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

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  • Veale, Michael

Abstract

Presented as a talk at the 4th Workshop on Fairness, Accountability and Transparency in Machine Learning (FAT/ML 2017), Halifax, Nova Scotia, Canada. Machine learning systems are increasingly used to support public sector decision-making across a variety of sectors. Given concerns around accountability in these domains, and amidst accusations of intentional or unintentional bias, there have been increased calls for transparency of these technologies. Few, however, have considered how logics and practices concerning transparency have been understood by those involved in the machine learning systems already being piloted and deployed in public bodies today. This short paper distils insights about transparency on the ground from interviews with 27 such actors, largely public servants and relevant contractors, across 5 OECD countries. Considering transparency and opacity in relation to trust and buy-in, better decision-making, and the avoidance of gaming, it seeks to provide useful insights for those hoping to develop socio-technical approaches to transparency that might be useful to practitioners on-the-ground.

Suggested Citation

  • Veale, Michael, 2017. "Logics and practices of transparency and opacity in real-world applications of public sector machine learning," SocArXiv 6cdhe, Center for Open Science.
    • Handle: RePEc:osf:socarx:6cdhe
    DOI: 10.31219/osf.io/6cdhe
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    References listed on IDEAS

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    1. Edwards, Lilian & Veale, Michael, 2017. "Slave to the Algorithm? Why a 'right to an explanation' is probably not the remedy you are looking for," LawArXiv 97upg, Center for Open Science.
    2. Aurélien Buffat, 2015. "Street-Level Bureaucracy and E-Government," Public Management Review, Taylor & Francis Journals, vol. 17(1), pages 149-161, January.
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    Cited by:

    1. Veale, Michael & Binns, Reuben, 2017. "Fairer machine learning in the real world: Mitigating discrimination without collecting sensitive data," SocArXiv ustxg, Center for Open Science.

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