Under Review at ACM CSUR.

Authors: 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, and Wolfgang Nejdl.

Topics: Causality, Artificial Intelligence, Counterfactual, Interpretability, Explainability, Robustness, Bias, Discrimination, Fairness, Privacy, Safety, Healthcare.

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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Preprint:

Our paper preprint is published on arXiv.

Download paper here

Repository:

In our repository, we curated a list of datasets used for recent Causal Machine Learning (ML) publications:

Citation

@misc{ganguly2023reviewrolecausalitydeveloping,
      title={A Review of the Role of Causality in Developing Trustworthy AI Systems}, 
      author={Niloy Ganguly and Dren Fazlija and Maryam Badar and Marco Fisichella and Sandipan Sikdar and Johanna Schrader and Jonas Wallat and Koustav Rudra and Manolis Koubarakis and Gourab K. Patro and {Wadhah} {Zai El Amri} and Wolfgang Nejdl},
      year={2023},
      eprint={2302.06975},
      archivePrefix={arXiv},
      url={https://arxiv.org/abs/2302.06975}, 
}