SAML
Society-Aware Machine Learning (SAML)
SAML is a European Research Council (ERC) Starting Grant project focused on developing machine learning systems that are not only accurate and efficient but also fair, transparent, and socially responsible. By incorporating the perspectives of multiple stakeholders throughout the AI development process, SAML aims to create trustworthy and human-centered AI technologies.
This work is funded by the European Union through the ERC Starting Grant programme (Grant Agreement No. 101040177).
ERC Summary & Objectives
Objective 1: Developing a Society-Aware Machine Learning Framework
SAML establishes the methodological foundations for society-aware machine learning, where algorithms are designed and evaluated by explicitly accounting for the utilities, preferences, and welfare of all relevant stakeholders. This required addressing several fundamental technical challenges, including estimating the causal consequences of decisions and actions, comparing competing stakeholder objectives, and extending fairness assessment beyond traditional prediction-based metrics.
We developed new methods for causal inference that estimate the downstream consequences of decisions under realistic conditions, including hidden confounding and temporal dynamics (DeCaFlow, A Practical Approach to Causal Inference over Time).
We further introduced methods for comparing and jointly optimizing diverse objectives, integrating preferences and allowing practitioners to systematically explore Pareto-optimal trade-offs (COPA, Hellinger Multimodal Variational Autoencoders, Hyper-Transforming Latent Diffusion Models).
Building on these technical foundations, we established new frameworks for fairness assessment that move beyond binary decision outcomes by considering treatment decisions, their downstream consequences, long-term societal effects, and the welfare of multiple stakeholders (A Causal Framework to Measure and Mitigate Non-binary Treatment Discrimination, Fairness Beyond Binary Decisions, Designing Long-term Group Fair Policies in Dynamical Systems, Long-term Fairness with Selective Labels, First-See-Then-Design).
Objective 2: Achieving Consensual Machine Learning
Building on methodological advances (summarized under Objective 1), SAML develops machine learning methods that seek solutions benefiting all involved stakeholders.
We introduced decision-making frameworks that explicitly balance the utilities of system deployers, affected individuals, and society, enabling transparent exploration of performance–fairness trade-offs and the design of policies that remain fair over time (First-See-Then-Design, Designing Long-term Group Fair Policies in Dynamical Systems, Long-term Fairness with Selective Labels).
Beyond institutional decision-making, SAML empowers individuals affected by automated decisions. We develop a framework allowing individuals to overcome negative automated decisions through personalized algorithmic recourse that accounts for their preferences and constraints (From Universal to Individualized Actionability).
Finally, SAML improves the transparency and interpretability of machine learning by making trade-offs between competing objectives explicit and by developing inherently interpretable models that better expose the reasoning behind automated decisions (COPA, First-See-Then-Design, Towards Reasonable Concept Bottleneck Models).
Objective:3 Addressing Ethical Challenges in Machine Learning
The methodological advances developed throughout SAML translate into practical solutions for addressing ethical challenges arising from the deployment of machine learning in society.
We developed methods to identify and mitigate unfairness in high-stakes applications such as lending by uncovering previously overlooked forms of discrimination, extending fairness assessment beyond conventional metrics, and evaluating fairness in modern AI systems such as large language models (A Causal Framework to Measure and Mitigate Non-binary Treatment Discrimination, Fairness Beyond Binary Decisions, Accept or Deny? Evaluating LLM Fairness and Performance in Loan Approval, On the Misalignment Between Legal Notions and Statistical Metrics of Intersectional Fairness).
In parallel, the project increases individuals’ ability to respond to automated decisions through personalized recourse while improving the transparency and accountability of AI systems through interpretable models and explicit analysis of trade-offs between competing objectives (From Universal to Individualized Actionability, Towards Reasonable Concept Bottleneck Models).
SAML Publications
Project Contributors
- Batuhan Koyuncu
- Deborah Kanubala
- Georgi Vitanov
- Huyen Thuc Khanh Vo
- Isabel Valera
- Kavya Gupta
- Lena Marie Budde
- María Martínez García