anssiko
commented
4 years ago I recently read a paper Model Cards for Model Reporting that discusses a "model card" proposal that suggests an approach where an ML model is accompanied by documentation detailing the model's limitations and performance characteristics. Perhaps this model [pun unintended] would work in the context of the web-based ML as well. In fact, it would be quite a natural fit in my view.
Here are couple of proof of concept model cards from Google's Cloud Vision API: face detection and object detection.
Edit: More examples of model cards from another project (https://mediapipe.dev/) at https://google.github.io/mediapipe/solutions/models
Adding @mmitchellai, one of the paper's authors, for comments.
dontcallmedom
mmitchellai
Jutta-Inclusive
toreini
In her talk, Jutta highlighted the risks for minorities or groups whose data are underrepresented in data used for training models, and approaches to reduce the bias (e.g. the "lawnmower" approach)
@JohnRochfordUMMS 's talk highlighted that privacy concerns make that phenomenon even stronger for people with disabilities, and highlighted tools that can help identify bias in training data.
Are there well known metrics or metadata that a model provider can (and ideally, should) attach to their models to help developers assess how much and what kind of bias they might be importing when they use a given model? Are there natural fora where discussions on these metadata are expected to happen?