arXiv:2411.08875v4 Announce Type: replace Abstract: Existing algorithms for explaining the output of image classifiers use different definitions of explanations and a variety of techniques to find them. However, none of the existing tools use a principled approach based on formal definitions of cause and explanation. In this paper we present a novel black-box approach to computing explanations grounded in the theory of actual causality. We prove relevant theoretical results and present an algorithm for computing approximate explanations based on these definitions. We prove termination of our a
The increasing complexity and adoption of AI models necessitate greater transparency and interpretability, especially in high-stakes applications.
A principled approach to causal explanations can enhance trust, accountability, and reliability of AI systems, addressing a critical bottleneck for wider deployment.
AI explanation methods shift from heuristic approaches to those grounded in formal causality, potentially standardizing how AI decisions are understood and audited.
- · AI developers
- · Regulatory bodies
- · Sectors reliant on explainable AI (e.g., healthcare, finance)
- · Auditing firms
- · Black-box AI vendors unwilling to adopt explainability standards
- · Competitors using less rigorous explanation methods
Improved understanding and debugging of complex AI models become possible.
New regulations and certification standards for AI explainability emerge, influencing product design and market access.
Public confidence in AI increases, accelerating adoption in sensitive and critical infrastructure domains.
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Read at arXiv cs.AI