FairBench

This is a comprehensive AI fairness exploration framework developed by the MAMMOth project. Visit the overview, read the documentation, or try lightweight features in your browser below. Consider starring or submit issues in GitHub.

▶ Run 🔄 Clear Powered by pyodyne # some sensitive attribute with values M,F,O, binary target # labels y, and predicted ones yhat (see docs for multiclass) s = ["M","F","M","F","M","F","M", "O", "O"] y = [1, 1, 0, 0, 1, 0, 1, 1, 0] yhat = [1, 1, 1, 0, 0, 0, 0, 1, 0] # one of the patterns to unpack sensitive attributes into dimensions s = fb.Dimensions(fb.categories @ s) # a fairness report for base measures (all applicable to the # arguments) computed and compared across all groups report = fb.reports.pairwise(predictions=yhat, labels=y, sensitive=s) # show in an environment (default: Console) & focus on max differences # report.help() shows what to focus on report.show(env=fb.export.ConsoleTable(legend=False)) report.maxdiff.show(env=fb.export.Console(ansiplot=False))