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Learning to Limit Data Collection via Scaling Laws: A Computational Interpretation for the Legal Principle of Data Minimization

Conference on Fairness, Accountability and Transparency (FAccT), 2021

16 July 2021

Papers citing "Learning to Limit Data Collection via Scaling Laws: A Computational Interpretation for the Legal Principle of Data Minimization"

8 / 8 papers shown

What Data is Really Necessary? A Feasibility Study of Inference Data Minimization for Recommender Systems

Jens Leysen

Marco Favier

Bart Goethals

124

29 Aug 2025

A Common Pool of Privacy Problems: Legal and Technical Lessons from a Large-Scale Web-Scraped Machine Learning Dataset

442

20 Jun 2025

Engineering the Law-Machine Learning Translation Problem: Developing Legally Aligned Models

389

23 Apr 2025

The Data Minimization Principle in Machine Learning

239

29 May 2024

From Principle to Practice: Vertical Data Minimization for Machine Learning

Robin Staab

Nikola Jovanović

Mislav Balunović

Martin Vechev

335

17 Nov 2023

Scaling Laws Do Not ScaleAAAI/ACM Conference on AI, Ethics, and Society (AIES), 2023

Fernando Diaz

Michael A. Madaio

386

05 Jul 2023

Participatory Personalization in ClassificationNeural Information Processing Systems (NeurIPS), 2023

337

08 Feb 2023

On the Trade-Off between Actionable Explanations and the Right to be ForgottenInternational Conference on Learning Representations (ICLR), 2022

Gjergji Kasneci

459

30 Aug 2022