Accountability in a computerized society

By Helen Nissenbaum. 1997

Subfields: AI Ethics, Fairness & Society, Social Sciences & Humanities

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Citations in the corpus

Re-imagining Algorithmic Fairness in India and Beyond (2021, background): “Nissenbaum distinguishes four barriers to responsibility in computer systems: (1) the problem of many hands, (2) bugs, (3) blaming the computer, and (4) ownership without liability [146].”
Towards Accountability for Machine Learning Datasets: Practices from Software Engineering and Infrastructure (2020, background): “Our are the dataset accounts that should be kept before ( ex ante ), during ( in medias res ) and after ( ex post ) the collection and labeling of data? e.g., sometimes considered a mere single stage of “Data Management”.”
Ethical principles in machine learning and artificial intelligence: cases from the field and possible ways forward (2020, background): “Attributing responsibilities in complicated projects where many parties and developers may be involved, an issue known as the problem of many hands (Nissenbaum, 1996), may indeed be very difficult.”
Explaining Explanations in AI (2018, background): “These issues are not unique to computerised decision-making systems, but with the growth of machine learning based systems they have become even more important (Bodo et al., 2017; Kroll et al., 2016; Nissenbaum, 1996; Olhede and Wolfe, 2018; Pasquale, 2015; Selbst and Barocas, 2018; Veale and Edward”
Give Me Convenience and Give Her Death: Who Should Decide What Uses of NLP are Appropriate, and on What Basis? (2020, background): “Two decades ago, Helen Nissenbaum sounded the alarm about automating accountability (Nissenbaum, 1996).”
Translation Tutorial : A Shared Lexicon for Research and Practice in Human-Centered Software Systems (2018, background): “This view is carefully laid out by Nissenbaum (1996), who argues that such responsibility is held back in computer systems by a number of factors related to the way these systems are designed, implemented, and fielded.”
The fallacy of inscrutability (2018, background): “And only when we consider how the mechanism functions in light of the process which created it do we truly have the information we need to assign responsibility for the system’s behaviours [32].”
Fairer machine learning in the real world: Mitigating discrimination without collecting sensitive data (2017, background): “…range of linked concerns, including algorithmic transparency and accountability (Burrell, 2016; Keats Citron and Pasquale, 2014; Kroll et al., 2016; Nissenbaum, 1996), in-the-wild reliability (Z ˇ liobaite _ et al., 2016); security against adversaries (Huang et al., 2011; McDaniel et al., 2016);…”
Accountability in algorithmic decision making (2016, background): “…collective action it will be difficult to disaggregate and assign credit to exactly who did what (or might be responsible for a particular error), 21 yet disclosure of specific human involvement would bring about social influences that both reward individuals’ reputations and reduce the risk of…”
Six Human-Centered Artificial Intelligence Grand Challenges (2023, background): “Secondly, society might encounter barriers to view human deciders, who follow the suggestion of a recommender system, as responsible as it would view a non-or merely human-advised decider (Braun et al., 2021; Nissenbaum, 1996).”
Better, Nicer, Clearer, Fairer: A Critical Assessment of the Movement for Ethical Artificial Intelligence and Machine Learning (2019, background): “[45] H. Nissenbaum.”
Beyond the individual: governing AI's societal harm (2021, unknown): “Besides the accumulative effects raised by a certain type of conduct, also the opacity of different types of (interacting) conduct by many hands can contribute to the harm and to the difficulty of identifying and addressing it (Nissenbaum, 1996).”
Algorithmic Transparency in the News Media (2017, background): “While it may be difficult to disambiguate and assign credit or blame in large collective efforts (Nissenbaum 1996), such disclosure could serve to reward individuals’ reputations and exert social pressure toward normative behavior.”
Algorithmic Decision-Making Based on Machine Learning from Big Data: Can Transparency Restore Accountability? (2017, background): “Two decades ago, Helen Nissenbaum sounded the alert about the erosion of accountability in our computerized society (Nissenbaum 1996).”
Towards Transparency by Design for Artificial Intelligence (2020, background): “This is particularly true in situations where the agency is distributed and complex (Nissenbaum 1996), such as in the case of Wiki-pedia, where dozens of individuals can contribute to an article and bots are increasingly involved in the editing process.”