Bearing Account-able Witness to the Ethical Algorithmic System

By Daniel Neyland. 2016

121 citations recorded by the source corpus.

Subfields: Computer Science Applications, Safety Research, Sociology and Political Science

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Citations in the corpus

The ethics of algorithms: Mapping the debate (2016, neutral): “Algorithms, particularly those embedded in robotics, can for instance be made safely interruptible insofar as harmful actions can be discouraged without the algorithm being encouraged to deceive human users to avoid further interruptions (Orseau and Armstrong, 2016).”
The Challenges of Algorithm-Based HR Decision-Making for Personal Integrity (2019, methodology): “Given the complexity of these decisions and the vast number of variables molded into such an analysis it becomes virtually impossible for a human being to understand exactly how the algorithm proceeded and how it modeled information into a decision (Ananny 2016; Neyland 2015; Stohl et al. 2016).”
We get the algorithms of our ground truths: Designing referential databases in digital image processing (2017, background): “…(Muniesa, 2011), the performativity of a text-matching program for academic essays (Introna, 2016) and the implementation of an airport security system (Neyland, 2016) participate in deflating the ‘algorithmic drama’ (Ziewitz, 2016) and challenging it with more nuanced and grounded narratives.”
Ethical Implications and Accountability of Algorithms (2018, background): “At the other end of the spectrum is a more deterministic argument, whereby algorithms are controlling yet obscure, powerful yet inscrutable (Neyland 2016; Ziewitz 2016) and veer toward algorithms as beyond our control and the primary actors.”
Trustworthy Artificial Intelligence: A Review (2022, background): “Accountability of algorithms is an incremental process [18], requiring proper governance of the AI lifecycle and discussion between different stakeholders [144].”
Algorithmic Transparency in the News Media (2017, background): “Others have made inroads to studying the ethics of algorithmic systems through ethnographic techniques that allow for thick description of the dynamic human design processes around algorithmic system development (Neyland 2016).”
When the algorithm itself is a racist: Diagnosing ethical harm in the basic Components of Software (2016, background): “This is similar to the system described by Neyland (2016).”
Machine learning, social learning and the governance of self-driving cars (2018, background): “As computer entrepreneur-turned-academic Kevin Slavin puts it: ‘We’re writing things we can no longer read’ (quoted in Neyland, 2016).”
Towards Transparency by Design for Artificial Intelligence (2020, background): “…impact assessments) or ex-post ones (e.g., analysis of the impact of a final decision) have been criticized to as not modular enough and not taking the entire process (design, implementation, evaluation) into account (Wieringa with reference to Neyland 2016; Diakopoulos 2016; Kroll et al. 2016).”
What to account for when accounting for algorithms: a systematic literature review on algorithmic accountability (2020, methodology): “As he illustrates [110, p. 68]: Accountability was not accomplished in a single moment, by a single person, but instead was distributed among project members and the ethics board and across ongoing activities, with questions taken back to the project team between meetings and even to be carried…”
Algorithmic IF … THEN rules and the conditions and consequences of power (2017, methodology): “We have sought to extend existing work on algorithmic systems (Gillespie, 2011; Neyland, 2015) by drawing on the work of Latour (2005), to explore a particular approach to power.”
Algorithmic surveillance: the collection conundrum (2017, methodology): “In other surveillance contexts, algorithms have been used to minimise data retention and to restrict access to data deemed irrelevant to the legitimate purpose of detecting suspicious behaviour (Neyland 2016).”
Algorithms and their others: Algorithmic culture in context (2016, background): “As Neyland (2016) notes, the danger to be guarded against here is taking an essentializing view of algorithms.”