McKane Andrus is the Research Associate for PAI’s Fairness, Transparency, and Accountability in AI research team. McKane’s current research employs a range of disciplinary lenses to understand the legal and organizational obstacles to using demographic data and explainability tools in algorithmic decision making. He is also helping to build out transparent and accountable methodologies for AI research and deployment.

McKane has spoken and presented research at AIES, 4S, the Code For America Summit, the Center for Human Compatible AI, and the Algorithmic Fairness and Opacity Working Group. He has also done work with AI4ALL, AI Now, Connected Camps, and Digital Democracy.

Prior to joining PAI, McKane completed his M.S. in Human Robot Interaction at UC Berkeley. Before this he earned undergraduate degrees at UC Berkeley with High Honors in Computer Science and Interdisciplinary Studies with concentrations in AI and Technology & Inequality respectively. As a founding member of Graduates for Engaged and Extended Scholarship in computing and Engineering (GEESE), McKane has contributed to organizing and community-building efforts around socially amenable AI. McKane’s work at large attempts to bridge questions of AI, (in)justice, and power, drawing from Science and Technology Studies, Computer Science, Political Economy, Sociology, Philosophy, Law, and Human Computer/Robot Interaction.