Keeping Designers in the Loop: Communicating Inherent Algorithmic Trade-offs Across Multiple Objectives
Keeping Designers in the Loop: Communicating Inherent Algorithmic Trade-offs Across Multiple Objectives
复制标题
让设计师了解情况:沟通跨多个目标的固有算法权衡
DOI:
10.1145/3357236.3395528
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发表时间:
2020
期刊:
影响因子:
--
通讯作者:
Zhu, Haiyi
中科院分区:
文献类型:
--
作者:
Yu, Bowen;Yuan, Ye;Terveen, Loren;Wu, Zhiwei Steven;Forlizzi, Jodi;Zhu, Haiyi
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.
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发表时间:
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期刊:
Proceedings of the 2018 Designing Interactive Systems Conference
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