add interface or integration to provide interpretability/explainability of hugging face models
### Feature request

require model answer requests on training biases and model transparency for interpretability and explainability within workflow of such model processes, currently there is no support for this on the platform

e.g.
a form of blackbox testing on the models
a form of ui interface
a way to evaluate
a way to visualize
a linege of data changes in learning process
a publically available benchmarking of models
a way to retune the models - debasing bias

perhaps, linkage to captum/lime, or other such tooling

### Motivation

regulation requirements for trustworthy ai (to be able to answer how the model learned this for correctness, transparency, and fairness)
to be able to correct the biases in training datasets

this is important because you have an array of models which support zero transparency.
it is also important for progressing ai.
to build model lineage
to provide for continued compliance and data governance across different geographic regulations in use of such models.

fundamentally, to answer these questions:
how the result was produced
whether the model was correct in producing such a result based on the implementation

[https://ec.europa.eu/futurium/en/ai-alliance-consultation.1.html]

### Your contribution

not sure how I can help if the developers have yet to even add such feature and make themselves unapproachable, this is something that is constantly overlooked by the ML/DL community with a lot of marketing hype where they cannot fully explain outside of a research paper how the model process reached that result.

This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.

Please note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md) are likely to be ignored.