The challenges and limits of big data algorithms in technocratic governance

By Marijn Janssen, George Kuk. 2016

297 citations recorded by the source corpus.

Subfields: Information Systems, Media Technology, Safety Research

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Citations in the corpus

Blockchain in government: Benefits and implications of distributed ledger technology for information sharing (2017, background): “These “algorithms become increasingly autonomous and invisible, they become harder for the public to detect and scrutinize their impartiality status” (Janssen & Kuk, 2016, p. 371).”
What to account for when accounting for algorithms: a systematic literature review on algorithmic accountability (2020, methodology): “Similarly, Fink [65, p. 1454] argues that algorithms should be made inspectable for a ‘broad range of people’, and Janssen and Kuk [82, p. 372] note that citizens should be able to scrutinize the government’s algorithms.”
Algorithmic Regulation (2019, background): “Such proposals to use algorithms to assess risk, allocate regulatory resources and target intervention and enforcement action have attracted sustained critical scrutiny in recent years (for instance Janssen and Kuk 2016; Zarsky 2016).”
How Data Science Workers Work with Data: Discovery, Capture, Curation, Design, Creation (2019, background): “Janssen and Kuk called for enhanced algorithmic transparency in “unravelling the imperceptibility, material-ity and governmentality of how algorithms work ” (em-phasis in original) [46].”
Data governance: Organizing data for trustworthy Artificial Intelligence (2020, unknown): “This approach is often advocated as a foundation for data governance (Rothstein, Borraz, & Huber, 2013) 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).”
Transparent to whom? No algorithmic accountability without a critical audience (2018, background): “The increased prominence of algorithmic models in organizational decision-making and the speed at which new data science techniques are developed and adopted, have been case for concern and has led many to call for increased transparency (Hildebrandt, 2012; Janssen & Kuk, 2016; Pasquale, 2015).”