Fair Machine Guidance to Enhance Fair Decision Making in Biased People

Yang, Mingzhe; Arai, Hiromi; Yamashita, Naomi; Baba, Yukino

doi:10.1145/3613904.3642627

Proceedings of the CHI Conference on Human Factors in Computing Systems 2024

DOI: 10.1145/3613904.3642627

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Fair Machine Guidance to Enhance Fair Decision Making in Biased People

Mingzhe Yang,

Hiromi Arai,

Naomi Yamashita

et al.

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Cited by 1 publication

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(De)Noise: Moderating the Inconsistency Between Human Decision-Makers

Grgić-Hlača,

Ali,

Gummadi

et al. 2024

Proc. ACM Hum.-Comput. Interact.

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Prior research in psychology has found that people's decisions are often inconsistent. An individual's decisions vary across time, and decisions vary even more across people. Inconsistencies have been identified not only in subjective matters, like matters of taste, but also in settings one might expect to be more objective, such as sentencing, job performance evaluations, or real estate appraisals. In our study, we explore whether algorithmic decision aids can be used to moderate the degree of inconsistency in human decision-making in the context of real estate appraisal. In a large-scale human-subject experiment, we study how different forms of algorithmic assistance influence the way that people review and update their estimates of real estate prices. We find that both (i) asking respondents to review their estimates in a series of algorithmically chosen pairwise comparisons and (ii) providing respondents with traditional machine advice are effective strategies for influencing human responses. Compared to simply reviewing initial estimates one by one, the aforementioned strategies lead to (i) a higher propensity to update initial estimates, (ii) a higher accuracy of post-review estimates, and (iii) a higher degree of consistency between the post-review estimates of different respondents. While these effects are more pronounced with traditional machine advice, the approach of reviewing algorithmically chosen pairs can be implemented in a wider range of settings, since it does not require access to ground truth data.

show abstract

(De)Noise: Moderating the Inconsistency Between Human Decision-Makers

Grgić-Hlača,

Ali,

Gummadi

et al. 2024

Proc. ACM Hum.-Comput. Interact.

View full text Add to dashboard Cite

show abstract

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Fair Machine Guidance to Enhance Fair Decision Making in Biased People

Cited by 1 publication

References 82 publications

(De)Noise: Moderating the Inconsistency Between Human Decision-Makers

(De)Noise: Moderating the Inconsistency Between Human Decision-Makers

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