Crowdsourcing Perceptions of Fair Predictors for Machine Learning
Crowdsourcing Perceptions of Fair Predictors for Machine Learning
复制标题
众包对机器学习公平预测的看法
DOI:
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发表时间:
2019
期刊:
影响因子:
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通讯作者:
V. Kostakos
中科院分区:
文献类型:
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作者:
Niels van Berkel;Jorge Gonçalves;Danula Hettiachchi;S. Wijenayake;Ryan M. Kelly;V. Kostakos
The increased reliance on algorithmic decision-making in socially impactful processes has intensified the calls for algorithms that are unbiased and procedurally fair. Identifying fair predictors is an essential step in the construction of equitable algorithms, but the lack of ground-truth in fair predictor selection makes this a challenging task. In our study, we recruit 90 crowdworkers to judge the inclusion of various predictors for recidivism. We divide participants across three conditions with varying group composition. Our results show that participants were able to make informed decisions on predictor selection. We find that agreement with the majority vote is higher when participants are part of a more diverse group. The presented workflow, which provides a scalable and practical approach to reach a diverse audience, allows researchers to capture participants' perceptions of fairness in private while simultaneously allowing for structured participant discussion.
DOI:
10.1145/3025453.3025884
发表时间:
2017
期刊:
Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems
影响因子:
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作者:
Lee, Min Kyung;Kim, Ji Tae;Lizarondo, Leah
通讯作者:
Lizarondo, Leah
DOI:
10.1609/aaai.v32i1.11512
发表时间:
2017-09
期刊:
ArXiv
影响因子:
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作者:
Ritesh Noothigattu;Snehalkumar `Neil' Gaikwad;E. Awad;Sohan Dsouza;Iyad Rahwan;Pradeep Ravikumar;Ariel D. Procaccia
通讯作者:
Ritesh Noothigattu;Snehalkumar `Neil' Gaikwad;E. Awad;Sohan Dsouza;Iyad Rahwan;Pradeep Ravikumar;Ariel D. Procaccia