The Threat of Algocracy: Reality, Resistance and Accommodation

By John Danaher. Philosophy & Technology, 2016

378 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Safety Research

View Paper: https://www.semanticscholar.org/paper/358658b861245cda48b9c5ea86a7ff430665271a

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Citations in the corpus

Transparency and the Black Box Problem: Why We Do Not Trust AI (2021, background): “…some scholars even have argued that the growing dependence on these systems gives rise to “the threat of algocracy—a situation in which algorithm-based systems structure and constrain the opportunities for human participation in, and comprehension of, public deci-sion-making” (Danaher, 2016, 246).”
The Illusion of Control: Placebo Effects of Control Settings (2018, background): “Relatedly, explorations of the ethics of misrepresentative control settings might fit well within emerging conversations about the ethics of algorithmic systems [5, 11, 13, 44].”
Ethics of artificial intelligence and robotics (2020, background): “(Danaher 2016b) calls this problem the ‘algocracy’.”
Algorithms at Work: The New Contested Terrain of Control (2020, background): “…rhetoric around artificial intelligence that suggests that it is fully automated, meaning that it is a technical system with no “ humans in the loop ” (Danaher, 2016), even though human curation remains essential to make most algorithmic technologies function correctly (e.g., Pine, Wolf, &…”
Algorithmic Accountability and Public Reason (2017, background): “This may lead to ‘algocracy’, in which ‘the legitimacy of public decision-making processes’ is thwarted by ‘the opacity of certain algocratic governance systems’ (Danaher, 2016).”
What to account for when accounting for algorithms: a systematic literature review on algorithmic accountability (2020, background): “For instance, as a starting point, one could pay the most attention to systems which substantially impact individuals [22, 45].”
The computational therapeutic: exploring Weizenbaum’s ELIZA as a history of the present (2018, background): “…launched by governments ‘for our own good’—and by corporations for theirs—instantiate a therapeutic relationship between computer and human, designed to change human behavior rather than assist in informing autonomous human decision-making or reasoning (Danaher 2017; Yeung 2012; Freed-man 2012).”
WeBuildAI (2019, background): “Computational algorithms increasingly take on governance and management roles in administrative and legal aspects of public and private decision-making [26, 27, 47, 79].”
Automation, Algorithms, and Beyond: Why Work Design Matters More Than Ever in a Digital World (2020, background): “…is considerable agreement that new technologies will significantly change the overall workforce structure (Brynjolfsson, Mitchell, & Rock, 2018; Danaher, 2016; Huang & Rust, 2018), with many commentators being especially concerned about the effects of digitalization on the less skilled…”
A systematic review of algorithm aversion in augmented decision making (2019, background): “More recently, however, fervent calls against opaque algocracy have occupied the limelight (e.g., Danaher, 2016; O'Neil, 2016; Pasquale, 2015).”
Fairer machine learning in the real world: Mitigating discrimination without collecting sensitive data (2017, background): “…between organisations deploying such systems and those aﬀected by them, co-operation may not be feasible or desirable; aﬀected groups may instead be drawn (understandably) to more adversarial forms of resistance and political action (Brunton and Nissenbaum, 2015; Danaher, 2016; Lyon, 2007).”