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Algorithmic Fairness amid Social Determinants: Reflection, Characterization, and Approach
Zeyu Tang, Alex John London, Atoosa Kasirzadeh, Sanmi Koyejo, Peter Spirtes, Kun Zhang
https://arxiv.org/abs/2508.08337

Algorithmic Fairness amid Social Determinants: Reflection, Characterization, and Approach
Social determinants are variables that, while not directly pertaining to any specific individual, capture key aspects of contexts and environments that have direct causal influences on certain attributes of an individual. Previous algorithmic fairness literature has primarily focused on sensitive attributes, often overlooking the role of social determinants. Our paper addresses this gap by introducing formal and quantitative rigor into a space that has been shaped largely by qualitative proposa…

Monitoring Robustness and Individual Fairness
Ashutosh Gupta, Thomas A. Henzinger, Konstantin Kueffner, Kaushik Mallik, David Pape
https://arxiv.org/abs/2506.00496

Monitoring Robustness and Individual Fairness
Input-output robustness appears in various different forms in the literature, such as robustness of AI models to adversarial or semantic perturbations and individual fairness of AI models that make decisions about humans. We propose runtime monitoring of input-output robustness of deployed, black-box AI models, where the goal is to design monitors that would observe one long execution sequence of the model, and would raise an alarm whenever it is detected that two similar inputs from the past…

A Proof System with Causal Labels (Part I): checking Individual Fairness and Intersectionality
Leonardo Ceragioli, Giuseppe Primiero
https://arxiv.org/abs/2507.14650

A Proof System with Causal Labels (Part I): checking Individual Fairness and Intersectionality
In this article we propose an extension to the typed natural deduction calculus TNDPQ to model verification of individual fairness and intersectionality in probabilistic classifiers. Their interpretation is obtained by formulating specific conditions for the application of the structural rule of Weakening. Such restrictions are given by causal labels used to check for conditional independence between protected and target variables.

A Statistical Physics perspective on fairness in shared expenses: The bar bill analogy
Nuno Crokidakis, Lucas Sigaud
https://arxiv.org/abs/2507.23542 https://

A Statistical Physics perspective on fairness in shared expenses: The bar bill analogy
In social contexts where individuals consume varying amounts, such as shared meals or bar gatherings, splitting the total bill equally often yields surprisingly fair outcomes. In this work, we develop a statistical physics framework to explain this emergent fairness by modeling individual consumption as stochastic variables drawn from a realistic distribution, specifically the gamma distribution. Introducing a Boltzmann-like weighting factor, we derive exact analytical expressions for the parti…

This https://arxiv.org/abs/2402.08533 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csGT_…

Grace Period is All You Need: Individual Fairness without Revenue Loss in Revenue Management
Imagine you and a friend purchase identical items at a store, yet only your friend received a discount. Would your friend's discount make you feel unfairly treated by the store? And would you be less willing to purchase from that store again in the future? Based on a large-scale online survey that we ran on Prolific, it turns out that the answers to the above questions are positive. Therefore, when allocating resources to different customers, sellers should consider both the total reward and in…

Canonical Representations of Markovian Structural Causal Models: A Framework for Counterfactual Reasoning
Lucas de Lara (IECL)
https://arxiv.org/abs/2507.16370

Canonical Representations of Markovian Structural Causal Models: A Framework for Counterfactual Reasoning
Counterfactual reasoning aims at answering contrary-to-fact questions like ''Would have Alice recovered had she taken aspirin?'' and corresponds to the most fine-grained layer of causation. Critically, while many counterfactual statements cannot be falsified -- even by randomized experiments -- they underpin fundamental concepts like individual-wise fairness. Therefore, providing models to formalize and implement counterfactual beliefs remains a fundamental scientific problem. In the Markovian …