Data Science Research Ethics and the Challenges of Inference, Public Data and Consent
Data Ethics in Research Seminar Series more information...
Dr. Jake Metcalf, Director of the AI on the Ground Initiative at the Data & Society Research Institute
Data science, and the related disciplines of machine learning and artificial intelligence, are founded on the assumed availability of massive amounts of data. The scientific and economic justification for collecting and using all that data is deceptively simple: we can infer expensive- and hard-to-know data from cheap- and easy-to-know data and make predictions and automated decisions on the basis of the patterns we find. When that data is about human behavior, that inferential step is ethically fraught because it often involves data that is ubiquitous (social media, geolocation, biometrics, etc.) being used to predict traits that are from an entirely different context (race, religion, sexual preference, gender, etc.), and typically without knowledge or consent. This is a highly complex ethical challenge, yet our research ethics norms and regulations were written for a different paradigm of scientific research. In this talk, I will illustrate this dynamic with several cases of data science research ethics controversies and consider how we might establish new practices for ethical research.
Co-Sponsored by the Center for Statistical Training and Consulting (CSTAT) and MSU Libraries