More accountability for big-data algorithms
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It is commonly expressed that extensive transparency might encourage system gaming, and this is used as justification for opacity.
“It is commonly expressed that extensive transparency of algorithms to the public might encourage system gaming [19], and this is brought to bear as a justification for opacity.”
From Fairness and Accountability Design Needs for Algorithmic Support in High-Stakes Public Sector Decision-Making (2018) · cites this paper as [19]
This kind of model generalization on the testing dataset is poor, and it can lead the model to prefer a dataset with more samples or samples with a low identification degree.
“This kind of model generalization on the testing dataset is poor, it can lead the model to prefer a dataset with more samples or samples with a low identification degree [41].”
From An Improved VGG19 Transfer Learning Strip Steel Surface Defect Recognition Deep Neural Network Based on Few Samples and Imbalanced Datasets (2021) · cites this paper as [41]
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Veale, M., Van Kleek, M., & Binns, R. (2018) · Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3174014 · Cited by 490 · see citing works
Measuring discrimination in algorithmic decision making
Žliobaitė, I. (2017) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-017-0506-1 · Cited by 272 · see citing works
“…as well as decisions informed by big data has raised public awareness that data-driven decision making may lead to discrimination against groups of people (Angwin and Larson 2016; Burn-Murdoch 2013; Corbett-Davies et al. 2016; Dwoskin 2015; Nature Editorial 2016; The White House 2016; Miller 2015).”
BackgroundInfluentialWan, X., Zhang, X., & Liu, L. (2021) · Applied Sciences · doi:10.3390/app11062606 · Cited by 90 · see citing works
A brief review on algorithmic fairness
Wang, X., Zhang, Y., & Zhu, R. (2022) · Management System Engineering · doi:10.1007/s44176-022-00006-z · Cited by 73 · see citing works
“…refers to a set of techniques that use computing or statistical power to answer questions of fairness in market and business (Lambrecht and Tucker 2019; Zhang 2018; Zhang et al. 2019; Kallus et al. 2022), politics and law (Teodorescu et al. 2021; Chen et al. 2021), and public affairs (Editorial.”
BackgroundVeale, M., Van Kleek, M., & Binns, R. (2018) · doi:10.31235/osf.io/8kvf4 · Cited by 60 · see citing works
An Argumentation-Based Perspective Over the Social IoT
Lippi, M., Mamei, M., Mariani, S., & Zambonelli, F. (2017) · IEEE Internet of Things Journal · doi:10.1109/jiot.2017.2775047 · Cited by 47 · see citing works
Nordic lights? National AI policies for doing well by doing good
Dexe, J., & Franke, U. (2020) · Journal of Cyber Policy · doi:10.1080/23738871.2020.1856160 · Cited by 32 · see citing works
Metcalfe, B., Boshuizen, H., Bulens, J., & Koehorst, J. (2023) · F1000Research · doi:10.12688/f1000research.137262.1 · Cited by 29 · see citing works
Dexe, J., Franke, U., & Rad, A. (2021) · The Geneva Papers on Risk and Insurance Issues and Practice · doi:10.1057/s41288-021-00207-9 · Cited by 21 · see citing works
“Thus, the public debate is full of concern about the various forms of bias in automated decision-making that have been discovered, disadvantaging, for example, poorer people and those from minorities (Nature 2016).”
BackgroundMulti-chaos, fractal and multi-fractional AI in different complex systems
Karaca, Y. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-90032-4.00016-x · Cited by 23 · see citing works
Rawls’s Original Position and Algorithmic Fairness
Franke, U. (2021) · Philosophy & Technology · doi:10.1007/s13347-021-00488-x · Cited by 17 · see citing works
“In particular, it has been discovered that automated decisions sometimes disadvantage, e.g., poorer people and those from minorities (Nature, 2016), raising questions about fairness.”
BackgroundCyber situational awareness issues and challenges
Franke, U., Andreasson, A., Artman, H., Brynielsson, J., Varga, S., & Vilhelm, N. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-90570-1.00015-2 · Cited by 17 · see citing works
First- and Second-Level Bias in Automated Decision-making
Franke, U. (2022) · Philosophy & Technology · doi:10.1007/s13347-022-00500-y · Cited by 16 · see citing works
“Furthermore, many dis-concerting discoveries have been made of cases where systems have turned out to be biased in various ways, disadvantaging, e.g., poorer people and those from minorities (Nature, 2016).”
BackgroundRequirements on and Procurement of Explainable Algorithms—A Systematic Review of the Literature
Franke, U., Hallström, C. H., Artman, H., & Dexe, J. (2024) · Advances in intelligent systems and computing · doi:10.1007/978-3-031-66635-3_4 · Cited by 4 · see citing works
Coordination in Socio-technical Systems: Where are we now? Where do we go next?
Mariani, S. (2019) · Science of Computer Programming · doi:10.1016/j.scico.2019.102317 · Cited by 4 · see citing works
Optimization hierarchy for fair statistical decision problems
Aswani, A. J., & Olfat, M. (2022) · The Annals of Statistics · doi:10.1214/22-aos2217 · Cited by 3 · see citing works
Coordination of Complex Socio-Technical Systems: Challenges and Opportunities
Mariani, S. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-04771-9_22 · Cited by 2 · see citing works
On Learning and Testing of Counterfactual Fairness through Data Preprocessing
Chen, H., Lu, W., Song, R., & Ghosh, P. (2023) · Journal of the American Statistical Association · doi:10.1080/01621459.2023.2186885 · Cited by 2 · see citing works
Zhang, K. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-981-33-4572-0_64 · Cited by 1 · see citing works
Reflexivity and positionality statements: a way to tackle “second-level arbitrariness” bias in AI?
Conti, L. G., & Seele, P. (2025) · Humanities and Social Sciences Communications · doi:10.1057/s41599-025-06208-6 · Cited by 3 · see citing works
“This uncertainty triggers a self-regulatory mechanism referred to as the panopticon effect (Downing, 2010; Seele, 2016): inmates adjust their behaviour as if they are constantly under surveillance.”
Politics and Ethics in the Age of Algorithms
(2020) · Duke University Press eBooks · doi:10.1215/9781478009276-001 · Cited by 1 · see citing works
Intelligence’s Dilemma: AI Algorithms May Cause Anti-Intelligence
Zhang, Y., & Nauman, U. (2019) · Communications in computer and information science · doi:10.1007/978-981-13-7983-3_34
(2020) · Duke University Press eBooks · doi:10.1215/9781478009276-010
(2020) · Duke University Press eBooks · doi:10.1215/9781478009276-012
(2020) · Duke University Press eBooks · doi:10.1215/9781478009276-003
(2020) · Duke University Press eBooks · doi:10.1215/9781478009276-009
(2020) · Duke University Press eBooks · doi:10.1215/9781478009276-004
(2020) · Duke University Press eBooks · doi:10.1215/9781478009276-006
(2020) · Duke University Press eBooks · doi:10.1215/9781478009276-007
(2020) · Duke University Press eBooks · doi:10.1215/9781478009276-011
How Do ML Students Explain Their Models and What Can We Learn from This?
Franke, U. (2025) · Lecture notes in business information processing · doi:10.1007/978-3-031-85849-9_28
- Venue
- Nature
- Published
- 2016-09-21
- Volume
- 537
- Issue
- 7621
- Pages
- 449-449
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- OpenAlex
- W4246615335
- Cited by
- 31
- References
- 0
- Citations by year
- 2025: 2 · 2024: 1 · 2023: 2 · 2022: 5 · 2021: 3 · 2020: 11 · 2019: 2 · 2018: 3 · 2017: 2
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Social Sciences › Law › Digitalization, Law, and Regulation
- Keywords
- algorithmic accountability, big data algorithms, algorithmic bias, algorithmic transparency, public data sources, algorithmic fairness
- MeSH terms
- Algorithms, Humans, Social Responsibility
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