Causality Is Key to Understand and Balance Multiple Goals in Trustworthy ML and Foundation Models

TL;DR

The paper addresses the challenge of balancing multiple trustworthy-AI objectives—fairness, privacy, robustness, explainability, and accuracy—in both standard ML and foundation models. It argues for a unifying causal framework based on structural causal models and do-calculus to identify invariant pathways, disentangle direct and indirect effects, and guide interventions. Key contributions include outlining concrete integration strategies such as Causally Constrained ML, invariant feature learning, disentangled representations, double ML, and causal discovery, along with a causal auditing perspective for privacy and accountability. The work also discusses practical integration across pre-training, post-training, and auditing, and highlights challenges like data scarcity and transportability while proposing opportunities such as scalable causal pipelines and high-quality causal datasets. Overall, this approach aims to make AI in high-stakes settings more ethical, transparent, and reliable by reducing reliance on spurious correlations and clarifying underlying causal mechanisms.

Abstract

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 trade-offs among key principles of trustworthy ML, including fairness, privacy, robustness, accuracy, and explainability. While these objectives should ideally be satisfied simultaneously, they are often addressed in isolation, leading to conflicts and suboptimal solutions. Drawing on existing applications of causality in ML that successfully align goals such as fairness and accuracy or privacy and robustness, this paper argues that a causal approach is essential for balancing multiple competing objectives in both trustworthy ML and foundation models. Beyond highlighting these trade-offs, we examine how causality can be practically integrated into ML and foundation models, offering solutions to enhance their reliability and interpretability. Finally, we discuss the challenges, limitations, and opportunities in adopting causal frameworks, paving the way for more accountable and ethically sound AI systems.

Figures (2)

• Figure 1: Causal Trustworthy ML Cycle: Causal ML can leverage existing knowledge and causal auditing to enhance different components of trustworthiness: explainability, fairness, privacy, and accuracy while simultaneously advancing understanding through causal discovery.
• Figure 2: While trustworthy AI involves inherent trade-offs between its key components, causality can help mitigate these tensions and enhance synergies.