76 citations · 77 across the 3 of their papers we have counts for
4 papers
Post-Hoc Explanations Fail to Achieve their Purpose in Adversarial Contexts
Sebastian Bordt, Michèle Finck, Eric Raidl +1
Existing and planned legislation stipulates various obligations to provide information about machine learning algorithms and their functioning, often interpreted as obligations to…
Learning to Limit Data Collection via Scaling Laws: A Computational Interpretation for the Legal Principle of Data Minimization
Divya Shanmugam, Samira Shabanian, Fernando Diaz +2
Modern machine learning systems are increasingly characterized by extensive personal data collection, despite the diminishing returns and increasing societal costs of such practice…
Reviving Purpose Limitation and Data Minimisation in Data-Driven Systems
Asia J. Biega, Michèle Finck
This paper determines whether the two core data protection principles of data minimisation and purpose limitation can be meaningfully implemented in data-driven systems. While cont…
Operationalizing the Legal Principle of Data Minimization for Personalization
Asia J. Biega, Peter Potash, Hal Daumé +2
Article 5(1)(c) of the European Union's General Data Protection Regulation (GDPR) requires that "personal data shall be [...] adequate, relevant, and limited to what is necessary i…