Trustworthy AI Suffers from Invariance Conflicts and Causality is The Solution

Trustworthy AI goals conflict with each other: The paper identifies that core objectives like fairness, robustness, privacy, and explainability are "hard to achieve simultaneously, especially while preserving utility," meaning developers often face difficult trade-offs when trying to make AI systems more trustworthy.

Causality helps explain why trade-offs exist: Rather than treating trustworthy AI challenges as separate problems, the paper argues that causality provides a "unifying framework" for understanding these conflicts as stemming from incompatible requirements for how models should behave when data changes—offering a common language to discuss why improving one aspect sometimes worsens another.

Not all trade-offs are unavoidable: Through case studies and simulations, the authors demonstrate that some conflicts between trustworthy AI objectives can be "softened or resolved through selective invariance," suggesting that careful causal analysis may reveal solutions that weren't obvious when treating problems independently.

This applies broadly across AI systems: The framework applies to both traditional machine learning models and large-scale foundation models (like large language models), indicating the findings are relevant across the spectrum of modern AI deployment.

Significant work remains: The paper acknowledges "open challenges and opportunities," suggesting that while causality is a promising direction for building trustworthy AI, the field still needs to develop practical methods for applying causal thinking to real-world AI systems.