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Engineering Equity: How AI Can Help Reduce the Harm of Implicit Bias

Abstract

This paper focuses on the potential of “equitech”—AI technology that improves equity. Recently, interventions have been developed to reduce the harm of implicit bias, the automatic form of stereotype or prejudice that contributes to injustice. However, these interventions—some of which are assisted by AI-related technology—have significant limitations, including unintended negative consequences and general inefficacy. To overcome these limitations, we propose a two-dimensional framework to assess current AI-assisted interventions and explore promising new ones. We begin by using the case of human resource recruitment as a focal point to show that existing approaches have exploited only a subset of the available solution space. We then demonstrate how our framework facilitates the discovery of new approaches. The first dimension of this framework helps us systematically consider the analytic information, intervention implementation, and modes of human-machine interaction made available by advancements in AI-related technology. The second dimension enables the identification and incorporation of insights from recent research on implicit bias intervention. We argue that a design strategy that combines complementary interventions can further enhance the effectiveness of interventions by targeting the various interacting cognitive systems that underlie implicit bias. We end with a discussion of how our cognitive interventions framework can have positive downstream effects for structural problems.

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Notes

  1. In this paper, we endorse the widespread view that implicit bias is a mental construct (e.g., an association, attitude, or internal structure) that causes behaviors. However, this view is not unanimously held; for instance, De Houwer (2019) proposes to take implicit bias as a behavioral phenomenon—specifically, behavior that is automatically influenced by cues that function as an indicator of the social group to which one belongs.

  2. There is some disagreement concerning how best to draw the distinction between implicit and explicit attitudes in philosophy and psychology (Brownstein 2018). In the case of implicit and explicit bias, one common way of operationalizing the distinction in scientific practice is to associate them with implicit and explicit measures, respectively. In explicit measures, subjects are asked to report their attitudes in the test, while in implicit measures, their attitudes are inferred from other behaviors (Brownstein 2019). The disagreement will not be the focus of this paper, as we believe it will not affect the arguments of this paper.

  3. See Devine et al. (2012) for a more optimistic result that shows in-person, long-term debiasing can have effects for extended periods of time. However, Forscher et al. (2017) failed to fully replicate the study.

  4. Our paper focuses on AI-assisted intervention on implicit bias rather than on bias in general. Implicit and explicit biases are distinct scientific constructs, and their relation remains a topic of controversy. In addition, it is unclear whether findings in one field can be generalized to the other. For example, a recent study (Forscher et al. 2019) suggests that effective interventions on implicit bias may not always change explicit bias. Finally, implicit and explicit biases bring about different reactive attitudes. For instance, it has been shown that discrimination is considered less blameworthy when it is caused by implicit bias instead of explicit bias (Daumeyer et al. 2019). As a result, we will restrict our discussion to interventions on implicit bias to avoid complicating the discussion. However, the framework we develop in this paper can be adapted to explore intervention on explicit bias.

  5. For example, Hung and Yen (2020) extract five general principles for protecting basic human rights, including data integrity for reducing bias and inaccuracy through the examination of over 115 principles recently proposed by academies, governments, and NGOs.

  6. KBSs are computer programs that generate information to help humans solve problems or generate solutions. AI has played an important role in enhancing the capacity of KBSs by powering knowledge acquisition, representation, and reasoning. In this paper, the use of this term is adopted from the discipline of analytics (Sharda et al. 2020). It is different from the knowledge-based system in AI which represents knowledge and performs inferences explicitly.

  7. Microaggressions are “brief and commonplace daily verbal, behavioral, or environmental indignities, whether intentional or unintentional, that communicate hostile, derogatory, or negative racial slights and insults toward people of [underrepresented groups]” (Sue et al. 2007, p. 271). Examples include talking over interviewees with a particular demographic background and insensitive comments demeaning interviewee’s heritage or identity.

  8. Currently, fair proxy communication and interview in virtual space are not products; they are only proposed ideas (Seibt and Vestergaard 2018; Skewes et al. 2019)

  9. Determining which information should be masked to reduce implicit bias is difficult, and the determination needs to be made on a case-by-case basis. In the information technology (IT) industry, for example, the assessment of purely professional skills may be distinguished from other traits related to interpersonal skills (e.g., personality and coordination skills) that may not be essential to the job. So, when evaluating an applicant’s coding skills, demographic cues are irrelevant and should be masked. Conversely, in other industries (e.g., insurance sales), masking demographic information could be a loss when assessing the applicant’s communication styles that may be essential to the job performance.

  10. Nonetheless, if the robot is too natural, it may trigger the uncanny valley effect—humanoid robots may elicit unintended cold, eerie feelings in human viewers (Mori 1970; MacDorman and Chattopadhyay 2016).

  11. Another example of how AI can help predict human biases is by using ML to detect biases expressed in ordinary language. Caliskan et al. (2017) developed Word-Embedding Association Test (WEAT)—a method of measuring the associations between words. Their model, trained on a corpus of text from the internet, succeeded in replicating the known biases revealed by the Implicit Association Test (e.g., male or female names are associated with career or family respectively). As a result, WEAT can potentially be developed to identify an individual’s implicit bias through analyzing the text she produces.

  12. A possible solution to this attendant harm focuses on reducing the implicit bias of interviewers. Since AI detects bias, it can also be programmed to alert the interviewers for correction while masking the biased expressions to the interviewees. The detection record can be used by senior managers to choose better interviewers.

  13. However, we should not think of the three types of cognition-based interventions as a final and unrevisable category of cognition-based intervention. This is because as our knowledge about the mechanisms of implicit bias grows, new types of cognition-based intervention may become available.

  14. However, we need to be careful of the unforeseen ethical consequences of interventions (such as those involving VR). For example, Madary and Metzinger (2016) point out that VR can induce illusions of embodiment and change one’s long-term psychological states. Risky content and privacy are critical issues too. Therefore, they offer a list of ethical recommendations as a framework for future study. While there will always be unforeseeable risks involved in new technology, such research will help us minimize it.

  15. The interventions proposed in this paper are generally based on currently available AI and AI-related technologies; however, their advancement relies on the development of AI research in some domains. In particular, predictive interventions face the challenge of modeling and predicting the behavior of an individual accurately; on top of that, prescriptive interventions, in order to suggest decisions to its user, require a causal model, which represents how the intervention leads to results for a particular user (Albrecht and Stone 2018; Sheridan 2016). Finally, we need empirical research to validate the effectiveness of the specific implementation of these interventions.

  16. According to Miller (2018), responsibility is about the ability to fulfill a duty, and accountability is about the liability to respond to one’s performance of duties. Accountability presumes responsibility, but is not identical with it. Please see Miller (2018) for further distinction of the two notions.

  17. Engelen and Nys (2020) propose the concept of perimeters of autonomy, according to which changes in an agent’s options within the perimeters can occur without precluding his autonomy because he still has a range of options to choose from. Nonetheless, there may be an issue about how to draw the perimeters.

  18. The complex interaction between cognitive and structural factors can have unpredictable consequences. It is exemplified in the change of implicit and explicit antigay bias before and after same-sex marriage legalization. Ofosu et al. (2019) found that implicit and explicit antigay bias decreased before the legalization of same-sex marriage. Nevertheless, the change of attitude following legalization differs depending on whether the legalization was passed locally: a deeper decrease was found if the legalization was passed locally, whereas an increase following federal legalization in states that never passed local legalization. However, note that Tankard and Paluck (2017) found that federal legalization led individuals to change their perceptions of social norms regarding gay marriage, but not their personal attitudes.

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Acknowledgements

For helpful discussions and feedback on earlier drafts of this work, thanks to Michael S. Brownstein, Acer Chang, Caitrin Donovan, Ivan Gonzalez-Cabrera, Julia Haas, Richard Heersmink, Bryce Huebner, Calvin Lai, Eric Schwitzgebel, Jacob Sparks, and two anonymous referees.

Funding

This work is supported in part by an Academia Sinica Fellowship to Dr. Linus Ta-Lun Huang, sponsored by Academia Sinica, Taiwan. This research is also funded in part by the Ministry of Science and Technology Taiwan to Dr. Tzu-wei Hung (MOST 107-2410-H-001-101-MY3).

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Lin, YT., Hung, TW. & Huang, L.TL. Engineering Equity: How AI Can Help Reduce the Harm of Implicit Bias. Philos. Technol. 34, 65–90 (2021). https://doi.org/10.1007/s13347-020-00406-7

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Keywords

  • Implicit bias
  • Decision support
  • Augmented decision
  • Fairness
  • AI4SG
  • Artificial intelligence