Keeping Human Concerns in the Loop: Human-Centered Approaches to Responsible AI

Amidst rhetoric for the transformative power of machine learning and AI, significant evidence suggests that such systems might reproduce or amplify existing societal biases, in high-stakes sectors such as education, health, and criminal justice. In this talk, we will discuss how human-centered design methods may support developing more fair and responsible AI systems. This talk will focus on approaches to co-designing AI systems with stakeholders, and will share how emerging design processes and tools might support data scientists and ML engineers in identifying and mitigating issues of unfairness throughout the AI design lifecycle.  

Bio: Michael is a postdoctoral researcher at Microsoft Research working with the FATE (Fairness, Accountability, Transparency, and Ethics in AI) research group. He works at the intersection of human-computer interaction and AI/ML, focusing on design processes to enable more fair and responsible AI. Michael received his PhD in Human-Computer Interaction from Carnegie Mellon University, where he was a PIER fellow funded by the Institute for Education Sciences and a Siebel Scholar.