"Toward Secure and Trustworthy Wireless Sensing:" Wireless sensing uses radio signals to perceive physical properties of people and their environments, enabling applications such as remote health monitoring, fall detection, and elderly care without the intrusiveness of cameras. But the apparent privacy advantage of radio no longer holds: the same signals readily leak sensitive biometric information such as breathing, heart rate, and gait, and the problem deepens when these systems fail to respect appropriate information flows. — Tanvir Ahmed

"The Structure and Interpretation of Socially Embedded Algorithmic Outputs": Applying AI algorithms to social settings requires translating model outputs into scientific findings, real-world decisions, or moral obligations. These complex social conclusions involve considerations outside of the traditional machine learning framework. In this talk, I use empirical data science, economic theory, and queer philosophy to navigate tradeoffs when estimating population-level race statistics, modeling admissions processes, and inferring missing gender data for algorithmic fairness. — Evan Dong

The current boom in AI has been accompanied with tremendous hype, both negative and positive. My goal is to go beneath this surface and provide a better understanding of what AI systems are actually doing, and what concerns I have about where they are going. I am neither an enthusiast nor a doomer. The very real problems created by AI today and in the foreseeable future need to be approached by looking at the ways we (in the broad sense) choose to apply it and the way in which we fit it into our world.