The inaugural conference AI & Gender-Based Harms: Implications for Policy and Practice was a collaboration on all fronts aimed at addressing a growing societal problem. With an interdisciplinary roster of researchers, clinicians, advocates, policymakers, and practitioners, the event examined the impact of AI-facilitated gender-based harms and response policies.

In February, Data Fluency Certificate undergraduate students Sima Raha, Nazifa Qazizada, and Computational Biology undergraduate Hannah Beakley attended the WECode Conference at Harvard University, learning from tech industry leaders and connecting with fellow students.

Held in Toronto, Canada, last month, the IEEE Conference on Secure and Trustworthy Machine Learning (IEEE SATML) focuses on expanding on the theoretical and practical understandings of vulnerabilities inherent to ML systems, exploring the robustness of ML algorithms and systems, and aiding in developing a unified, coherent scientific community which aims to build trustworthy ML systems. The event’s organizers recognized only two papers with their Distinguished Paper Award, and new research by Brown CS PhD student Victor Ojewale was one of them.

DSI's Director of Graduate Studies, Andras Zsom, led a workshop called "Introduction to Interpretability in Machine Learning."