Discover and read the best of Twitter Threads about #trustworthyml
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Our group @ai4life_harvard is gearing up for showcasing our recent research and connecting with the #ML #TrustworthyML #XAI community at #NeurIPS2022. Hereβs where you can find us at a glance. More details about our papers/talks/panels in the thread below π [1/N] 
@ai4life_harvard [Conference Paper] Which Explanation Should I Choose? A Function Approximation Perspective to Characterizing Post Hoc Explanations (joint work with #TessaHan and @Suuraj) -- arxiv.org/abs/2206.01254. More details in this thread [2/N]
[Conference Paper] Efficient Training of Low-Curvature Neural Networks (joint work with @Suuraj, #KyleMatoba, @francoisfleuret) -- arxiv.org/abs/2206.07144. More details in this thread [3/N]
Excited to share this. I recently chatted with @twimlai podcast about my work on exposing vulnerabilities of explanation methods & how these methods may mislead end-users into trusting biased models. Thank you so much for the amazing chat, @samcharrington! #trustworthyml
The work discussed in this podcast was done in collaboration with some amazing students and researchers: Osbert Bastani, @dylanslack20, Sophie, @emilycjia @sameer_
@harvard_data @HarvardHBS please see above
