Operationalizing legal and societal constraints in information systems
Data protection laws define requirements for respectful processing of user data. However, the lack of technical interpretations of many of those requirements has inhibited their adoption. In this talk, I will discuss our recent efforts to operationalize several data protection principles from GDPR—including the data minimization principle and the right to be forgotten—in the context of automated profiling and decision-making systems. We propose new models that enable the adoption of those principles and evaluate the outcomes in the domain of recommendation and search systems. Our results demonstrate remaining practical challenges and some of the impacts the implementation of the legal principles might have on the digital ecosystem.
Bio: Asia J. Biega is an incoming faculty member at the Max Planck Institute for Security and Privacy, currently spending time as a postdoctoral researcher in the Fairness, Accountability, Transparency, and Ethics in AI (FATE) Group at Microsoft Research Montréal. At MPI-SP, she will head a group focused on Responsible Computing. Through interdisciplinary collaborations, she designs legally, ethically, and socially responsible information and social computing systems and studies how they interact with and influence their users. Before joining Microsoft Research, she completed her PhD at the Max Planck Institute for Informatics and the Max Planck Institute for Software Systems. She has published her work in leading information retrieval, Web, and data mining venues, and has been serving on the program committees of conferences such as SIGIR, WSDM, KDD, AAAI, and FAT*/FAccT. She has co-organized the NIST TREC Fair Ranking track, and a FAT* panel on technology refusal. Beyond academia, her perspectives and methodological approaches are informed by an industrial experience, including work on privacy infrastructure at Google and consulting for Microsoft product teams on issues related to FATE and privacy.