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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!
The text was updated successfully, but these errors were encountered:
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!
The text was updated successfully, but these errors were encountered: