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/

Zendy Plus

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

Zendy Tools

Zendy Open

As more industries integrate machine learning into socially sensitivedecision processes like hiring, loan-approval, and parole-granting, we are atrisk of perpetuating historical and contemporary socioeconomic disparities.This is a critical problem because on the one hand, organizations who use butdo not understand the discriminatory potential of such systems will facilitatethe widening of social disparities under the assumption that algorithms arecategorically objective. On the other hand, the responsible use of machinelearning can help us measure, understand, and mitigate the implicit historicalbiases in socially sensitive data by expressing implicit decision-making mentalmodels in terms of explicit statistical models. In this paper we specify,implement, and evaluate a "fairness-aware" machine learning interface calledthemis-ml, which is intended for use by individual data scientists andengineers, academic research teams, or larger product teams who use machinelearning in production systems.

Themis-ml: A Fairness-aware Machine Learning Interface for End-to-end Discrimination Discovery and Mitigation