Here is an excerpt from an article written by Gianvito Lanzolla, Margherita Pagani, and Christopher L. Tucci for MIT Sloan Management Review. To read the complete article, check out others, sign up for email alerts, and obtain subscription information, please click here.
Illustration Credit: Christian Gralingen
* * *
Summary: As organizations adopt diverse flavors of AI more widely and scale applications across the enterprise, managing new risks that arise in the …
Leaders with even a cursory understanding of artificial intelligence know that while the technology can help them improve productivity and capture new opportunities, it can also expose their organization to many risks. Those with a bit more knowledge are aware that surfacing and mitigating those risks requires adopting responsible AI practices. And leaders who are scaling an AI implementation within their organization will quickly realize that ad hoc attention to those practices is inadequate and that they need to develop the capacity to systematically govern AI at scale.
But building that capacity is proving far harder than most executives expect. They know what they need to accomplish; frameworks from governments and regulators define important guardrails and principles, such as transparency, fairness, and accountability.1 But to implement controls and principles into day-to-day workflows and decision-making, organizations must rethink AI governance. They must frame that task not as a compliance obligation but as a strategic, adaptive capability that evolves as AI systems scale, use cases expand, and risks shift over time.
In this article, we will share how leading organizations are doing exactly that. We will also introduce an approach to adaptive AI governance built on two principles: matching governance controls to the type of AI system and risk involved, and embedding those controls directly into workflows, decision rights, and accountability structures.
The Fundamentals of AI Risk
To design effective AI governance, leaders must first understand the multiple ways in which AI can fail and the corresponding risks. The nature and severity of these risks depend on the type of system, its level of autonomy, and the scope of domains affected by its decisions.
* * *
Here is a direct link to the complete article.
References (2)
1. See, for example, “Artificial Intelligence Risk Management Framework (AI RMF 1.0),” PDF file (National Institute of Standards and Technology, U.S. Department of Commerce, January 2023), https://doi.org/10.6028/NIST.AI.100-1.
2. C. O’Neil, “Audit the Algorithms That Are Ruling Our Lives,” Financial Times, July 30, 2018, www.ft.com. See also C. O’Neil, H. Sargeant, and J. Appel, “Explainable Fairness in Regulatory Algorithmic Auditing,” West Virginia Law Review 127, no. 1 (September 2024): 79-133.