Interpretability in the context of foundation models refers to our ability to understand and explain how these large-scale neural networks make decisions. These models, including large language and vision-language models, often function as complex "black boxes," meaning their internal reasoning steps remain opaque. Achieving interpretability is crucial for multiple reasons, particularly in AI safety and alignment. It enables us to verify that a model isn't pursuing unintended goals or harboring hidden biases. Additionally, interpretability aids in debugging models by allowing engineers to diagnose errors more effectively than treating models as opaque artifacts. Given the widespread deployment of foundation models, interpretability has become a key factor in ensuring trustworthiness and control, allowing users to calibrate their trust in AI systems that will be ubiquitous in society.
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