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

Researchers propose integrating causal methods into machine learning systems to balance competing objectives like fairness, privacy, robustness, accuracy, and explainability. The paper argues that addressing these principles in isolation leads to conflicts and suboptimal solutions, while causal approaches can help navigate trade-offs in both trustworthy ML and foundation models.

• →Traditional approaches to trustworthy ML often address objectives like fairness and privacy in isolation, creating conflicts and suboptimal outcomes.
• →Causal methods can help balance multiple competing objectives simultaneously in machine learning systems.
• →The integration of causality into ML and foundation models can enhance reliability and interpretability.
• →Existing applications show successful alignment of goals such as fairness with accuracy and privacy with robustness through causal approaches.
• →Adopting causal frameworks faces challenges and limitations but offers opportunities for more accountable AI systems.