Connecting Business Ethics to Institutionalize Business AI Ethics

By: Ankita Manohar Walawalkar, Department of Business Administration, Asia University, Taiwan;


In AI system’s response to the rapid growth transformed discourse on AI ethics has arisen. This article aims to increase awareness for the growth of AI ethics by moving knowledge from business ethics, accenting business roles and the institutionalization procedure. This discussion centres around ethical concepts, theories, and application contexts. Along with this article highlights present challenges in AI ethics and explores possible visions from business ethics. Each knowledge bridge is detailed, showcasing the transferability of concepts.


Business ethics is defined as the inspection of activities, corporate circumstances, and decisions connecting ethical considerations of correct and incorrect. Business ethics has developed a vigorously, addressing corporate scandals and executing positive business ethics [1]. The thoughts have led to the ratification of obligatory laws, assisting business practices on a national and international scale. Understandings from the achievement and disappointments of institutionalizing business ethics over recent years can deliver valuable teachings for AI ethics. key ideas to advance the discussion and institutionalization of AI ethics includes Stakeholder managing, reporting on non-financial digital problems, corporate governance and regulation, AI/tech ethics in tertiary education, and the main topic of Greenwashing and digital ethics washing [2].

Fig 1: Ethics in AI

Five business ethics ranges to advance AI ethics:

A. Stakeholder management

Looking at the challenges encounter by AI ethics boards, considered as ‘ethics washing,’ suggests a stakeholder management method to methodically handle AI’s ethical issues. While admitting analyses, the article advocates for the request of stakeholder theory in AI ethics, accenting its flexibility [3]. It stresses the essential for proper stakeholder engagement in AI ethics, going outside superficial ethical actions. Utilizing stakeholder theory can push practical approaches in AI ethics, providing visions into the theory of the firm and varied stakeholder explanations in digital frameworks[4].

B. Reporting on non‑financial digital issues

AI ethics goes through a fundamental challenge entrenched in the imperviousness and complication immediate algorithms and third-party data gathering. Working similar with the development of business ethics reporting, this article advocates for transparent disclosure in AI ethics reporting, bring into line with Corporate Social Responsibility (CSR) standards [5]. Though CSR reporting has become uniform and extensively accepted, AI ethics reporting can influence these insights to address the ‘what and how’ of reporting around AI systems. In spite of challenges, motivating deliberations on the solemnization of AI ethics information disclosure remains critical for experts and academics [2].

C. Corporate governance and regulation

In AI ethics, the formation of soft-law strategies by companies over 200 soft-law guidelines have arisen in the past five years, concentrating on values like transparency, justice, non-maleficence, privacy, and responsibility, raise fears about their imprecision and lack of ethical authority [6] [7]. As AI ethics faces increasing legislative focus, mainly in the European Union, there’s a parallel with business ethics, where past involvements with self-regulation offer valuable understandings. Overall, AI ethics stances to benefit from the knowledges and outlines recognized in business ethics [1].

D. AI/Tech ethics in tertiary education

The AI enlarges its inspiration, the imperative to fit in ethics into advanced education for software developers and AI experts becomes critical. However, global AI/tech ethics paths display a lack of reliability and values, like to the historical evolution of business ethics education. In the U.S., authorization values already mandate a thoughtful of ethical issues in computer science programs [8]. AI ethics education can influence diverse literature, pioneering approaches, and enlightening technologies, positioning with the developing regulatory landscape. Future call for institutionalized AI ethics education is expected, possibly reflecting initiatives like “no ethics, no grant” to safeguard compliance and authorization standards [2].

E. Greenwashing and ethics washing

AI ethics is facing a concern about its credibility and moral authority. The terms like “ethical AI” or “responsible AI” are possible manipulations by Big Tech to evade regulation, leading to the inventing of terms like “ethics washing” and “machine-washing.” This approach involves establishments engaging in ambiguous behaviour about ethical AI. “Ethics bashing” analyses the trivialization of ethics as separate tools or erections. Notwithstanding these challenges, applied ethics offers deep insights and tools for averting unethical behaviour in corporate contexts [9]. The reputational mutilation from ethics washing underlines parallels between AI ethics and business ethics, see-through an instrumentalization of societal morals for marketplace dominance and regulatory inspiration, similar to greenwashing practices in business ethics. Leveraging acumens from greenwashing research can inform the study of AI ethics washing and deceptive practices by technology companies [2] [9][10].

Fig 2. Five business ethics ranges to advance AI ethics


The article aims to raise consciousness and contribute to the rising discourse on AI ethics by extending thoughts on business ethics. Additionally, its need to highlight the AI ethics with other applied ethics fields, such as medicine and business, the manuscript underscores the potential for collaborative discussions to address shared challenges and foster a multidisciplinary approach. One of the notable limitations is that the application of ethics may not completely remove unethical practices in AI or business. Furthermore, there are challenges in pick up the check AI as a legal person, restrictive its accountability in contrast to corporations. The future research endeavours to design effective governance mechanisms that bond ethical codes with real enactment, safeguarding ethical values interpret into actual practices in AI and expertise.


  1. Ramachandran, Ramakrishnan, Inter-Relationship between Business Ethics and Corporate Governance Among Indian Companies (January 6, 2007). International Seminar At The Institute of Management, NIRMA University, Ahmadabad, January 5-7, 2007, Available at SSRN:
  2. M. D. Schultz and P. Seele, “Towards AI ethics’ institutionalization: knowledge bridges from business ethics to advance organizational AI ethics,” AI Ethics, vol. 3, no. 1, pp. 99–111, Feb. 2023, doi: 10.1007/s43681-022-00150-y.
  3. L. Lämmermann, P. Hofmann, and N. Urbach, “Managing artificial intelligence applications in healthcare: Promoting information processing among stakeholders,” Int. J. Inf. Manag., vol. 75, p. 102728, Apr. 2024, doi: 10.1016/j.ijinfomgt.2023.102728.
  4. A. Verbeke and V. Tung, “The Future of Stakeholder Management Theory: A Temporal Perspective,” J. Bus. Ethics, vol. 112, no. 3, pp. 529–543, Feb. 2013, doi: 10.1007/s10551-012-1276-8.
  5. “Responsible AI & Digital Ethics: Why Organizations Should Care | IDC Blog.” Accessed: Feb. 29, 2024. [Online]. Available:
  6. ZHU, M., & XU, C. (2023). International soft law governance of artificial intelligence ethics: Current situation, challenges and countermeasures. Bulletin of Chinese Academy of Sciences (Chinese Version), 38(7), 1037-1049
  7. T. Hagendorff, “The Ethics of AI Ethics: An Evaluation of Guidelines,” Minds Mach., vol. 30, no. 1, pp. 99–120, Mar. 2020, doi: 10.1007/s11023-020-09517-8.
  8. J. Zhou and F. Chen, “AI ethics: from principles to practice,” AI Soc., vol. 38, no. 6, pp. 2693–2703, Dec. 2023, doi: 10.1007/s00146-022-01602-z.
  9. T. Hagendorff, “AI ethics and its pitfalls: not living up to its own standards?,” AI Ethics, vol. 3, no. 1, pp. 329–336, Feb. 2023, doi: 10.1007/s43681-022-00173-5.
  10. Gopal Mengi; Sudhakar Kumar (2022) Artificial Intelligence and Machine Learning in Healthcare, Insights2Tecinfo, pp. 1.

Cite As

Walawalkar A. M. (2024) Connecting Business Ethics to Institutionalize Business AI Ethics, Insights2Techinfo, pp.1

68930cookie-checkConnecting Business Ethics to Institutionalize Business AI Ethics
Share this:

Leave a Reply

Your email address will not be published.