Learning from the People: From Normative to Descriptive Solutions to Problems in Security, Privacy & Machine Learning

A variety of experts — computer scientists, policy makers, judges — constantly make decisions about best practices for computational systems. They decide which features are fair to use in a machine learning classifier predicting whether someone will commit a crime, and which security behaviors to recommend and require from end-users. Yet, the best decision is not always clear. Studies have shown that experts often disagree with each other, and, perhaps more importantly, with the people for whom they are making these decisions: the users. This raises a question: Is it possible to learn best-practices directly from the users? The field of moral philosophy suggests yes, through the process of descriptive decision-making, in which we observe people’s preferences from which to infer best practice rather than using experts’ normative (prescriptive) determinations of best practice. In this talk, I will explore the benefits and challenges of applying such a descriptive approach to making computationally-relevant decisions regarding: (i) selecting security prompts for an online system; (ii) determining which features to include in a classifier for jail sentencing; (iii) defining standards for ethical virtual reality content.