I think this is a very valid hypothesis but it's hard to control for in experiments, since if these traits are necessary to become (or stay) famous, we don't really have a control group.
LLMs are largely used by developers, who (in some sense or the other) supervise what the LLM does constantly (even if that means for sum committing to main and running in production). We do already have a lot of tools: tests, compilation, a programming language with its harsh restrictions compared to natural language, and of course the eye test, this is not the case for a lot of jobs where GenAI is used for hyperautomation, so I am really curious in which way it will or won't get adopted in other areas.
I just open-sourced: https://github.com/cloudexplain/xaiflow, a mlflow plugin to get interactive xai (particularly shap values) as mlflow artifacts.
Furthermore looking into causal AI, especially dowhy.
Thanks for the question, there are a couple of existing solutions:
- There is already a mlflow builtin tool to log shap plots. This is quite helpful but becomes tedious if you want to dive deep into explainability, e.g. if you want to understand the influence factors for 100s of observations. Furthermore they lack interactivity. Here's the link to the builtin tool: https://mlflow.org/docs/latest/ml/evaluation/shap
- There are tools like shapash or what-if tool, but those require a running python environment. This plugin let's you log shap values in any productive run and explore them in pure html, with some of the features that the other tools provide (more might be coming if we see interest in this).