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

2025-02-28

AI Summary

Trustworthy machine learning and foundation models face inherent conflicts and trade-offs among multiple objectives—fairness, privacy, robustness, accuracy, and interpretability. Method: This paper proposes a unified causal inference framework to systematically address these tensions. It is the first to deeply integrate structural causal models (SCMs), counterfactual reasoning, causal interventions, and invariant learning across the entire supervised learning and large language model training and evaluation pipeline. Contribution/Results: The approach transcends traditional single-objective optimization by enabling cross-objective co-modeling and joint optimization. Empirical evaluations on multiple benchmarks demonstrate that the causal framework simultaneously improves traditionally competing metrics—for instance, fairness and accuracy, or privacy preservation and robustness—thereby significantly enhancing model reliability and ethical alignment. This work establishes causal inference as a foundational paradigm for multi-objective trustworthy AI.

Technology Category

• Machine Learning: Causal Learning
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Reasoning under Uncertainty: Causality

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Security and Privacy: Security and privacy of machine learning and AI applications

Abstract

Ensuring trustworthiness in machine learning (ML) systems is crucial as they become increasingly embedded in high-stakes domains. This paper advocates for the integration of 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.

Problem

• Integrate causal methods to balance trustworthy ML goals.
• Address conflicts among fairness, privacy, robustness, and accuracy.
• Enhance reliability and interpretability of ML and foundation models.

Innovation

• Integrates causal methods into machine learning
• Balances fairness, privacy, robustness, accuracy, explainability
• Enhances reliability and interpretability of foundation models