Reliability and Interpretability in Science and Deep Learning

All models, including AI systems, rely on hidden assumptions: The paper emphasizes that both traditional science and deep learning models operate under unstated assumptions, challenging the notion that AI can be purely data-driven or "theory-free." Understanding these assumptions is critical for assessing whether an AI system is actually reliable.

Deep neural networks are fundamentally different from traditional scientific models in ways that affect reliability: DNNs have much higher "epistemic complexity" than conventional scientific models, meaning their inner workings are harder to understand and verify. This complexity directly undermines our ability to assess how reliable these systems actually are and whether they will continue working well over time.

The "black box" problem is not just about transparency—it's about fundamental uncertainty: The paper identifies a direct connection between a model's interpretability (whether we can understand how it works) and our ability to estimate its reliability. When we cannot interpret a deep learning model, we cannot properly evaluate whether it will perform as expected in new situations.

Standard error analysis alone is insufficient for AI systems: Applying traditional scientific error-checking methods to deep learning models is inadequate because DNNs operate so differently from conventional scientific approaches. New frameworks are needed that account for these fundamental differences.

Interpretability is essential for responsible AI development: The paper suggests that improving our ability to interpret and understand AI models is not just a nice-to-have feature—it is necessary for building AI systems we can actually trust and rely upon responsibly.