Open jaanli opened 2 months ago
sorry @jaanli I'm just seeing this now - would love to discuss further, would a quick call be easier than trying to discuss here?
sorry @jaanli I'm just seeing this now - would love to discuss further, would a quick call be easier than trying to discuss here?
Sure! No rush happy to chat next week I’m at jaan@onefact.org. We have been using the ACA to help underwrite insurance products with this part of the act: https://www.hhs.gov/civil-rights/for-individuals/section-1557/index.html - if you have a whitepaper or reference would love to read :) talk soon!
Hi! We love your work @onefact and are happy to help if we can.
Work I helped develop during my postdoc is here: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10148336/
We have assessed several large language models for compliance with the Affordable Care Act non-discrimination clause (https://www.hhs.gov/about/leadership/melanie-fontes-rainer.html).
Specifically, the demographic parity metric is one I haven't found in your repository, and such assessment is necessary prior to training machine learning/artificial intelligence algorithms using labels derived from clinical phenotypes. For example presence or absence of a disease could be computed as a SQL query executed against a clinical data repository such as the one we work with from the NIH, researchallofus.org (@all-of-us).
Are such algorithmic fairness criteria for clinical phenotype assessment out of scope for @EqualityAI?
Please let us know as we will be releasing open source tools around this over the summer and don't want to duplicate your excellent work here!