A Review of the Role of Causality in Developing Trustworthy AI Systems
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.
Code (1)
Similar Papers 제목 키워드 기반
A Survey of Out-of-distribution Generalization for Graph Machine Learning from a Causal View
Graph machine learning (GML) has been successfully applied across a wide range of tasks. Nonetheless, GML faces significant challenges in generalizing over out-of-distribution (OOD) data, which raises concerns about its …
FairnessOut-of-Distribution GeneralizationRE-centric Recommendations for the Development of Trustworthy(er) Autonomous Systems
Complying with the EU AI Act (AIA) guidelines while developing and implementing AI systems will soon be mandatory within the EU. However, practitioners lack actionable instructions to operationalise ethics during AI syst…
EthicsTowards Trustworthy and Aligned Machine Learning: A Data-centric Survey with Causality Perspectives
The trustworthiness of machine learning has emerged as a critical topic in the field, encompassing various applications and research areas such as robustness, security, interpretability, and fairness. The last decade saw…
Adversarial RobustnessFairnessparameter-efficient fine-tuningSurveyCausality Is Key to Understand and Balance Multiple Goals in Trustworthy ML and Foundation Models
Ensuring trustworthiness in machine learning (ML) systems is crucial as they become increasingly embedded in high-stakes domains. This paper advocates for integrating causal methods into machine learning to navigate the …
FairnessNavigateTrustworthy Artificial Intelligence in the Context of Metrology
We review research at the National Physical Laboratory (NPL) in the area of trustworthy artificial intelligence (TAI), and more specifically trustworthy machine learning (TML), in the context of metrology, the science of…
Uncertainty Quantification