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A Review of the Role of Causality in Developing Trustworthy AI Systems
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A Review of the Role of Causality in Developing Trustworthy AI Systems
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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.
Forward citations
Cited by 3 Pith papers
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Trustworthy AI Suffers from Invariance Conflicts and Causality is The Solution
Causality provides a unifying framework for resolving trade-offs in trustworthy AI by managing invariance conflicts under changes to the data-generating process.
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Trustworthy AI Suffers from Invariance Conflicts and Causality is The Solution
Causality resolves trade-offs in trustworthy AI by treating them as invariance conflicts under different data-generating process changes.
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Self-Explainability in Self-Adaptive and Self-Organising Systems: Status and Research Directions
A systematic literature review defines self-explainability, proposes a taxonomy and levels framework, and reports that most approaches are conceptual with no standard evaluation method.
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