Keeping Designers in the Loop: Communicating Inherent Algorithmic Trade-offs Across Multiple Objectives [conference paper]
Conference
ACM Designing Interactive Systems Conference – July 2020
Authors
Bowen Yu (Ph.D. student), Ye Yuan, Loren Terveen (professor), Zhiwei Steven Wu (assistant professor), Jodi L. Forlizzi, Haiyi Zhu (adjunct assistant professor)
Abstract
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.
Link to full paper
Keywords
human computer interaction (HCI), social computing