As artificial intelligence (AI) becomes an increasingly powerful tool in both public and private sector organisations, executives are faced with a critical challenge: how to deploy AI responsibly while maintaining public trust and upholding ethical standards. Ethical AI encompasses complex issues rooted in institutional, economic, and societal pressures. The responsibility falls on the ‘ethics owner’ within organisations to navigate the legal, moral, and strategic implications of AI systems (Metcalf et al., 2019).
For C-suite leaders, the deployment of ethical AI is no longer just a technical concern. It involves reconciling business goals with ethical values such as transparency, fairness, and accountability. However, as AI systems evolve, so do the ethical debates surrounding their use. This article explores the key challenges executives face in implementing ethical AI and offers guidance on how to lead these initiatives effectively.
Understanding ethical AI: what’s at stake?
Ethical AI refers to the development and use of AI technologies that adhere to a set of values, principles, and techniques to guide moral conduct. However, who decides what is “right” or “wrong” in the context of AI development? The ambiguity around accountability creates significant challenges for organisations as they strive to integrate AI while maintaining public trust. Without clear regulations, business ethics often serve as a proxy for technology ethics, but this approach risks reducing complex moral questions to compliance checklists (Metcalf et al., 2019).
The tension between ethical concerns and business objectives can exacerbate the problem. AI systems, designed with values like fairness and equity in mind, often need to compete with financial and operational pressures. The public, however, expects organisations to take responsibility for the social impact of their AI technologies, whether it involves safeguarding privacy or preventing discrimination.
Industry perspectives on ethical AI
From the inside, ethical AI looks very different to those in the technology sector compared to how it’s perceived externally. For many organisations, the ethical process is seen as a set of internal procedures and compliance mechanisms designed to mitigate risk, rather than a broad philosophical inquiry into the moral consequences of AI. Ethical AI and the ethics owner within the industry tends to focus on technical outcomes—such as whether an algorithm performs as expected—while often overlooking broader societal impacts (Moss & Metcalf, 2020).
For example, the issue of bias in AI systems—highlighted by studies such as Buolamwini and Gebru’s (2018) research on commercial facial recognition systems—shows how organisations must balance technical performance with ethical considerations. These issues are not easily resolved through better engineering or more robust processes; they require a holistic view of AI’s role in society, including its potential to perpetuate biases and inequalities.
Executives need to be aware of the “echo-chamber effect” within the AI industry, where the same perspectives are reinforced without proper external scrutiny. This lack of diverse viewpoints can lead to a dangerous insularity, where companies become blind to the ethical risks of their own innovations.
Business ethics vs. ethical AI
There is a fundamental conflict between business ethics and ethical AI. Traditional business ethics, built around profit maximisation and shareholder value, often clash with the broader societal values AI is expected to uphold. Companies developing AI technologies have a duty to their shareholders, but they are also increasingly expected to act as stewards of social justice and fairness (Moss & Metcalf, 2020b).
Reputational capital—how the public perceives an organisation’s commitment to responsible AI—is becoming as important as financial capital. The tech sector, particularly, has seen how public pressure can force rapid changes in business strategy. For instance, IBM’s decision to cease offering facial recognition technology in response to concerns about racial profiling and surveillance was a clear example of how reputational concerns can drive ethical decisions (Peters, 2020).
For C-suite executives, the challenge lies in balancing these pressures. Ethical AI requires a commitment to long-term ethical practices that may, at times, conflict with short-term business objectives. To successfully navigate these tensions, executives must be willing to prioritise ethics as a core element of corporate strategy, not just as a marketing tool or a compliance task.
Overcoming techno-solutionism
A key pitfall in deploying ethical AI is “technological solutionism,” the belief that all ethical challenges can be solved through better technology. While AI companies often present themselves as leaders in ethical innovation, they may rely too heavily on checklists and best practices that fail to address deeper ethical concerns (Metcalf et al., 2019). This approach can lead to a false sense of security, where organisations believe they have mitigated all risks simply because they have followed the right procedures.
Executives should be cautious of falling into this trap. Ethical AI and the ethics owner responsible should not just focus on implementing the right technical safeguards; it requires ongoing dialogue with stakeholders, continuous assessment of social impact, and a commitment to transparency. Organisations must avoid becoming insular, instead fostering collaboration across sectors and disciplines to ensure that ethical AI is responsive to real-world concerns.
As AI continues to reshape industries and societies, executives in the corporate and public sectors must take the lead in ensuring that these technologies are developed and deployed ethically. Ethical AI is not merely a technical issue but a broader organisational challenge that involves aligning business practices with societal values.
To succeed, C-suite leaders must embrace a holistic approach to ethical AI, one that goes beyond compliance and addresses the fundamental moral questions that these technologies raise. By doing so, organisations can build trust with their stakeholders, mitigate reputational risks, and contribute to a future where AI benefits all of humanity.
References:
• Buolamwini, J., & Gebru, T. (2018). Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification. Proceedings of Machine Learning Research, 81(81), 1–15.
• Metcalf, J., Moss, E., & boyd, danah. (2019). Owning Ethics: Corporate Logics, Silicon Valley, and the Institutionalization of Ethics. Social Research: An International Quarterly, 86(2), 449–476.
• Moss, E., & Metcalf, J. (2020). Ethics Owners: A New Model of Organizational Responsibility in Data-Driven Technology Companies. Data & Society.
• Peters, J. (2020, June 8). IBM will no longer offer, develop, or research facial recognition technology. The Verge.