arXiv AI August 5, 2026 By Jose M. \'Alvarez

Implementing Causal Perception: Competing SCMs and Situated Fairness

arXiv:2608. 03917v1 Announce Type: new Abstract: Causal perception occurs when agents with competing Structural Causal Models (SCMs) of the same system infer different probability distributions, including the hypothetical distributions implied by each agent's SCM under the same set of interventions.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv Machine Learning

Sep 18

When fairness metrics fail: A utility-based perspective on $\varepsilon$-fairness

The paper argues that traditional probabilistic fairness metrics can miss significant disparities in the actual consequences of decisions. By introducing a utility-based framework, the authors show that a process can satisfy ε-fairness yet still be maximally unfair when utilities are considered. They apply this framework to college admissions and credit‑risk assessment, demonstrating that equalizing probabilities alone may mask unequal utility outcomes across groups.

By Tolulope Fadina, Thorsten Schmidt

safety

arXiv AI

Jun 30

A causal modeling perspective on decision theory

arXiv:2606. 29911v1 Announce Type: new Abstract: Decision theory provides a formal framework for how agents should make choices under uncertainty, drawing on ideas from philosophy, probability, and causality.

By Arvid Sj\"olander

agents

arXiv AI

Jul 13

Tuning Derivatives for Causal Fairness in Machine Learning

arXiv:2605. 05882v2 Announce Type: replace-cross Abstract: Artificial-intelligence systems are becoming ubiquitous in society, yet their predictions typically inherit biases with respect to protected attributes such as race, gender, or age.

By Filip Edstr\"om, Guilherme W. F. Barros, Tetiana Gorbach, Xavier de Luna

safety

arXiv AI

Jul 28

Fairness Interventions in Classification: A Study on AI Explainability

arXiv:2407. 14766v4 Announce Type: replace-cross Abstract: This paper presents a philosophical and experimental study of fairness interventions in AI classification, centered on the explainability and transparency of corrective methods, and on the opposition between two fairness criteria, namely Demographic Parity and Equalized Odds.

By Thomas Souverain, Paul \'Egr\'e

benchmarks safety

arXiv AI

Jun 9

Causal Agent Replay: Counterfactual Attribution for LLM-Agent Failures

arXiv:2606. 08275v1 Announce Type: cross Abstract: When an LLM agent fails -- issues a refund it should not have, calls the wrong tool, leaks data -- existing tooling answers what happened (observability) or whether it passed (evaluation), but not which step caused the failure.

By Jaineet Shah

llms agents benchmarks

arXiv Machine Learning

Jun 17

Exposing the Illusion of Fairness: Auditing Vulnerabilities to Distributional Manipulation Attacks

arXiv:2507. 20708v3 Announce Type: replace Abstract: The rapid deployment of AI systems in high-stakes domains, including those classified as high-risk under the The EU AI Act (Regulation (EU) 2024/1689), has intensified the need for reliable compliance auditing.

By Valentin Lafargue, Adriana Laurindo Monteiro, Emmanuelle Claeys, Laurent Risser, Jean-Michel Loubes

robotics safety

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When fairness metrics fail: A utility-based perspective on $\varepsilon$-fairness

A causal modeling perspective on decision theory

Tuning Derivatives for Causal Fairness in Machine Learning

Fairness Interventions in Classification: A Study on AI Explainability

Causal Agent Replay: Counterfactual Attribution for LLM-Agent Failures

Exposing the Illusion of Fairness: Auditing Vulnerabilities to Distributional Manipulation Attacks