journal article Dec 04, 2020

Introduction to special issue algorithmic transparency in government: Towards a multi-level perspective

Information Polity Vol. 25 No. 4 pp. 409-417 · SAGE Publications
View at Publisher Save 10.3233/ip-200010
Abstract
The editorial sets the stage for the special issue on algorithmic transparency in government. The papers in the issue bring together transparency challenges experienced across different levels of government, including macro-, meso-, and micro-levels. This highlights that transparency issues transcend different levels of government – from European regulation to individual public bureaucrats. With a special focus on these links, the editorial sketches a future research agenda for transparency-related challenges. Highlighting these linkages is a first step towards seeing the bigger picture of why transparency mechanisms are put in place in some scenarios and not in others. Finally, this introduction present an agenda for future research, which opens the door to comparative analyses for future research and new insights for policymakers.
Topics

No keywords indexed for this article. Browse by subject →

References
34
[1]
Agarwal "Public administration challenges in the world of AI and bots" Public Administration Review (2018) 10.1111/puar.12979
[2]
Aleksovska, M., Schillemans, T., & Grimmelikhuijsen, S. (2019). Lessons from five decades of experimental and behavioral research on accountability: a systematic literature review. Journal of Behavioral Public Administration, 2(2). 10.30636/jbpa.22.66
[3]
Ananny "Toward an ethics of algorithms: convening, observation, probability, and timeliness" Science, Technology, & Human Values (2016) 10.1177/0162243915606523
[4]
Bannister, F., & Connolly, R. (2020). Administration by algorithm: a risk management framework. Information Polity, 25(4). 10.3233/ip-200249
[5]
How the machine ‘thinks’: Understanding opacity in machine learning algorithms

Jenna Burrell

Big Data & Society 2016 10.1177/2053951715622512
[6]
Barocas "Big data’s disparate impact" California Law Review (2016)
[7]
How the machine ‘thinks’: Understanding opacity in machine learning algorithms

Jenna Burrell

Big Data & Society 2016 10.1177/2053951715622512
[8]
Accountable Artificial Intelligence: Holding Algorithms to Account

Madalina Busuioc

Public Administration Review 10.1111/puar.13293
[9]
CRITICAL QUESTIONS FOR BIG DATA

danah boyd, Kate Crawford

Information, Communication & Society 2012 10.1080/1369118x.2012.678878
[10]
Dencik, L., & Kaun, A. (2020). Datafication and the welfare state. Global Perspectives, 1(1). 10.1525/gp.2020.12912
[11]
European Union Regulations on Algorithmic Decision Making and a “Right to Explanation”

Bryce Goodman, Seth Flaxman

AI Magazine 2017 10.1609/aimag.v38i3.2741
[12]
Giest "‘For good measure’: data gaps in a big data world" Policy Sciences (2020) 10.1007/s11077-020-09384-1
[13]
To be or not to be algorithm aware: a question of a new digital divide?

Anne-Britt Gran, Peter Booth, Taina Bucher

Information, Communication & Society 10.1080/1369118x.2020.1736124
[14]
Grimmelikhuijsen "Latent transparency and trust in government: unexpected findings from two survey experiments" Government Information Quarterly (2020) 10.1016/j.giq.2020.101497
[15]
Ingrams, A. (2020). A machine learning approach to open public comments for policymaking. Information Polity, 25(4). 10.3233/ip-200256
[16]
Janssen "Big and open linked data (BOLD) in government: a challenge to transparency and privacy" Government Information Quarterly (2015) 10.1016/j.giq.2015.11.007
[17]
Jilke "Microbrook, mesobrook, macrobrook" Perspectives on Public Management and Governance (2019) 10.1093/ppmgov/gvz015
[18]
Kaminski, M.E., & Malgieri, G. (2020). Multi-layered explanations from algorithmic impact assessments in the GDPR. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, pp. 68-79. 10.1145/3351095.3372875
[19]
Kizilcec, R.F. (2016). How much information? Effects of transparency on trust in an algorithmic interface. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, pp. 2390-2395. ACM. 10.1145/2858036.2858402
[20]
Kleinberg "Human decisions and machine predictions" The Quarterly Journal of Economics (2017)
[21]
Kroll "Accountable algorithms" University of Pennsylvania Law Review (2016)
[22]
Fair, Transparent, and Accountable Algorithmic Decision-making Processes

Bruno Lepri, Nuria Oliver, Emmanuel Letouzé et al.

Philosophy & Technology 2018 10.1007/s13347-017-0279-x
[23]
Rethink government with AI

Helen Margetts, Cosmina Dorobantu

Nature 2019 10.1038/d41586-019-01099-5
[24]
Understanding the Complex Dynamics of Transparency

Albert Meijer

Public Administration Review 2013 10.1111/puar.12032
[25]
Meijer, A.J., & Grimmelikhuijsen, S.G. (2021). Responsible and Accountable Algorithmization: How to Generate Citizen Trust in Governmental Usage of Algorithms. In Schuilenburg, M., & Peeters, R. (eds.) The Algorithmic Society: Technology, Power, and Knowledge. Routledge. 10.4324/9780429261404-5
[26]
The ethics of algorithms: Mapping the debate

Brent Daniel Mittelstadt, Patrick Allo, Mariarosaria Taddeo et al.

Big Data & Society 10.1177/2053951716679679
[27]
Peeters "Machine justice: governing security through the bureaucracy of algorithms" Information Polity (2018) 10.3233/ip-180074
[29]
Roberts "Bridging levels of public administration: how macro shapes meso and micro" Administration & Society (2020) 10.1177/0095399719877160
[30]
Sandvig "When the algorithm itself is a racist: diagnosing ethical harm in the basic components of software" International Journal of Communication (2016)
[31]
Van der Voort "Rationality and politics of algorithms. Will the promise of big data survive the dynamics of public decision making" Government Information Quarterly (2019) 10.1016/j.giq.2018.10.011
[32]
Wagner "Liable, but not in control? Ensuring meaningful human agency in automated decision-making systems" Policy & Internet (2019) 10.1002/poi3.198
[33]
Young "Artificial discretion as a tool of governance: a framework for understanding the impact of artificial intelligence on public administration" Perspectives on Public Management and Governance (2019)
[34]
Zouridis, S., van Eck, M., & Bovens, M. (2020). Automated discretion. In Evans, T., & Hupe, P. (eds.) Discretion and the quest for controlled freedom, pp. 313-329. Cham: Palgrave Macmillan. 10.1007/978-3-030-19566-3_20
Metrics
24
Citations
34
References
Details
Published
Dec 04, 2020
Vol/Issue
25(4)
Pages
409-417
Cite This Article
Sarah Giest, Stephan Grimmelikhuijsen (2020). Introduction to special issue algorithmic transparency in government: Towards a multi-level perspective. Information Polity, 25(4), 409-417. https://doi.org/10.3233/ip-200010