Cited by 52Ethics and Social Impacts of AIExplainable Artificial Intelligence (XAI)Privacy-Preserving Technologies in Data

Considerations for a more ethical approach to data in AI: On data representation and infrastructure

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Baird, Alice, Schuller, Björn·2020·Frontiers in Big Data

Abstract

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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Research works printed but not linked: 3

  1. Diakopoulos N. Friedler S. (2017). How to Hold Algorithms Accountable. MIT Technology Review. Available online at: http://bit.ly/2f8Iple
  2. Nikolova G. Kotev V. Dantchev D. Kiriazov P. (2018). Basic inertial characteristics of human body by walking, in Proceedings of The 15th International Symposium on Computer Methods in Biomechanics and Biomedical Engineering and the 3rd Conference on Imaging and Visualization, CMBBE (Lisbon), 26– 29.
  3. Trajanov D. Zdraveski V. Stojanov R. Kocarev L. (2018). Dark data in internet of things (IOT): challenges and opportunities, in 7th Small Systems Simulation Symposium (Niš), 1– 8.

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Authors
Baird, A., & Schuller, B.
Affiliations
University of Augsburg (DE)
Corresponding author
Baird, Alice
Venue
Frontiers in Big Data
Published
2020-09-02
Volume
3
Pages
25-25
ISSN
2624-909X (ISSN-L)
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
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

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