Periodic Reporting for period 2 - BayesianGDPR (Bayesian Models and Algorithms for Fairness and Transparency)
Période du rapport: 2021-10-01 au 2023-03-31
The ambition of the BayesianGDPR project is to develop models and algorithms that will enable large-scale applications of fair machine learning systems (taking into account fairness under uncertainty in collected data, in models, in future data predictions, and in future consequences of decisions or actions) that are also transparent in various challenging domains in science, industry, and decision making. We set out to achieve this ambitious grand challenge by: 1) developing a machine learning framework for addressing fairness under uncertainty in a static setting, 2) extending the framework for addressing fairness under uncertainty in a dynamic setting, and 3) allowing stakeholders to gain some knowledge of what changes are required for fairness to be met, thereby ensuring transparency in fairness.
The success of the BayesianGDPR project would benefit many other machine learning-based disciplines, such as computer vision, natural language processing, and data mining. In the short term, organisations relying on machine learning technologies will have concrete tools to comply with the non-discriminatory principles of GDPR and similar laws. In the medium term, BayesianGDPR would impact research in computational law, and its integration into mainstream legal practice. In the long term, BayesianGDPR will also ensure the continued confidence of the general public in the deployment of machine learning systems.
Key publications around fairness under uncertainty in a static setting:
* Myles Bartlett, Sara Romiti, Viktoriia Sharmanska, Novi Quadrianto. Okapi: Generalising Better by Making Statistical Matches Match. Thirty-Sixth Conference on Neural Information Processing Systems NeurIPS, New Orleans, Louisiana, USA, 2022.
* Sara Romiti, Christopher Inskip, Viktoriia Sharmanska, Novi Quadrianto. RealPatch: A Statistical Matching Framework for Model Patching with Real Samples. European Conference on Computer Vision ECCV, Tel-Aviv, Israel, 2022.
* Thomas Kehrenberg, Myles Bartlett, Viktoriia Sharmanska, Novi Quadrianto. Addressing Missing Sources with Adversarial Support-Matching. arXiv, 2022.
* 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, p. 18. ISSN 2624-8212, 2021.
* Viktoriia Sharmanska, Lisa Anne Hendricks, Trevor Darrell, Novi Quadrianto. Contrastive Examples for Addressing the Tyranny of the Majority. arXiv, 2020.
Key publications around fairness under uncertainty in a dynamic setting:
* Ainhize Barrainkua, Paula Gordaliza, Jose A. Lozano, Novi Quadrianto. A Survey on Preserving Fairness Guarantees in Changing Environments. arXiv, 2022.
* Gergely D. Németh, Miguel Angel Lozano, Novi Quadrianto, Nuria Oliver. A Snapshot of the Frontiers of Client Selection in Federated Learning. arXiv, 2022.
Key publications around transparency in fairness:
* Oliver Thomas, Fair Representations in the Data Domain. PhD Thesis, University of Sussex, 2022.
* Oliver Thomas, Miri Zilka, Adrian Weller, Novi Quadrianto. An Algorithmic Framework for Positive Action. ACM conference on Equity and Access in Algorithms, Mechanisms, and Optimization EAAMO, Virtual, 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.