38 citations · 38 across the 1 of their papers we have counts for
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On The Impact of Machine Learning Randomness on Group Fairness
Prakhar Ganesh, Hongyan Chang, Martin Strobel +1
Statistical measures for group fairness in machine learning reflect the gap in performance of algorithms across different groups. These measures, however, exhibit a high variance b…
Data Privacy and Trustworthy Machine Learning
Martin Strobel, Reza Shokri
The privacy risks of machine learning models is a major concern when training them on sensitive and personal data. We discuss the tradeoffs between data privacy and the remaining g…
High Dimensional Model Explanations: an Axiomatic Approach
Neel Patel, Martin Strobel, Yair Zick
Complex black-box machine learning models are regularly used in critical decision-making domains. This has given rise to several calls for algorithmic explainability. Many explanat…
On the Privacy Risks of Model Explanations
Reza Shokri, Martin Strobel, Yair Zick
Privacy and transparency are two key foundations of trustworthy machine learning. Model explanations offer insights into a model's decisions on input data, whereas privacy is prima…