Bayesian Models and Algorithms for Fairness and Transparency
The General Data Protection Regulation (GDPR) states data should be processed lawfully, fairly and transparently. With this in mind, BayesianGDPR project aims to integrate the legal non-discriminatory principles of GDPR into automated machine-learning systems in a transparent manner. It will do so by using a novel Bayesian approach to model all sources of uncertainty, and taking into account feedback from humans and future consequences of their outputs. BayesianGDPR will provide organisations that rely on machine learning technologies with concrete tools allowing them compliance with the non-discriminatory principles of GDPR and similar laws. The project’s achievements will have an impact on computational law research and its integration into mainstream legal practice. It will also promote public confidence in machine learning systems.
- Novi Quadrianto
- Miri Zilka (Postdoc 2020–present)
- Yeat Jeng Ng (PhD Student 2020–present)
- Bradley Butcher, Vincent Huang, Christopher Robinson, Jeremy Reffin, Sema Sgaier, Grace Charles, Novi Quadrianto. Causal datasheet for datasets: An evaluation guide for real-world data analysis and data collection design using Bayesian Networks. Frontiers in Artificial Intelligence, 2021.
- Thomas Kehrenberg, Myles Bartlett, Oliver Thomas and Novi Quadrianto. Null-sampling for Interpretable and Fair Representations. European Conference on Computer Vision ECCV, Glasgow, UK, 2020.
- Viktoriia Sharmanska, Lisa Anne Hendricks, Trevor Darrell, Novi Quadrianto. Contrastive Examples for Addressing the Tyranny of the Majority. arXiv, 2020.