Causality for Ethics and Society

Causality plays a fundamental role in understanding many key ethical notions, such as responsibility, interpretability, fairness, harm, and related concepts. Causality is also fundamental to understanding broader social challenges, such as discrimination, inequality, the impact of artificial intelligence on society, and others. The rise of causal modeling methods has opened up entirely novel ways of thinking about the role that causality plays in addressing all of these issues, with the potential to both benefit from and contribute to a wide range of disciplines.

Causal models are already being actively employed in addressing important social challenges. In the sociological and legal literature on discrimination, causal methodology has been central both to conceptual discussions of whether demographic variables such as race, gender, and age can be causes, and in addressing thorny methodological disputes about how to detect discrimination using statistical data. Within philosophy and artificial intelligence, causal models have provided a basis for more rigorously theorizing about causal explanations, moral responsibility, and harm. Finally, causal models have contributed to discussions of algorithmic fairness by combining traditional statistical approaches to discrimination with causal approaches that focus on the use of proxies, path-specific effects, and counterfactuals.

This workshop will bring together participants from a range of academic fields in order to present and discuss the most recent developments on employing formal causal reasoning in ethics and in social contexts. Possible topics include, but are not limited to the following topics.

• Algorithmic fairness, harm, or bias, through a causal lens
• Causal models and moral responsibility
• The causal analysis of discrimination
• Applications to AI and deep learning
• The role of values in causal modeling

In addition to these possibilities, we will consider any submission employing causal models in addressing an ethical or socially relevant problem. We welcome contributions debating the applicability of causal concepts within a particular domain as well as proposals for modifying existing causal frameworks for addressing novel problems. As the workshop will include speakers from a wide range of academic fields, we encourage contributions that address problems arising in interdisciplinary contexts.

Keynote Speakers:

• Elias Bareinboim (Computer Science, Columbia University)
• Joseph Halpern (Computer Science, Cornell University)
• Lily Hu (Philosophy, Yale University)
• Niki Kilbertus (Computer Science, TU Munich)
• Kasper Lippert-Rasmussen (Political Science, Aarhus University)

General questions about the conference can be sent to: [email protected]

More information can be found at: