A Review of the Role of Causality in Developing Trustworthy AI Systems

Niloy Ganguly; Dren Fazlija; Maryam Badar; Marco Fisichella; Sandipan Sikdar; Johanna Schrader; Jonas Wallat; Koustav Rudra; Manolis Koubarakis; Gourab K. Patro; Wadhah Zai El Amri; Wolfgang Nejdl

arXiv · 2302.06975

A Review of the Role of Causality in Developing Trustworthy AI Systems

Niloy Ganguly·Dren Fazlija·Maryam Badar·Marco Fisichella·Sandipan Sikdar·Johanna Schrader·Jonas Wallat·Koustav Rudra·Manolis Koubarakis·Gourab K. Patro·Wadhah Zai El Amri·Wolfgang Nejdl

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Abstract

State-of-the-art AI models largely lack an understanding of the cause-effect relationship that governs human understanding of the real world. Consequently, these models do not generalize to unseen data, often produce unfair results, and are difficult to interpret. This has led to efforts to improve the trustworthiness aspects of AI models. Recently, causal modeling and inference methods have emerged as powerful tools. This review aims to provide the reader with an overview of causal methods that have been developed to improve the trustworthiness of AI models. We hope that our contribution will motivate future research on causality-based solutions for trustworthy AI.

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Niloy Ganguly, Dren Fazlija, Maryam Badar, Marco Fisichella, Sandipan Sikdar, Johanna Schrader, Jonas Wallat, Koustav Rudra, Manolis Koubarakis, Gourab K. Patro, Wadhah Zai El Amri, Wolfgang Nejdl. 2023-02-14. A Review of the Role of Causality in Developing Trustworthy AI Systems. https://arxiv.org/abs/2302.06975

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