a pre-trained neural network to estimate best-fit parameters of the GSR model. This approach allows easy and instantaneous fitting of IAT data with minimal demands on coding or technical expertise on the part of the user, making the new model accessible and effective.

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“…As noted throughout this paper, the ongoing (and, generally, unexamined) assumption that a participant's responses on the IAT map directly onto something "implicit" has led us into conceptual confusion. Recently, researchers have taken this much more seriously and argued explicitly that, for example, behavior on the IAT is an instance of biased behavior rather than an indicator of bias (De Houwer & Boddez, 2022;Gawronski et al, 2022;Krajbich, 2022;Melnikoff & Kurdi, 2022;Olson & Gill, 2022;K. A. Ratliff & Smith, 2022).…”

Section: Implications Of Gsr-ddm For the Iat's "Implicitness"mentioning

confidence: 99%

“…A. Ratliff & Smith, 2022). Further, a consensus appears to be building that the conceptual focus of "implicitness" is more usefully organized around intentionality than awareness (see De Houwer & Boddez, 2022;Dovidio & Kunst, 2022;Krajbich, 2022;Melnikoff & Kurdi, 2022;Olson & Gill, 2022;K. A. Ratliff & Smith, 2022).…”

Section: Implications Of Gsr-ddm For the Iat's "Implicitness"mentioning

confidence: 99%

Improving the reliability and validity of the IAT with a dynamic model driven by similarity

Kvam¹,

Smith²,

Irving³

et al. 2022

Preprint

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The Implicit Association Test [IAT], like many behavioral measures, seeks to quantify meaningful individual differences in cognitive processes that are difficult to assess with approaches like self-reports. However, much like other behavioral measures, the IAT appears to show low test-retest reliability and typical scoring methods fail to quantify all of the decision-making processes that generate the overt task performance. Here, we develop a new modeling approach for the IAT, the CAVEAT model, that leverages both response times and accuracy on the task to make inferences about representational similarity between the stimuli and categories, as in computational linguistic models of representation. The model disentangles processes related to cognitive control, stimulus encoding, associations between concepts and categories, and processes unrelated to the choice itself. This approach to analyzing IAT data illustrates that the unreliability in the IAT is almost entirely attributable to the methods used to analyze data from the task: the model parameters show test-retest reliability around .8-.9, on par with that of many of the most reliable self-report measures. Furthermore, we demonstrate how model parameters are better and more unbiased compared to the IAT D-score in predicting outcomes related to intergroup contact and motivation. Put together, the model provides much greater reliability, discriminant and predictive validity, and the ability to make inferences about processes like associations and response caution that are not otherwise possible. We conclude by reviewing new, model-based insights about the IAT related to awareness, strategic caution, faking, and the role of associations in decision-making.

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What Implicit Measures of Bias Can Do

Abstract: Benedek Kurdi is a member of the Scientific Advisory Board at Project Implicit, a 501(c)(3) non-profit organization and international collaborative of researchers who are interested in implicit social cognition.

Cited by 8 publications

References 78 publications

Online images amplify gender bias

Online images amplify gender bias

Improving the reliability and validity of the IAT with a dynamic model driven by similarity

Improving the reliability and validity of the IAT with a dynamic model driven by similarity

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