I start with characterizing a term, ‘Humphreys opacity’ (or, if you prefer, ‘epistemic opacity’):1 this involves the inability to surveil the steps of a process from a known input to a known desirable (or truthful, useful, beautiful, etc.) output in a timely manner to the decision-maker or responsible agent. (For more on the origin and nature of this characterization, recall this post.) In what follows, I set aside to what extent such Humphreys opacity is the effect of features of physical reality or is merely the result of a pragmatic cost-benefit analysis.
Humphreys opacity is in the news because the ideal to generate a so-called ‘glassbox’ AI — in which AI systems and machine learning models where the internal processes are fully visible, transparent, and interpretable to humans — seems so hard to achieve. In fact, Humphreys opacity is a design feature of contemporary LLMs that are rapidly being deployed in all kinds of organizations. At the moment neither end-users nor engineers can survey the steps that lead to an LLMs output in real time. It is by no means obvious that they could do so even after the fact in all salient contexts. Interestingly enough, at the moment such Humphreys opacity also seems a feature of any (say) Opus 4.8 token (in the sense of the token/type distinction) one may be interacting with as an end-user. Such tokens lack luminosity about the inner workings of ‘their own’ underlying machinery, too. This much is familiar enough in public debate and also the scholarly secondary literature.