The Tyranny of Data? The Bright and Dark Sides of Data-Driven Decision-Making for Social Good
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Currently, the literature relies unilaterally on convenience datasets, often from the UCI repository or similar with limited industry context and engagement.
“Currently, the literature relies unilaterally on convenience datasets (enabling comparative studies), often from the UCI repository [13] or similar with limited industry context and engagement [193,286,287].”
From Fairness in machine learning: A survey (2024) · cites this paper as [193]
Critics of current research argue that efforts will fail unless contextual, sociocultural, and social policy challenges are better understood.
“However, beyond this, critics of current research [50,193,266,286,287,297,297,306] argue that eforts will fail unless contextual, sociocultural, and social policy challenges are better understood.”
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- Authors
- Lepri, B., Staiano, J., Sangokoya, D., Letouzé, E., & Oliver, N.
- Affiliations
- Fondazione Bruno Kessler (IT)
- MIT Media Lab (US)
- Corresponding author
- Lepri, Bruno
- Venue
- Studies in Big Data
- Publisher
- Springer International Publishing
- Published
- 2017
- Pages
- 3-24
- ISSN
- 2197-6503 (ISSN-L)
- 2197-6511
- OpenAlex
- W2558100504
- Cited by
- 111
- References
- 111
- Citations by year
- 2026: 2 · 2025: 13 · 2024: 19 · 2023: 18 · 2022: 13 · 2021: 13 · 2020: 13 · 2019: 12 · 2018: 6 · 2017: 2
- FWCI
- 44.46
- Type
- book chapter
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Studies in big data (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (submittedVersion, cc-by-nc-nd)
- arXiv (Cornell University)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Decision Sciences › Information Systems and Management › Big Data Technologies and Applications
- Social Sciences › Political Science and International Relations › E-Government and Public Services
- Keywords
- data-driven decision making, algorithmic decision-making, social good, algorithmic transparency, accountability, privacy preservation, living labs, citizen participation, public safety, access to finance, fair employment, predictive analytics, human behavior analysis
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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