Beyond the promise: implementing ethical AI
Open accessAbstract
Artificial Intelligence (AI) applications can and do have unintended negative consequences for businesses if not implemented with care. Specifically, faulty or biased AI applications risk compliance and governance breaches and damage to the corporate brand. These issues commonly arise from a number of pitfalls associated with AI development, which include rushed development, a lack of technical understanding, and improper quality assurance, among other factors. To mitigate these risks, a growing number of organisations are working on ethical AI principles and frameworks. However, ethical AI principles alone are not sufficient for ensuring responsible AI use in enterprises. Businesses also require strong, mandated governance controls including tools for managing processes and creating associated audit trails to enforce their principles. Businesses that implement strong governance frameworks, overseen by an ethics board and strengthened with appropriate training, will reduce the risks associated with AI. When applied to AI modelling, the governance will also make it easier for businesses to bring their AI deployments to scale.
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The rapid evolution of AI technologies has outpaced the formulation of comprehensive ethical guidelines, raising concerns about potential misuse and unintended consequences.
“The rapid evolution of AI technologies has outpaced the formulation of comprehensive ethical guidelines, raising concerns about the potential misuse and unintended consequences of these powerful tools [32], [33].”
From Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond (2024) · cites this paper as [33]
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“1 Framework overall structure and companies includes privacy, fairness, accountability, transparency, explainability, and safety [27, 28].”
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“Like these studies, similar benefits and risks related to AI were discussed in Eitel-Porter’s research [28].”
Ethical concerns of generative AI and mitigation strategies: A systematic mapping study
Huang, Y., Arora, C., Huong, W. C., Kanij, T., Madugalla, A. K., & Grundy, J. (2026) · Applied Soft Computing · doi:10.1016/j.asoc.2026.114789 · Cited by 21 · see citing works
From EU Robotics and AI Governance to HRI Research: Implementing the Ethics Narrative
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Lancaster, C., Schulenberg, K., Flathmann, C., McNeese, N. J., & Freeman, G. (2023) · ACM Journal on Responsible Computing · doi:10.1145/3632121 · Cited by 21 · see citing works
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Agbese, M., Alanen, H.-K., Antikainen, J., Halme, E., Isomaki, H., Jantunen, M., Kemell, K., Rousi, R. A., Vainio-Pekka, H., & Vakkur, V. (2022) · e-Informatica Software Engineering Journal · doi:10.37190/e-inf230101 · Cited by 21 · see citing works
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Leveraging responsible artificial intelligence to enhance salespeople well-being and performance
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Governance of Ethical and Trustworthy Al Systems: Research Gaps in the ECCOLA Method
Agbese, M., Alanen, H.-K., Antikainen, J., Halme, E., Isomäki, H., Jantunen, M., Kemell, K., Rousi, R. A., Vainio-Pekka, H., & Vakkuri, V. (2021) · IEEE International Requirements Engineering Conference Workshops (REW) · doi:10.1109/rew53955.2021.00042 · Cited by 18 · see citing works
“Therefore, ethics helps in engendering trust and scaling AI technology with confidence [7].”
BackgroundInfluentialJaved, H., Muqeet, H. A., Danesh, A., Rehman, A. U., Javed, T., & Bermak, A. (2024) · IEEE Access · doi:10.1109/access.2024.3443812 · Cited by 18 · see citing works
“The concept of informed consent is particularly vital when patient data is used to train AI models, as patients should understand that their data might be used to make medical predictions or aid in diagnosis [78].”
BackgroundIntegrating AI tools for enhanced autism education: a comprehensive review
Adako, O. P., Adeusi, O. C., & Alaba, P. A. (2024) · International Journal of Developmental Disabilities · doi:10.1080/20473869.2024.2392983 · Cited by 22 · see citing works
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Responsible AI (RAI) in Manufacturing: A Qualitative Framework
Besinger, P., Vejnoska, D., & Ansari, F. (2024) · Procedia Computer Science · doi:10.1016/j.procs.2024.01.081 · Cited by 16 · see citing works
The Transformative Impact of Ai On Csr, Esg, and Sustainability: Critical Review and Case Studies
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Huang, G. I., Zhang, C., Torres, W. C. Z., Xiong, X., & Li, H. (2025) · Tourism Management · doi:10.1016/j.tourman.2025.105315 · Cited by 19 · see citing works
Ye, D., Yuan, R., Luo, J., Liu, M. J., & Yannopoulou, N. (2025) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2025.124349 · Cited by 16 · see citing works
An Integrative Theoretical Framework for Responsible Artificial Intelligence
Haidar, A. (2023) · International Journal of Digital Strategy Governance and Business Transformation · doi:10.4018/ijdsgbt.334844 · Cited by 23 · see citing works
A DSL for Testing LLMs for Fairness and Bias
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“The works of several researchers [12, 14, 24], the European Commission [9], the European Union AI Act [13], and the UNESCO recommendation on the ethics of AI [38], among others, constitute this basis.”
Chatikobo, M. V., & Pasipamire, N. (2024) · Cogent Education · doi:10.1080/2331186x.2024.2425209 · Cited by 30 · see citing works
Exploring the impact of responsible AI usage on users’ behavioral intentions
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Risk as a driver for AI framework development on manufacturing
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“The trends show that around 85% of the AI projects could deliver erroneous outcomes due to bias in data, algorithms, or poor managing by the teams involved in their development and implementation [25].”
BackgroundLay User Involvement in Developing Human-centric Responsible AI Systems: When and How?
Vincenzi, B., Stumpf, S., Taylor, A. S., & Nakao, Y. (2024) · ACM Journal on Responsible Computing · doi:10.1145/3652592 · Cited by 14 · see citing works
“The options (see Appendix A.2 ) for this question were always the same at each stage, and were based on prior literature and common real-world practices (e.g., References [ 35 , 69 , 71 , 89 ]).”
MethodResponsible AI-Based Business Process Management and Improvement
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Care robot literacy: integrating AI ethics and technological literacy in contemporary healthcare
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“The convergence of these phases is evident as AI development is already characterized by its pursuit of fairness, trustworthiness and empowerment of users [59].”
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Transparency and Accountability
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Algorithmic Social Injustices: Antecedents and Mitigations
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Artificial Intelligence and Ethics Challenges of Technologies
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Business Management Transformation Through the Influence of Artificial Intelligence
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“The integration of artificial intelligence into an expanding range of business management domains necessitates ongoing scrutiny of this subject matter [4].”
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Towards a Definition of a Responsible Artificial Intelligence
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- Authors
- Eitel-Porter, R.
- Affiliations
- Accenture (United Kingdom) (GB)
- Corresponding author
- Eitel-Porter, Ray
- Venue
- AI and Ethics
- Published
- 2020-10-06
- Volume
- 1
- Issue
- 1
- Pages
- 73-80
- ISSN
- 2730-5953 (ISSN-L)
- 2730-5961
- OpenAlex
- W3092263108
- Cited by
- 245
- References
- 27
- Citations by year
- 2026: 37 · 2025: 62 · 2024: 72 · 2023: 37 · 2022: 25 · 2021: 12
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- AI and Ethics (publishedVersion, cc-by)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Social Sciences › Decision Sciences › Management Science and Operations Research › Impact of AI and Big Data on Business and Society
- Keywords
- ethical AI, AI governance, responsible AI, algorithmic bias, AI risk management, AI ethics principles, ethics boards, audit trail
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