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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.
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 sev
ML-based predictive systems are increasingly used to support decisions with a critical impact on individuals lives such as college admission, job hiring, child custody, criminal risk assessment, etc. As a result, fairness emerged as an important requ
Addressing the problem of fairness is crucial to safely use machine learning algorithms to support decisions with a critical impact on peoples lives such as job hiring, child maltreatment, disease diagnosis, loan granting, etc. Several notions of fai
To ensure accountability and mitigate harm, it is critical that diverse stakeholders can interrogate black-box automated systems and find information that is understandable, relevant, and useful to them. In this paper, we eschew prior expertise- and
Today, children are increasingly connected to the Internet and consume content and services through various means. It has been a challenge for less tech-savvy parents to protect children from harmful content and services. Internet of Things (IoT) has