Making Sense of AI Ethics and Governance Investments

Abstract

As organisations rapidly adopt artificial intelligence (AI), they encounter a host of complex ethical challenges. These challenges are made even more difficult by the nature of AI itself: it is not a static or uniform technology but rather an evolving ecosystem of tools and systems that interact dynamically with their environments. Managers are under increasing pressure to address these challenges and justify governance investments, while established frameworks for responsible AI governance are still emerging. The central question of thew paper, then, is how managers make sense of these evolving ethical challenges. One productive way to explore this - that we also adopt in this paper - is drawing parallels with the field of Corporate Social Responsibility (CSR). Like AI governance, CSR also involves responding to ambiguous expectations and CSR research provides valuable insights into how organizations engage in "sensemaking" (a process of interpreting and giving meaning to complex situations in order to guide decisions and justify actions). In CSR literature, two dominant forms of sensemaking are identified: "value-driven" and "instrumental". The value-driven approach is rooted in ethical principles and moral commitments; the instrumental approach treats governance as a strategic tool, aligning ethical practices with business goals. Drawing from this conceptual framework, the paper presents a study of managers from diverse sectors to explore how they make sense of AI ethics and governance. The analysis reveals that AI governance sensemaking similarly falls into these two categories, value-driven and instrumental, but importantly, neither approach is sufficient on its own. Instead, our qualitative study finds that managers should blend these two approaches into what we describe as a "holistic sensemaking strategy". By adopting a holistic framework that integrates both perspectives, managers are better equipped to navigate the evolving and ambiguous terrain of AI governance, thus making investments in governance that are both principled and practical.