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

TL;DR

This paper emphasizes the importance of causal methods in balancing multiple trustworthiness goals like fairness, privacy, and robustness in machine learning and foundation models, aiming for more reliable and ethical AI systems.

Contribution

It advocates for integrating causality into ML to effectively manage trade-offs among key trustworthiness principles, highlighting practical solutions and future challenges.

Findings

Causal approaches help balance fairness and accuracy.

Causality improves privacy and robustness trade-offs.

Practical integration enhances model interpretability.

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.