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

Machine learning systems are increasingly used to support public sectordecision-making across a variety of sectors. Given concerns aroundaccountability in these domains, and amidst accusations of intentional orunintentional bias, there have been increased calls for transparency of thesetechnologies. Few, however, have considered how logics and practices concerningtransparency have been understood by those involved in the machine learningsystems already being piloted and deployed in public bodies today. This shortpaper distils insights about transparency on the ground from interviews with 27such actors, largely public servants and relevant contractors, across 5 OECDcountries. Considering transparency and opacity in relation to trust andbuy-in, better decision-making, and the avoidance of gaming, it seeks toprovide useful insights for those hoping to develop socio-technical approachesto transparency that might be useful to practitioners on-the-ground.  An extended, archival version of this paper is available as Veale M., VanKleek M., & Binns R. (2018). `Fairness and accountability design needs foralgorithmic support in high-stakes public sector decision-making' Proceedingsof the 2018 CHI Conference on Human Factors in Computing Systems (CHI'18),http://doi.org/10.1145/3173574.3174014.

Logics and practices of transparency and opacity in real-world applications of public sector machine learning