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Individual utilities of life satisfaction reveal inequality aversion unrelated to political alignment
Crispin Cooper, Ana Friedrich, Tommaso Reggiani, Wouter Poortinga
https://arxiv.org/abs/2509.07793 …

Individual utilities of life satisfaction reveal inequality aversion unrelated to political alignment
How should well-being be prioritised in society, and what trade-offs are people willing to make between fairness and personal well-being? We investigate these questions using a stated preference experiment with a nationally representative UK sample (n = 300), in which participants evaluated life satisfaction outcomes for both themselves and others under conditions of uncertainty. Individual-level utility functions were estimated using an Expected Utility Maximisation (EUM) framework and tested …

Stairway to Fairness: Connecting Group and Individual Fairness
Theresia Veronika Rampisela, Maria Maistro, Tuukka Ruotsalo, Falk Scholer, Christina Lioma
https://arxiv.org/abs/2508.21334

Stairway to Fairness: Connecting Group and Individual Fairness
Fairness in recommender systems (RSs) is commonly categorised into group fairness and individual fairness. However, there is no established scientific understanding of the relationship between the two fairness types, as prior work on both types has used different evaluation measures or evaluation objectives for each fairness type, thereby not allowing for a proper comparison of the two. As a result, it is currently not known how increasing one type of fairness may affect the other. To fill this…

Who Pays for Fairness? Rethinking Recourse under Social Burden
Ainhize Barrainkua, Giovanni De Toni, Jose Antonio Lozano, Novi Quadrianto
https://arxiv.org/abs/2509.04128 https:…

Who Pays for Fairness? Rethinking Recourse under Social Burden
Machine learning based predictions are increasingly used in sensitive decision-making applications that directly affect our lives. This has led to extensive research into ensuring the fairness of classifiers. Beyond just fair classification, emerging legislation now mandates that when a classifier delivers a negative decision, it must also offer actionable steps an individual can take to reverse that outcome. This concept is known as algorithmic recourse. Nevertheless, many researchers have exp…

Fairness in Repeated Matching: A Maximin Perspective
Eugene Lim, Tzeh Yuan Neoh, Nicholas Teh
https://arxiv.org/abs/2510.04624 https://arxiv.org/pdf/2510.0…

Fairness in Repeated Matching: A Maximin Perspective
We study a sequential decision-making model where a set of items is repeatedly matched to the same set of agents over multiple rounds. The objective is to determine a sequence of matchings that either maximizes the utility of the least advantaged agent at the end of all rounds (optimal) or at the end of every individual round (anytime optimal). We investigate the computational challenges associated with finding (anytime) optimal outcomes and demonstrate that these problems are generally computa…

Local Search-based Individually Fair Clustering with Outliers
Binita Maity, Shrutimoy Das, Anirban Dasgupta
https://arxiv.org/abs/2510.06130 https://arxiv.…

Local Search-based Individually Fair Clustering with Outliers
In this paper, we present a local search-based algorithm for individually fair clustering in the presence of outliers. We consider the individual fairness definition proposed in Jung et al., which requires that each of the $n$ points in the dataset must have one of the $k$ centers within its $n/k$ nearest neighbors. However, if the dataset is known to contain outliers, the set of fair centers obtained under this definition might be suboptimal for non-outlier points. In order to address this iss…

AI-Driven Grading and Moderation for Collaborative Projects in Computer Science Education
Songmei Yu, Andrew Zagula
https://arxiv.org/abs/2510.03998 https://

AI-Driven Grading and Moderation for Collaborative Projects in Computer Science Education
Collaborative group projects are integral to computer science education, as they foster teamwork, problem-solving skills, and industry-relevant competencies. However, assessing individual contributions within group settings has long been a challenge. Traditional assessment strategies, such as the equal distribution of grades or subjective peer assessments, often fall short in terms of fairness, objectivity, and scalability, particularly in large classrooms. This paper introduces a semi-automate…

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.

Correct-By-Construction: Certified Individual Fairness through Neural Network Training
Ruihan Zhang, Jun Sun
https://arxiv.org/abs/2508.15642 https://arxiv…

Correct-By-Construction: Certified Individual Fairness through Neural Network Training
Fairness in machine learning is more important than ever as ethical concerns continue to grow. Individual fairness demands that individuals differing only in sensitive attributes receive the same outcomes. However, commonly used machine learning algorithms often fail to achieve such fairness. To improve individual fairness, various training methods have been developed, such as incorporating fairness constraints as optimisation objectives. While these methods have demonstrated empirical effectiv…

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…

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…

How Quantization Shapes Bias in Large Language Models
Federico Marcuzzi, Xuefei Ning, Roy Schwartz, Iryna Gurevych
https://arxiv.org/abs/2508.18088 https://

How Quantization Shapes Bias in Large Language Models
This work presents a comprehensive evaluation of how quantization affects model bias, with particular attention to its impact on individual demographic subgroups. We focus on weight and activation quantization strategies and examine their effects across a broad range of bias types, including stereotypes, toxicity, sentiment, and fairness. We employ both probabilistic and generated text-based metrics across nine benchmarks and evaluate models varying in architecture family and reasoning ability.…

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 …

Fair Cooperation in Mixed-Motive Games via Conflict-Aware Gradient Adjustment
Woojun Kim, Katia Sycara
https://arxiv.org/abs/2508.17696 https://arxiv.org/p…

Fair Cooperation in Mixed-Motive Games via Conflict-Aware Gradient Adjustment
Multi-agent reinforcement learning in mixed-motive settings presents a fundamental challenge: agents must balance individual interests with collective goals, which are neither fully aligned nor strictly opposed. To address this, reward restructuring methods such as gifting and intrinsic motivation have been proposed. However, these approaches primarily focus on promoting cooperation by managing the trade-off between individual and collective returns, without explicitly addressing fairness with …

Assessing Trustworthiness of AI Training Dataset using Subjective Logic -- A Use Case on Bias
Koffi Ismael Ouattara, Ioannis Krontiris, Theo Dimitrakos, Frank Kargl
https://arxiv.org/abs/2508.13813

Assessing Trustworthiness of AI Training Dataset using Subjective Logic -- A Use Case on Bias
As AI systems increasingly rely on training data, assessing dataset trustworthiness has become critical, particularly for properties like fairness or bias that emerge at the dataset level. Prior work has used Subjective Logic to assess trustworthiness of individual data, but not to evaluate trustworthiness properties that emerge only at the level of the dataset as a whole. This paper introduces the first formal framework for assessing the trustworthiness of AI training datasets, enabling uncert…