[abra0]

I'm not sure exactly what the ask here is.

>In contrast, for our own Entity Recognition models we can (and do) calculate probabilities that explain why a certain entity is shown.

>Hence, I think for API users of GPT3, OpenAI should return additional statistics why a certain result is returned the way it is to make it really useful and more importantly compliant.

For LLMs, you can get the same thing: the distribution of probabilities for the next token, for each token. But right now we cannot say why the probabilities are the way they are, same goes for your image recognition models.

[jsemrau]

The problem in a nutshell, and the one the FTC had pointed out, is Model explainability. I was working in the past of an AI for automated lending decisions. We were asked to be able to explain every single decision the engine took.

If now a news article reaches our AI engine, it will tag, categorize, classify, and rank this news article. All based on models that are explainable.

LLMs, at least how I personally implemented them in the past, create a huge black box that is largely non-explainable.