Interpretability is the ability for the decision processes and inner workings of AI and machine learning systems to be understood by humans or other outside observers.[1]

Present-day machine learning systems are typically not very transparent or interpretable. You can use a model's output, but the model can't tell you why it made that output. This makes it hard to determine the cause of biases in ML models.[1]

Interpretability is a focus of Chris Olah and Anthropic's work, though most AI alignment organisations work on interpretability to some extent, such as Redwood Research[2].

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  1. ^

    Multicore (2020) Transparency / Interpretability (ML & AI), AI Alignment Forum, August 1.

  2. ^