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Title: Keeping Designers in the Loop: Communicating Inherent Algorithmic Trade-offs Across Multiple Objectives
Artificial intelligence algorithms have been used to enhance a wide variety of products and services, including assisting human decision making in high-stake contexts. However, these algorithms are complex and have trade-offs, notably between prediction accuracy and fairness to population subgroups. This makes it hard for designers to understand algorithms and design products or services in a way that respects users' goals, values, and needs. We proposed a method to help designers and users explore algorithms, visualize their trade-offs, and select algorithms with trade-offs consistent with their goals and needs. We evaluated our method on the problem of predicting criminal defendants' likelihood to re-offend through (i) a large-scale Amazon Mechanical Turk experiment, and (ii) in-depth interviews with domain experts. Our evaluations show that our method can help designers and users of these systems better understand and navigate algorithmic trade-offs. This paper contributes a new way of providing designers the ability to understand and control the outcomes of algorithmic systems they are creating.
Authors:
; ; ; ; ;
Award ID(s):
2001851 2000782
Publication Date:
NSF-PAR ID:
10178837
Journal Name:
Proceedings of the 2020 ACM Designing Interactive Systems Conference
Page Range or eLocation-ID:
1245 to 1257
Sponsoring Org:
National Science Foundation
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