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Recent work in fair machine learning has proposed dozens of technicaldefinitions of algorithmic fairness and methods for enforcing thesedefinitions. However, we still lack an understanding of how to develop machinelearning systems with fairness criteria that reflect relevant stakeholders'nuanced viewpoints in real-world contexts. To address this gap, we propose aframework for eliciting stakeholders' subjective fairness notions. Combining auser interface that allows stakeholders to examine the data and the algorithm'spredictions with an interview protocol to probe stakeholders' thoughts whilethey are interacting with the interface, we can identify stakeholders' fairnessbeliefs and principles. We conduct a user study to evaluate our framework inthe setting of a child maltreatment predictive system. Our evaluations showthat the framework allows stakeholders to comprehensively convey their fairnessviewpoints. We also discuss how our results can inform the design of predictivesystems.

Soliciting Stakeholders' Fairness Notions in Child Maltreatment Predictive Systems