Here is an excerpt from an article written by François-Xavier de Vaujany and Aurélie Leclercq-Vandelannoitte 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: Mark Airs/Ikon Images
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As AI systems take on more decision-making, leaders need to build shared narratives instead of assigning blame.
Early one morning in 2018, a self-driving Uber vehicle fatally struck a pedestrian in Tempe, Arizona. The world had questions: Who was responsible? Was it the safety driver behind the wheel? The engineers who designed the algorithms? Uber’s leadership? Or the regulators who had allowed autonomous-vehicle testing? The inability to name a single culprit signaled a profound shift in how responsibility must be understood and attributed in the age of intelligent technologies.
As organizations deploy increasingly autonomous systems such as drones, trading bots, or algorithmic decision makers (like automated resume screeners or credit assessment tools), agency becomes distributed, emerging from the complex interplay of human and machine actions. Decisions, once linear and traceable, now unfold across networks of people and artificial intelligence systems, introducing new forms of influence and unpredictability.
For today’s leaders, this means that the old search for a culprit loses relevance. The real challenge is not to assign blame but to instead construct a shared narrative — to uncover not only what went wrong but how collective activities, assumptions, and technologies shaped the outcome. As our recent research, published in MIS Quarterly, shows, forging organizational learning and resilience depends on this collaborative revisiting of how decisions happen and how stories of responsibility are constructed. We call this process narrative responsibility.
Why Classic Models of Responsibility No Longer Work
Classic theories of responsibility have rested on three core assumptions: that the world is fundamentally linear, with events following clear cause-and-effect logic; that decision makers act in a shared space and time, making the link between actions and consequences traceable; and that responsibility can be precisely attributed backward to an individual whose intentions and choices drive outcomes.
Consistent with these assumptions, when something goes wrong, organizations often enact traditional models of accountability by holding a senior leader personally responsible. For instance, after two fatal crashes of Boeing’s 737 MAX aircraft killed 346 people in 2018 and 2019, CEO Dennis Muilenburg was swiftly dismissed as a visible response to the crisis.
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