Madeleine Waller

Maria-von-Linden-Straße 1

Tübingen, 72076

I am a Postdoctoral Researcher interested in AI & Law, specifically bridging the gap between technical approaches for fairness and explainability in algorithmic decision-making systems and relevant legal frameworks such as non-discrimination laws and the EU AI Act. I am part of the Theory of Machine Learning Group under Prof. Ulrike von Luxburg.

Previously, I completed my Ph.D. at King’s College London with Dr. Oana Cocarascu and Dr. Odinaldo Rodrigues, funded by the UKRI Centre for Doctoral Training in Safe and Trusted Artificial Intelligence. During my doctoral studies, I was a UKRI Policy Intern in the Financial Scrutiny Unit in Welsh Parliament and and took part in a placement at The Alan Turing Institute.

My thesis was on the fairness of algorithmic decision-making systems, examining and developing technical methods for detecting and mitigating bias in algorithms, with the goal of ensuring these tools are both effective and legally compliant. My current research focuses on how existing legal frameworks address issues of bias, discrimination, and accountability in AI and whether current regulations cover these issues suffiiciently.

news

May 01, 2026	Started as a Postdoctoral Researcher at the University of Tübingen!🍾
Mar 24, 2026	Passed my PhD Viva 🎉
Mar 06, 2026	Our paper got accepted at The 4th World Conference on Explainable Artificial Intelligence (XAI-2026)!
Dec 22, 2025	Submitted my PhD thesis! Just in time for Christmas 🎄

selected publications

Bias Mitigation Methods: Applicability, Legality, and Recommendations for Development

Madeleine Waller, Odinaldo Rodrigues, Michelle Seng Ah Lee, and 1 more author

Journal of Artificial Intelligence Research, 2024

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As algorithmic decision-making systems (ADMS) are increasingly deployed across various sectors, the importance of research on fairness in Artificial Intelligence (AI) continues to grow. In this paper we highlight a number of significant practical limitations and regulatory compliance issues associated with the application of existing bias mitigation methods to ADMS. We present an example of an algorithmic system used in recruitment to illustrate these limitations. Our analysis of existing methods indicates a pressing need for a change in the approach to the development of new methods. In order to address the limitations, we provide recommendations for key factors to consider in the development of new bias mitigation methods that aim to be effective in real-world scenarios and comply with legal requirements in the European Union, United Kingdom and United States, such as non-discrimination, data protection and sector-specific regulations. Further, we suggest a checklist relating to these recommendations that should be included with the development of new bias mitigation methods.
@article{waller2024bias, title = {Bias Mitigation Methods: Applicability, Legality, and Recommendations for Development}, author = {Waller, Madeleine and Rodrigues, Odinaldo and Lee, Michelle Seng Ah and Cocarascu, Oana}, journal = {Journal of Artificial Intelligence Research}, volume = {81}, pages = {1043--1078}, year = {2024}, }

Identifying Reasons for Bias: An Argumentation-Based Approach

Madeleine Waller, Odinaldo Rodrigues, and Oana Cocarascu

Proceedings of the AAAI Conference on Artificial Intelligence, 2024

Abs DOI Bib PDF Video

@article{walleraaai,
  title = {Identifying Reasons for Bias: An Argumentation-Based Approach},
  volume = {38},
  doi = {https://doi.org/10.1609/aaai.v38i19.30165},
  number = {19},
  journal = {Proceedings of the AAAI Conference on Artificial Intelligence},
  author = {Waller, Madeleine and Rodrigues, Odinaldo and Cocarascu, Oana},
  year = {2024},
  pages = {21664-21672},
}

Beyond Consistency: Nuanced Metrics for Individual Fairness

Madeleine Waller, Odinaldo Rodrigues, and Oana Cocarascu

Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency, FAccT 2025, Athens, Greece, June 23-26, 2025, 2025

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Individual fairness is the principle aiming for equitable treatment for each individual affected by decisions. Despite its intuitive appeal, the practical applications of individual fairness for algorithmic decision-making systems remain relatively unexplored. In this paper, we investigate the consistency score metric and demonstrate how it fails to adequately capture fairness at the individual level, underscoring the need for a more fine-grained approach. We show that (1) the consistency score obscures instances where individuals are treated significantly differently to the individuals most similar to them and (2) the perceived fairness of individual decisions can be affected by several factors, including the similarity notion itself. To address these issues, we propose four new metrics that measure different aspects of the treatment of individuals with respect to similar individuals, under varying similarity definitions. Our comprehensive evaluation of the new metrics shows that they offer a more nuanced approach to assessing individual fairness, enabling decision-makers to focus on individuals most adversely affected by controversial decisions.
@article{wallerfacct, author = {Waller, Madeleine and Rodrigues, Odinaldo and Cocarascu, Oana}, title = {Beyond Consistency: Nuanced Metrics for Individual Fairness}, journal = {Proceedings of the 2025 {ACM} Conference on Fairness, Accountability, and Transparency, FAccT 2025, Athens, Greece, June 23-26, 2025}, pages = {2087--2097}, publisher = {{ACM}}, year = {2025}, doi = {10.1145/3715275.3732141}, }