REVIEW 1 cited by
A Review of the Role of Causality in Developing Trustworthy AI Systems
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original 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.
Forward citations
Cited by 1 Pith paper
-
Causal Abstraction Learning based on the Semantic Embedding Principle
The authors propose the Semantic Embedding Principle to learn linear causal abstractions on the Stiefel manifold from observational data with partial structural priors, and develop three Riemannian optimization algorithms.
Discussion (0). Continue with ORCID to comment.