Information Communication & Society · 2018 · 248 citations · 41 references
ComputingData SciencePublic PolicyAlgorithmsData EthicsScience PolicyAccountabilityAlgorithmic FairnessAlgorithmic GovernanceData GovernanceEthics Of Automated Decision-makingGovernment TransparencyAccountability Of Algorithmic SystemsAlgorithmic AccountabilityData TransparencyPolitical OrientationsSocio-technical SystemsTransparencyTransparency In Algorithmic GovernanceTransparency In Algorithms
Big data and data science are reshaping organizational decision‑making, yet the growing reliance on opaque algorithms embeds bias and obscures the subjective choices made throughout the data value chain. The authors call for greater transparency in algorithm development and use, arguing that the key question is to whom such transparency should be directed. They conclude that without a critical audience, algorithms cannot be held accountable.
Big data and data science transform organizational decision-making. We increasingly defer decisions to algorithms because machines have earned a reputation of outperforming us. As algorithms become embedded within organizations, they become more influential and increasingly opaque. Those who create algorithms may make arbitrary decisions in all stages of the 'data value chain', yet these subjectivities are obscured from view. Algorithms come to reflect the biases of their creators, can reinforce established ways of thinking, and may favour some political orientations over others. This is a cause for concern and calls for more transparency in the development, implementation, and use of algorithms in public- and private-sector organizations. We argue that one elementary – yet key – question remains largely undiscussed. If transparency is a primary concern, then to whom should algorithms be transparent? We consider algorithms as socio-technical assemblages and conclude that without a critical audience, algorithms cannot be held accountable.
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null · Philosophy study · 2016 · 28.3K citations · Full text
Judgment under Uncertainty: Heuristics and Biases
Amos Tversky, Daniel Kahneman · Science · 1974 · 28K citations
Judgment under Uncertainty: Heuristics and Biases
Amos Tversky, Daniel Kahneman · 1975 · 23.1K citations
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Mark D. Wilkinson, Michel Dumontier, IJsbrand Jan Aalbersberg et al. · Scientific Data · 2016 · 17.8K citations · Full text