IDEAS home Printed from https://ideas.repec.org/p/osf/socarx/6cdhe.html
   My bibliography  Save this paper

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://osf.io/download/5a103c146c613b026fd2e2ec/
    Download Restriction: no

    File URL: https://libkey.io/10.31219/osf.io/6cdhe?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Aurélien Buffat, 2015. "Street-Level Bureaucracy and E-Government," Public Management Review, Taylor & Francis Journals, vol. 17(1), pages 149-161, January.
    2. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Veale, Michael & Van Kleek, Max & Binns, Reuben, 2018. "Fairness and Accountability Design Needs for Algorithmic Support in High-Stakes Public Sector Decision-Making," SocArXiv 8kvf4, Center for Open Science.
    2. König, Pascal D. & Wenzelburger, Georg, 2021. "The legitimacy gap of algorithmic decision-making in the public sector: Why it arises and how to address it," Technology in Society, Elsevier, vol. 67(C).
    3. Huiying Zhang & Zijian Zhu, 2024. "Mobile Government Service Promotion Strategies: Exploring Sustainable Development Pathways Based on Provincial Government Practices in China," Sustainability, MDPI, vol. 16(16), pages 1-21, August.
    4. Hazel Si Min Lim & Araz Taeihagh, 2019. "Algorithmic Decision-Making in AVs: Understanding Ethical and Technical Concerns for Smart Cities," Sustainability, MDPI, vol. 11(20), pages 1-28, October.
    5. Buhmann, Alexander & Fieseler, Christian, 2021. "Towards a deliberative framework for responsible innovation in artificial intelligence," Technology in Society, Elsevier, vol. 64(C).
    6. Cobbe, Jennifer & Veale, Michael & Singh, Jatinder, 2023. "Understanding Accountability in Algorithmic Supply Chains," SocArXiv p4sey, Center for Open Science.
    7. Kirsten Martin & Ari Waldman, 2023. "Are Algorithmic Decisions Legitimate? The Effect of Process and Outcomes on Perceptions of Legitimacy of AI Decisions," Journal of Business Ethics, Springer, vol. 183(3), pages 653-670, March.
    8. Vesnic-Alujevic, Lucia & Nascimento, Susana & Pólvora, Alexandre, 2020. "Societal and ethical impacts of artificial intelligence: Critical notes on European policy frameworks," Telecommunications Policy, Elsevier, vol. 44(6).
    9. Söderlund, Kasia & Engström, Emma & Haresamudram, Kashyap & Larsson, Stefan & Strimling, Pontus, 2024. "Regulating high-reach AI: On transparency directions in the Digital Services Act," Internet Policy Review: Journal on Internet Regulation, Alexander von Humboldt Institute for Internet and Society (HIIG), Berlin, vol. 13(1), pages 1-31.
    10. Mazur Joanna, 2019. "Automated Decision-Making and the Precautionary Principle in EU Law," TalTech Journal of European Studies, Sciendo, vol. 9(4), pages 3-18, December.
    11. Daniela Sele & Marina Chugunova, 2023. "Putting a Human in the Loop: Increasing Uptake, but Decreasing Accuracy of Automated Decision-Making," Rationality and Competition Discussion Paper Series 438, CRC TRR 190 Rationality and Competition.
    12. Frederik Zuiderveen Borgesius & Joost Poort, 2017. "Online Price Discrimination and EU Data Privacy Law," Journal of Consumer Policy, Springer, vol. 40(3), pages 347-366, September.
    13. Kira J.M. Matus & Michael Veale, 2022. "Certification systems for machine learning: Lessons from sustainability," Regulation & Governance, John Wiley & Sons, vol. 16(1), pages 177-196, January.
    14. Larisa Găbudeanu & Iulia Brici & Codruța Mare & Ioan Cosmin Mihai & Mircea Constantin Șcheau, 2021. "Privacy Intrusiveness in Financial-Banking Fraud Detection," Risks, MDPI, vol. 9(6), pages 1-22, June.
    15. Rolf H. Weber, 2021. "Artificial Intelligence ante portas: Reactions of Law," J, MDPI, vol. 4(3), pages 1-14, September.
    16. I. Ooijen & Helena U. Vrabec, 2019. "Does the GDPR Enhance Consumers’ Control over Personal Data? An Analysis from a Behavioural Perspective," Journal of Consumer Policy, Springer, vol. 42(1), pages 91-107, March.
    17. Janssen, Patrick & Sadowski, Bert M., 2021. "Bias in Algorithms: On the trade-off between accuracy and fairness," 23rd ITS Biennial Conference, Online Conference / Gothenburg 2021. Digital societies and industrial transformations: Policies, markets, and technologies in a post-Covid world 238032, International Telecommunications Society (ITS).
    18. Matus, Kira & Veale, Michael, 2021. "Certification Systems for Machine Learning: Lessons from Sustainability," SocArXiv pm3wy, Center for Open Science.
    19. Plantinga, Paul, 2022. "Digital discretion and public administration in Africa: Implications for the use of artificial intelligence," SocArXiv 2r98w, Center for Open Science.
    20. Vasiliki Koniakou, 2023. "From the “rush to ethics” to the “race for governance” in Artificial Intelligence," Information Systems Frontiers, Springer, vol. 25(1), pages 71-102, February.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:osf:socarx:6cdhe. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: OSF (email available below). General contact details of provider: https://arabixiv.org .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.