mlsanityData to 7 Oct 2026

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

arXiv 2302.06975 ↗First posted 14 Feb 2023cs.AIcited by 14

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Abstract (arXiv)

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.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Builds on 148 of its 300 references that are in the map. First posted on arXiv 14 Feb 2023.

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148 of 300 references.

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Citing papers per year: 2023: 4; 2024: 5; 2025: 2; 2026: 3.

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  1. 2023A Survey on Causal Discovery Methods for I.I.D. and Time Series Datacited by 35
  2. 2023CAR-DESPOT: Causally-Informed Online POMDP Planning for Robots in Confounded Environmentscited by 11
  3. 2023Equilibrium in the Computing Continuum through Active Inferencecited by 9
  4. 2025Causal Abstraction Learning based on the Semantic Embedding Principlecited by 9
  5. 2024Comprehensive Review and Empirical Evaluation of Causal Discovery Algorithms for Numerical Datacited by 8
  6. 2024Adaptive Stream Processing on Edge Devices through Active Inferencecited by 6
  7. 2024Causality from Bottom to Top: A Surveycited by 5
  8. 2023Active Inference on the Edge: A Design Studycited by 2
  9. 2025Causality Is Key to Understand and Balance Multiple Goals in Trustworthy ML and Foundation Modelscited by 2
  10. 2026Trustworthy AI Suffers from Invariance Conflicts and Causality is The Solutioncited by 1
  11. 2024COBRA-PPM: A Causal Bayesian Reasoning Architecture Using Probabilistic Programming for Robot Manipulation Under Uncertaintycited by 0
  12. 2024Industrial-Grade Smart Troubleshooting through Causal Technical Language Processing: a Proof of Conceptcited by 0
2 more
  1. 2026Responsible AI for General-Purpose Systems: Overview, Challenges, and A Path Forwardcited by 0
  2. 2026Self-Explainability in Self-Adaptive and Self-Organising Systems: Status and Research Directionscited by 0