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On the Trade-Off between Actionable Explanations and the Right to be Forgotten
Martin Pawelczyk · Tobias Leemann · Asia Biega · Gjergji Kasneci
Event URL: https://openreview.net/forum?id=0YezO0Y_FKa »

As machine learning (ML) models are increasingly being deployed in high-stakes applications, policymakers have suggested tighter data protection regulations (e.g., GDPR, CCPA). One key principle is the "right to be forgotten" which gives users the right to have their data deleted. To date, it is unknown whether these two principles can be operationalized simultaneously. Therefore, we introduce and study the problem of recourse invalidation in the context of data deletion requests. More specifically, we theoretically and empirically analyze the behavior of popular state-of-the-art algorithms and demonstrate that the recourses generated by these algorithms are likely to be invalidated if a small number of data deletion requests (e.g., 1 or 2) warrant updates of the predictive model. For the setting of linear models and overparameterized neural networks -- studied through the lens of neural tangent kernels (NTKs) -- we suggest a framework to identify a minimal subset of critical training points which, when removed, maximize the fraction of invalidated recourses. Using our framework, we empirically show that the removal of as little as 2 data instances from the training set can invalidate up to 95 percent of all recourses output by popular state-of-the-art algorithms. Thus, our work raises fundamental questions about the compatibility of "the right to an actionable explanation" in the context of the "right to be forgotten" while also providing constructive insights on the determining factors of recourse robustness.

Author Information

Martin Pawelczyk (University of Tübingen)

# Academic Exp ## Phd Student at Uni of Tübingen, Germany: ## MSc Statistics, London School of Economics, UK ## MSc Econometrics, University of Edinburgh, UK ## BSc Economics, University of Cologne, Germany # Work Exp ## ML intern at SDG financing Lab, OECD, Paris ## Working student at r2b energy consulting, Cologne

Tobias Leemann (University of Tuebingen)
Asia Biega (Max Planck Institute for Security and Privacy)
Gjergji Kasneci (University of Tuebingen)

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