Institutions Promoting or Countering Deliberate Ignorance

Teichman, Doron; Talley, Erich; Egidy, Stefanie; Engel, Christoph; Gummadi, Krishna P.; Hagel, Kristin; Lewandowski, Stephan; MacCoun, Robert J.; Utz, Sonja; Zamir, Eyal

doi:10.7551/mitpress/13757.003.0022

Cited by 4 publications

(6 citation statements)

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“…4,5 Further, machine-driven practices are often used in conjunction with nonblind human evaluations. [6][7][8] Blinding may be uncommon in institutional hiring in part because the jobs of most organizations vary widely in qualifications and duties. As a result, HR professionals may be concerned that uniform rules may not be appropriate in all cases.…”

Section: Encouraging Selfblinding In Hiringmentioning

confidence: 99%

Encouraging self-blinding in hiring

Fath,

Larrick,

Soll

2023

Behavioral Science & Policy

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One strategy for minimizing bias in hiring is blinding—purposefully limiting the information used when screening applicants to that which is directly relevant to the job and does not elicit bias based on race, gender, age, or other irrelevant characteristics. Blinding policies remain rare, however. An alternative to blinding policies is self-blinding, in which people performing hiring-related evaluations blind themselves to biasing information about applicants. Using a mock-hiring task, we tested ways to encourage self-blinding that take into consideration three variables likely to affect whether people self-blind: default effects on choices, people’s inability to assess their susceptibility to bias, and people’s tendency not to recognize the full range of information that can elicit that bias. Participants with hiring experience chose to receive or be blind to various pieces of information about applicants, some of which were potentially biasing. They selected potentially biasing information less often when asked to specify the applicant information they wanted to receive than when asked to specify the information they did not want to receive, when prescribing selections for other people than when making the selections for themselves, and when the information was obviously biasing than when it was less obviously so. On the basis of these findings, we propose a multipronged strategy that human resources leaders could use to enable and encourage hiring managers to self-blind when screening job applicants.

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Section: Encouraging Selfblinding In Hiringmentioning

confidence: 99%

Encouraging self-blinding in hiring

Fath,

Larrick,

Soll

2023

Behavioral Science & Policy

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“…It is not only individuals who can blind themselves to information that may introduce bias-collectives and institutions can do so as well (Teichman et al, 2021). One well known example is the practice of blind auditions in orchestras.…”

Section: Deliberate Ignorance As a Safeguard Of Fairnessmentioning

confidence: 99%

“…Nevertheless, there are many situations and settings in which individual, collective, and institutional decisions are likely to turn out better, fairer, or more equitable when potentially biasing information does not reach the information processing systems (Teichman et al, 2021)-including refusing to view one's Stasi file and blind auditions in orchestras. Other examples include the use of blinding in scientific data analysis, scientific peer review, and job applicant screening (MacCoun, 2021) or biomedical science, forensic science, and law (Robertson & Kesselheim, 2016).…”

Section: Deliberate Ignorance As a Safeguard Of Fairnessmentioning

confidence: 99%

“…Even if we humans may not be aware of-or are unable to control-implicit biases, we can tell algorithms to not consider traits, attributes, and cues that we know might bias us (e.g., name, gender, photos, ethnicity, age). That is, we can blind algorithms to protected attributes as an act of deliberate ignorance (Teichman et al, 2021). Algorithms thus seem to be in a much better position than humans to implement an actual veil of ignorance, thereby turning a thought experiment into reality and hopefully producing a juster world.…”

Section: Replacing Humans With Blinded Algorithms To Circumvent Human...mentioning

confidence: 99%

“…We now consider the opposite situation: How can help algorithms help people blind themselves against unwanted information in order to improve the accuracy or fairness of their judgments? There are ample examples of how people have used algorithms in their most general form-detailed, step-by-step instructions-to blind themselves or others to unwanted information (MacCoun, 2021;Robertson & Kesselheim, 2016;Teichman et al, 2021). We have already mentioned some examples of deliberate ignorance, including blind auditions in orchestras (Goldin & Rouse, 2000) and blinding in scientific data analysis, scientific peer review, and job applicant screening (MacCoun, 2021).…”

Section: Switching Roles: Using Algorithms To Blind Peoplementioning

confidence: 99%

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Blinding to circumvent human biases: Deliberate ignorance in humans, institutions, and machines

Hertwig¹,

Kozyreva²,

Herzog³

2022

Preprint

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Persistent inequalities and injustices are a blight on modern liberal societies. Examples abound, from the gender gap in pay to sentencing disparities between Black, Hispanic, and White defendants to allocation disparities in medical resources between Black and White patients. One cause of these and other inequalities is implicit social biases. In a process thought to be outside conscious control, human cognition is assumed to make associations between social groups and attributions such as "nurturing," "lazy," or "uneducated." Such associations can result in explicit unequal treatment. In theory, one way to circumvent implicit but, of course, also explicit biases is to delegate important decisions—for instance, on allocating benefits, resources, or opportunities—to algorithms, which are assumed to be free of human biases. However, evidence shows that algorithms can perpetuate and even amplify existing inequalities and injustices. We discuss how a philosophical thought experiment, Rawls's "veil of ignorance," and a psychological phenomenon, "deliberate ignorance" (the choice to not know), can shield people, institutions, and algorithms from biases. We explore the advantages and ways of blinding human and artificial decision makers to information that could result in biases and consider the practical problems of successfully blinding algorithms.

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