TY - UNPB

T1 - A Review of the Role of Causality in Developing Trustworthy AI Systems

AU - Ganguly, Niloy

AU - Fazlija, Dren

AU - Badar, Maryam

AU - Fisichella, Marco

AU - Sikdar, Sandipan

AU - Schrader, Johanna

AU - Wallat, Jonas

AU - Rudra, Koustav

AU - Koubarakis, Manolis

AU - Patro, Gourab K.

AU - Zai El Amri, Wadhah

AU - Nejdl, Wolfgang

PY - 2023

Y1 - 2023

N2 - 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.

AB - 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.

U2 - 10.48550/ARXIV.2302.06975

DO - 10.48550/ARXIV.2302.06975

M3 - Preprint

T3 - ACM Computing Surveys

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

ER -