Operationalizing the Legal Principle of Data Minimization for Personalization. - Asia J. Biega; Peter Potash; Hal Daumé III; Fernando Diaz 001; Michèle Finck (SIGIR, 2020)

Reviving Purpose Limitation and Data Minimisation in Personalisation, Profiling and Decision-Making Systems. - Michèle Finck; Asia Biega (CoRR, 2021)

Learning to Limit Data Collection via Scaling Laws: Data Minimization Compliance in Practice. - Divya Shanmugam; Samira Shabanian; Fernando Diaz 001; Michèle Finck; Asia Biega (CoRR, 2021)

Post-Hoc Explanations Fail to Achieve their Purpose in Adversarial Contexts. - Sebastian Bordt; Michèle Finck; Eric Raidl; Ulrike von Luxburg (FAccT, 2022)

Learning to Limit Data Collection via Scaling Laws: A Computational Interpretation for the Legal Principle of Data Minimization. - Divya Shanmugam; Fernando Diaz 001; Samira Shabanian; Michèle Finck; Asia Biega (FAccT, 2022)

Regulating data intermediaries: The impact of the Data Governance Act on the EU's data economy. - Gabriele Carovano; Michèle Finck (Comput. Law Secur. Rev., 2023)

Successful implementation of the EU AI Act requires interdisciplinary efforts. - Christian Montag; Michèle Finck (Nat. Mac. Intell., 2024)

Robustness and Cybersecurity in the EU Artificial Intelligence Act. - Henrik Nolte; Miriam Rateike; Michèle Finck (FAccT, 2025)

Machine Learners Should Acknowledge the Legal Implications of Large Language Models as Personal Data. - Henrik Nolte; Michèle Finck; Kristof Meding (CoRR, 2025)