Picking on the Same Person: The Ethics of Algorithmic Monoculture
Kathleen’s research addresses political, ethical, and epistemic problems generated by the use of machine learning in science and society. She received her Ph.D. in the History and Philosophy of Science from the University of Pittsburgh. Her dissertation examined the role of machine learning in scientific concept formation. She argued for a pragmatist approach to algorithmic transparency, explainable AI, and identity. Kathleen’s current research in moral and political philosophy is concerned with problems arising in non-state use of automated decision-making, such as in hiring and loan-approval algorithms, and with the value of transparent machine learning for a democratic society. Kathleen’s interdisciplinary ethics fellowship is in partnership with the Stanford Institute for Human-Centered Artificial Intelligence. She is working with the Computer Science department to develop Stanford's first Embedded EthiCS curriculum.