In recent years, there has been increasing interest in causal reasoning for designing fair decision-making systems due to its compatibility with legal frameworks, interpretability for human stakeholders, and robustness to spurious correlations inherent in observational data, among other factors. The recent attention to causal fairness, however, has been accompanied with great skepticism due to practical and epistemological challenges with applying current causal fairness approaches in the literature. Motivated by the long-standing empirical work on causality in econometrics, social sciences, and biomedical sciences, in this paper we lay out the conditions for appropriate application of causal fairness under the "potential outcomes framework." We highlight key aspects of causal inference that are often ignored in the causal fairness literature. In particular, we discuss the importance of specifying the nature and timing of interventions on social categories such as race or gender. Precisely, instead of postulating an intervention on immutable attributes, we propose a shift in focus to their perceptions and discuss the implications for fairness evaluation. We argue that such conceptualization of the intervention is key in evaluating the validity of causal assumptions and conducting sound causal analysis including avoiding post-treatment bias. Subsequently, we illustrate how causality can address the limitations of existing fairness metrics, including those that depend upon statistical correlations. Specifically, we introduce causal variants of common statistical notions of fairness, and we make a novel observation that under the causal framework there is no fundamental disagreement between different notions of fairness. Finally, we conduct extensive experiments where we demonstrate our approach for evaluating and mitigating unfairness, specially when post-treatment variables are present.
DISIM, University of L’Aquila（拉奎拉大学DISIM）；DIB and CILA, University of Bari “Aldo Moro”（巴里大学“Aldo Moro”DIB和CILA）
Transparency is a key requirement for ethical machines. Verified ethical behavior is not enough to establish justified trust in autonomous intelligent agents: it needs to be supported by the ability to explain decisions. Logic Programming (LP) has a great potential for developing such perspective ethical systems, as in fact logic rules are easily comprehensible by humans. Furthermore, LP is able to model causality, which is crucial for ethical decision making.
Causality Is Key to Understand and Balance Multiple Goals in Trustworthy ML and Foundation Models
CISPA Helmholtz Center for Information Security（CISPA海德堡信息安全中心）；Max Planck Institute for Intelligent Systems, Tübingen（马克斯·普朗克智能系统研究所（图宾根））；Google Research（谷歌研究）；ETH Zürich（苏黎世联邦理工学院）；University of Toronto（多伦多大学）
A Shift in Perspective on Causality in Domain Generalization
CHAI Hub（CHAI中心）；The University of Edinburgh（爱丁堡大学）；University of Manchester（曼彻斯特大学）；Archimedes, Athena Research Center（阿奇米德与雅典研究所）；King's College London（伦敦国王学院）；University College London（伦敦大学学院）；National and Kapodistrian University of Athens（雅典国家与卡波迪斯蒂亚纳大学）
It is crucial to consider the social and ethical consequences of AI and ML based decisions for the safe and acceptable use of these emerging technologies. Fairness, in particular, guarantees that the ML decisions do not result in discrimination against individuals or minorities. Identifying and measuring reliably fairness/discrimination is better achieved using causality which considers the causal relation, beyond mere association, between the sensitive attribute (e.g. gender, race, religion, etc.) and the decision (e.g. job hiring, loan granting, etc.). The big impediment to the use of causal…
Reinforcement learning (RL) has shown promise as a tool for engineering safe, ethical, or legal behaviour in autonomous agents. Its use typically relies on assigning punishments to state-action pairs that constitute unsafe or unethical choices. Despite this assignment being a crucial step in this approach, however, there has been limited discussion on generalizing the process of selecting punishments and deciding where to apply them. In this paper, we adopt an approach that leverages an existing framework -- the normative supervisor of (Neufeld et al., 2021) -- during training. This normative …
Revealing Multimodal Causality with Large Language Models
University of Technology Sydney（技术大学悉尼大学）；Tongji University（同济大学）；University of Melbourne（墨尔本大学）；University of Sydney（悉尼大学）；Macquarie University（麦考瑞大学）
Language Agents Meet Causality -- Bridging LLMs and Causal World Models
University of Amsterdam（阿姆斯特丹大学）；Institute for Logic, Language and Computation (ILLC)（逻辑、语言与计算研究所）；University of Edinburgh（爱丁堡大学）；Institute for Language, Cognition and Computation (ILCC)（语言、认知与计算研究所）
