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

Recently there are increasing concerns about the fairness of ArtificialIntelligence (AI) in real-world applications such as computer vision andrecommendations. For example, recognition algorithms in computer vision areunfair to black people such as poorly detecting their faces and inappropriatelyidentifying them as "gorillas". As one crucial application of AI, dialoguesystems have been extensively applied in our society. They are usually builtwith real human conversational data; thus they could inherit some fairnessissues which are held in the real world. However, the fairness of dialoguesystems has not been well investigated. In this paper, we perform a pioneeringstudy about the fairness issues in dialogue systems. In particular, weconstruct a benchmark dataset and propose quantitative measures to understandfairness in dialogue models. Our studies demonstrate that popular dialoguemodels show significant prejudice towards different genders and races. Besides,to mitigate the bias in dialogue systems, we propose two simple but effectivedebiasing methods. Experiments show that our methods can reduce the bias indialogue systems significantly. The dataset and the implementation are releasedto foster fairness research in dialogue systems.

Does Gender Matter? Towards Fairness in Dialogue Systems