CORDIS - EU research results

Bayesian Models and Algorithms for Fairness and Transparency

Project description

Novel Bayesian approach for fair, lawful and transparent data processing

The General Data Protection Regulation (GDPR) states data should be processed lawfully, fairly and transparently. With this in mind, the EU-funded 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.


"EU's GDPR prescribes that ""Personal Data shall be processed lawfully, fairly, and in a transparent manner."" The vision of this BayesianGDPR project is to integrate into automated machine learning systems using a novel Bayesian approach, in a transparent manner, the legal non-discriminatory principles of GDPR, taking into account feedback from humans and future consequences of their outputs. We aim to achieve this ambitious vision by 1) developing a machine learning framework for addressing fairness in classification problems and beyond, and under uncertainty about data, models, and predictions about future data (algorithmic fairness under uncertainty), 2) extending the framework to a setting where data points arrive over time, and models have to be dynamically updated when taking general feedback (feedback-driven setting), and 3) ensuring a human could understand how non-discrimination is defined and achieved by using, among others, uncertainty estimates for building interpretable models and/or explicitly explaining about changes being made to the models to enforce non-discriminatory principles (transparency in fairness). The BayesianGDPR project is ""doubly timely""; not just in terms of the criticality of the fairness and transparency in machine learning at this point in time, but also because recent breakthroughs in scalability have finally made it feasible to explore Bayesian approaches that are uniquely capable of addressing one of the most central aspects of the problem, i.e. uncertainty. BayesianGDPR will, in the short term, ensure that organisations relying on machine learning technologies are provided with concrete tools to comply with the non-discriminatory principles of GDPR and similar laws. In the medium term, it will impact research in computational law, and its integration into mainstream legal practice. In the long term, it will also ensure continued confidence of the general public in the deployment of machine learning systems."

Host institution

Net EU contribution
€ 1 329 947,00
BN1 9RH Brighton
United Kingdom

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South East (England) Surrey, East and West Sussex Brighton and Hove
Activity type
Higher or Secondary Education Establishments
Total cost
€ 1 329 947,00

Beneficiaries (2)