English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Prediction systems are successfully deployed in applications ranging fromdisease diagnosis, to predicting credit worthiness, to image recognition. Evenwhen the overall accuracy is high, these systems may exhibit systematic biasesthat harm specific subpopulations; such biases may arise inadvertently due tounderrepresentation in the data used to train a machine-learning model, or asthe result of intentional malicious discrimination. We develop a rigorousframework of *multiaccuracy* auditing and post-processing to ensure accuratepredictions across *identifiable subgroups*.  Our algorithm, MULTIACCURACY-BOOST, works in any setting where we haveblack-box access to a predictor and a relatively small set of labeled data forauditing; importantly, this black-box framework allows for improved fairnessand accountability of predictions, even when the predictor is minimallytransparent. We prove that MULTIACCURACY-BOOST converges efficiently and showthat if the initial model is accurate on an identifiable subgroup, then thepost-processed model will be also. We experimentally demonstrate theeffectiveness of the approach to improve the accuracy among minority subgroupsin diverse applications (image classification, finance, population health).Interestingly, MULTIACCURACY-BOOST can improve subpopulation accuracy (e.g. for"black women") even when the sensitive features (e.g. "race", "gender") are notgiven to the algorithm explicitly.

Multiaccuracy: Black-Box Post-Processing for Fairness in Classification