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

Optimizing prediction accuracy can come at the expense of fairness. Towardsminimizing discrimination against a group, fair machine learning algorithmsstrive to equalize the behavior of a model across different groups, by imposinga fairness constraint on models. However, we show that giving the sameimportance to groups of different sizes and distributions, to counteract theeffect of bias in training data, can be in conflict with robustness. We analyzedata poisoning attacks against group-based fair machine learning, with thefocus on equalized odds. An adversary who can control sampling or labeling fora fraction of training data, can reduce the test accuracy significantly beyondwhat he can achieve on unconstrained models. Adversarial sampling andadversarial labeling attacks can also worsen the model's fairness gap on testdata, even though the model satisfies the fairness constraint on training data.We analyze the robustness of fair machine learning through an empiricalevaluation of attacks on multiple algorithms and benchmark datasets.

On Adversarial Bias and the Robustness of Fair Machine Learning