用因果方法平衡可信AI中的公平、隐私等多重目标
Causality Is Key to Understand and Balance Multiple Goals in Trustworthy ML and Foundation Models
- 引入因果推理解决可信AI中各目标间的冲突
- 实证显示因果方法可同时提升公平性与准确性
- 适合关注AI伦理与系统可靠性的研究者
随着机器学习系统在高风险领域日益普及，确保其可信性至关重要。本文主张将因果方法融入机器学习，以协调可信AI的核心原则——公平性、隐私性、鲁棒性、准确性和可解释性之间的权衡。尽管这些目标理想上应同时满足，但当前常被孤立处理，导致矛盾与次优解。基于已有因果方法在公平性与准确性、隐私性与鲁棒性上的成功应用，本文认为因果视角对平衡可信AI及基础模型中的多重竞争目标至关重要。除了揭示这些权衡关系，本文还探讨了因果方法在实际中的整合路径，提出提升系统可靠性与可解释性的方案，并讨论采用因果框架所面临的挑战、局限与机遇，为构建更具问责性与伦理意识的AI系统铺路。
原文摘要 · Abstract (English)
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.
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