Considerations for a more ethical approach to data in AI: On data representation and infrastructure
Open accessAbstract
Data forms the development of Artificial Intelligence (AI) as we currently know it, and for many years centralised networking infrastructures have dominated both the sourcing and subsequent use of such data. Research suggests that centralised approaches result in poor representation, and as AI is now integrated more in daily life, there is a need for efforts to improve on this. The AI research community has now begun to explore managing data infrastructures more democratically, finding that decentralised networking allows for more transparency and therefore can alleviate core ethical concerns such as selection-bias. With this in mind, herein, we present a survey-based overview framed around data representation and data infrastructures in AI. We outline four key considerations (auditing, benchmarking, confidence and trust, explainability and interpretability) as they pertain to data-driven AI, and propose that incorporation of these aspects, along with improved interdisciplinary discussion may aid the mitigation of a plethora of ethical concerns relating to data and AI.
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It is difficult for hiring managers to explain selection/rejection when there is no clear accountability, which may reduce trust in AI tool and point out need for ethical governance systems.
“It is difficult for hiring managers to explain the reason for the selection or rejection of the candidate when there is no clear accountability, which may reduce trust in the AI tool and point out the need for ethical governance systems [11].”
From Mitigating Bias in Retail AI Hiring Tools: A Human-Centered H-A-I-R-E Governance Framework for Low-Wage Hires (2026) · cites this paper as [11]
Section 4.4 (page unavailable) says that explainability concerns why AI decisions were made and that, without an explanation, results are harder to substantiate. Sections 4.3 and 7 also discuss trust and ethical considerations. The paper does not establish the specific claim about hiring managers explaining candidate selection or rejection when accountability is unclear. (LLM, conf=0.84)
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Barclay, I., Preece, A. D., & Taylor, I. (2018) · ORCA Online Research @Cardiff (Cardiff University) · doi:10.48550/arxiv.1809.09444 · Cited by 8 · see citing works
Controlling quality and handling fraud in large scale crowdsourcing speech data collections
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Clustering of Tweets: A Novel Approach to Label the Unlabelled Tweets
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Stappen, L., Baird, A., Rizos, G., Tzirakis, P., Du, X., Hafner, F., Schumann, L., Mallol-Ragolta, A., Schuller, B. W., Lefter, I., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.14858 · Cited by 5 · see citing works
Self-citationMachine Learning and Artificial Intelligence
Schneider, D. F. (2020) · Success in academic surgery · doi:10.1007/978-3-030-28357-5_14 · Cited by 4 · see citing works
Blass, J. A. (2018) · AI Matters · doi:10.1145/3175502.3175512 · Cited by 3 · see citing works
Transhumanism and Distributed Ledger Technologies
Smith, C. J. (2019) · Springer eBooks · doi:10.1007/978-3-030-16920-6_34 · Cited by 3 · see citing works
Private Data Aggregation in Decentralized Networks
Setia, P. K., Tillem, G., & Erkin, Z. (2019) · International Istanbul Smart Grids and Cities Congress and Fair (ICSG) · doi:10.1109/sgcf.2019.8782377 · Cited by 3 · see citing works
Threat Detection on Twitter Using Corpus Linguistics
Beach, A. (2019) · ScholarWorks -A service of University of Vermont Libraries (University of Vermont) · Cited by 2 · see citing works
Spam User Detection Through Deceptive Images in Big Data
Zafar, S., Irum, N., Arshad, S., & Nawaz, T. (2019) · EAI/Springer Innovations in Communication and Computing · doi:10.1007/978-3-319-99966-1_28 · Cited by 1 · see citing works
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On the Texture Bias for Few-Shot CNN Segmentation
Azad, R., Fayjie, A. R., Kauffman, C., Ayed, I. B., Pedersoli, M., & Dolz, J. (2020) · arXiv · doi:10.48550/arxiv.2003.04052
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Social Dangers of Generative Artificial Intelligence: Review and Guidelines
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Bouhouita-Guermech, S., & Haidar, H. (2024) · Asian Bioethics Review · doi:10.1007/s41649-024-00292-7 · Cited by 10 · see citing works
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- Authors
- Baird, A., & Schuller, B.
- Affiliations
- University of Augsburg (DE)
- Corresponding author
- Baird, Alice
- Venue
- Frontiers in Big Data
- Publisher
- Frontiers Media SA
- Published
- 2020-09-02
- Volume
- 3
- Pages
- 25-25
- ISSN
- 2624-909X (ISSN-L)
- PMC
- PMC7931893
- OpenAlex
- W3082892415
- Cited by
- 46
- References
- 121
- Citations by year
- 2026: 5 · 2025: 13 · 2024: 12 · 2023: 10 · 2022: 2 · 2021: 4
- FWCI
- 0.78
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Big Data (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- OPUS (Augsburg University) (submittedVersion, cc-by)
- Funders
- Bayerisches Staatsministerium für Bildung und Kultus, Wissenschaft und Kunst
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Keywords
- AI ethics, data representation, data infrastructure, selection bias, data auditing, benchmarking, trust in AI, explainability, interpretability, transparency
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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