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The challenges and limits of big data algorithms in technocratic governance
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“and an effective solution to AI-specific risks such as data or algorithmic errors, data or algorithmic bias, or even data-embedded discrimination (Janssen & Kuk, 2016).”
From Data governance: Organizing data for trustworthy Artificial Intelligence (2020) · cites this paper as Janssen & Kuk, 2016
Citations
Cites
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“The reliance of algorithm (software analytics) to process and interpret vast amount of data from location sensors and other offline and online data collection devices reflect the “accumulation logic” of big data (Zuboff, 2015).”
MethodThe real-time city? Big data and smart urbanism
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- Barocas, 2016, Big Data's Disparate Impact (August 14, 2015), California Law Review, 104
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“As algorithmic decision-making has become more prevalent, so have calls for algorithmic transparency (Pascquale 2015; Diakopoulos 2016; Janssen and Kuk 2016).”
BackgroundRe-envisioning the Role of “Big Data” in the Nonprofit Sector: A Data Feminist Perspective
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Longo, J., & Dobell, A. R. (2018) · Politics and Governance · doi:10.17645/pag.v6i4.1561 · Cited by 5
“As algorithms become more widely used, increasingly autonomous, and invisible to those affected by their decisions, their status as impartial public servants becomes more difficult to monitor for bias or discrimination (Janssen & Kuk, 2016b).”
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“…to each other (Neyland, 2015), as their calculation affect people, organizational processes and places (Kitchin, 2017), but they are often inaccessible to public scrutiny and enact different grades of inclusion and exclusion (Gillespie, 2014; Neyland, 2015; Pasquale, 2015; Janssen and Kuk, 2016).”
BackgroundThe Role of Digital Transformation in Enhancing Transparency in AI-Assisted Public Administration
Ouboumlik, A., Khafif, H., Touhami, N. O., & Baiza, I. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-1737-3.ch007 · Cited by 3
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“A medida que los algoritmos “se vuelvan más autónomos e invisibles, para el público se volverán más difíciles de detectar [así como de] escrutar su imparcialidad” (Janssen & Kuk, 2016, p. 371).”
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Metadata
- Authors
- Janssen, M., & Kuk, G.
- Affiliations
- Delft University of Technology (NL)
- Nottingham Trent University (GB)
- Corresponding author
- Janssen, Marijn
- Venue
- Government Information Quarterly
- Publisher
- Elsevier BV
- Published
- 2016-07
- Volume
- 33
- Issue
- 3
- Pages
- 371-377
- ISSN
- 0740-624X (ISSN-L)
- OpenAlex
- W2527349292
- Cited by
- 363
- References
- 32
- Citations by year
- 2026: 64 · 2025: 42 · 2024: 43 · 2023: 31 · 2022: 48 · 2021: 47 · 2020: 39 · 2019: 27 · 2018: 16 · 2017: 5 · 2016: 1
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Government Information Quarterly (publishedVersion)
- Nottingham Trent University's Institutional Repository (Nottingham Trent Repository) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Social Sciences › Political Science and International Relations › E-Government and Public Services
- Social Sciences › Urban Studies › Smart Cities and Technologies
- Keywords
- big data algorithms, technocratic governance, algorithmic bias, algorithmic accountability, governmentality, algorithmic transparency, data curation, algorithmic discrimination, institutional logics, algorithmic governance
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