AI management an exploratory survey of the influence of GDPR and FAT principles
Open accessAbstract
As organizations increasingly adopt AI technologies, a number of ethical issues arise. Much research focuses on algorithmic bias, but there are other important concerns arising from the new uses of data and the introduction of technologies which may impact individuals. This paper examines the interplay between AI, Data Protection and FAT (Fairness, Accountability and Transparency) principles. We review the potential impact of the GDPR and consider the importance of the management of AI adoption. A survey of data protection experts is presented, the initial analysis of which provides some early insights into the praxis of AI in operational contexts. The findings indicate that organizations are not fully compliant with the GDPR, and that there is limited understanding of the relevance of FAT principles as AI is introduced. Those organizations which demonstrate greater GDPR compliance are likely to take a more cautious, risk-based approach to the introduction of AI.
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AI hiring tools may introduce bias, particularly when hiring decisions rely heavily on automated outputs.
“While such systems can reduce turnaround time, prior research suggests that their routine use may introduce bias, particularly when hiring decisions rely heavily on automated outputs.”
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 [10]
The paper’s FAT section (page not provided) says that automating existing decision processes can replicate biases and suggests human oversight to improve fairness. It supports a general concern about biased automated decisions, but does not specifically address hiring tools or show that bias is particularly likely when hiring decisions rely heavily on automated outputs. (LLM, conf=0.86)
Experimental — accuracy not yet measuredWithout transparency in AI decision-making processes has raised concerns about accountability, responsibility, ethics, and fairness as management may not be fully aware of decision-making.
“Moreover, without transparency in AI decision-making processes has raised concerns about accountability, responsibility, ethics, and fairness as the management may not be fully aware of the decisionmaking [10].”
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 [10]
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Chopra, Y., & Hashemi, S. (2026) · 2026 ASEE- NE Section Conference Proceedings · doi:10.18260/1-2--58593
- Authors
- Addis, C., & Kutar, M.
- Affiliations
- University of Salford (GB)
- Venue
- 2019 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI)
- Publisher
- IEEE
- Published
- 2019-08
- Pages
- 342-347
- OpenAlex
- W3015436428
- Cited by
- 11
- References
- 1
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 4 · 2023: 4
- FWCI
- 0.00
- Type
- conference paper
- Language
- en
- Open access
- green
- usir.salford.ac.uk
- Licence
- Locations
- IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI) (publishedVersion)
- University of Salford Institutional Repository (University of Salford) (submittedVersion, cc-by)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Law › Digitalization, Law, and Regulation
- Physical Sciences › Computer Science › Artificial Intelligence › Law, AI, and Intellectual Property
- Keywords
- AI management, GDPR compliance, fairness, accountability, and transparency, data protection, AI ethics, algorithmic bias, artificial intelligence adoption, risk-based approach, regulatory compliance
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