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Register a new userSoliciting Stakeholders Fairness Notions in Child Maltreatment Predictive Systems
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Added by
Hao-Fei Cheng
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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Recent work in fair machine learning has proposed dozens of technical definitions of algorithmic fairness and methods for enforcing these definitions. However, we still lack an understanding of how to develop machine learning systems with fairness criteria that reflect relevant stakeholders nuanced viewpoints in real-world contexts. To address this gap, we propose a framework for eliciting stakeholders subjective fairness notions. Combining a user interface that allows stakeholders to examine the data and the algorithms predictions with an interview protocol to probe stakeholders thoughts while they are interacting with the interface, we can identify stakeholders fairness beliefs and principles. We conduct a user study to evaluate our framework in the setting of a child maltreatment predictive system. Our evaluations show that the framework allows stakeholders to comprehensively convey their fairness viewpoints. We also discuss how our results can inform the design of predictive systems.
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We propose a method to test for the presence of differential ascertainment in case-control studies, when data are collected by multiple sources. We show that, when differential ascertainment is present, the use of only the observed cases leads to severe bias in the computation of the odds ratio. We can alleviate the effect of such bias using the estimates that our method of testing for differential ascertainment naturally provides. We apply it to a dataset obtained from the National Violent Death Reporting System, with the goal of checking for the presence of differential ascertainment by race in the count of deaths caused by child maltreatment.
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