Journal article

Crowdsourcing Perceptions of Fair Predictors for Machine Learning: A Recidivism Case Study

Niels van Berkel, Jorge Goncalves, Danula Hettiachchi, Senuri Wijenayake, Ryan Kelly, Vassilis Kostakos

Proceedings of the ACM on Human-Computer Interaction | Association for Computing Machinery | Published : 2019


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..

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