Details
Original language | English |
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Publication status | E-pub ahead of print - 2023 |
Publication series
Name | ACM Computing Surveys |
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Publisher | Association for Computing Machinery (ACM) |
ISSN (Print) | 0360-0300 |
Abstract
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2023. (ACM Computing Surveys).
Research output: Working paper/Preprint › Preprint
}
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 -