In a paper published in the journal AI & Society, Toni Gibea of Bucharest University of Economic Studies and Radu Uszkai of the University of Bucharest introduce the concept of “driving phronesis” — the capacity to make driving decisions that are action-oriented, context-dependent, and grounded in knowledge acquired through experience. Their proposal is not merely philosophical decoration. They argue that a promising future for autonomous vehicles is one in which they function as digital duplicates of skilled, virtuous drivers in specific places, endowed with what they call functional driving phronesis. The stakes, they contend, could not be higher, because the near-term success of autonomous vehicles hinges not on empty roads but on their ability to coexist with human drivers in mixed traffic.

The paper takes aim at one of the most persistent obsessions in autonomous vehicle ethics: the trolley problem. For over a decade, ethicists have debated whether a self-driving car should swerve to save five pedestrians at the cost of one passenger, framing the question as a modern version of the runaway trolley thought experiment. Gibea and Uszkai, drawing on earlier critiques by philosopher Johannes Himmelreich, argue that these idealized dilemmas are largely irrelevant to real-world deployment. Trolley cases are internally inconsistent, they allow only a top-down design approach, and they ignore the political and legal consensus needed before disruptive technology hits public roads. Worse, they smuggle in epistemic assumptions that no real car could rely on — assuming, for instance, that a heavy vehicle would reliably stop if blocked by a small car, when physics says otherwise.

In place of trolley dilemmas, the authors point to what Himmelreich called “mundane” scenarios: approaching a crosswalk with limited visibility, making a left turn against oncoming traffic, threading through a busy intersection. These situations, not sacrificial dilemmas, dominate actual driving. And they expose the limits of the standard ethical toolkits. A deontological, rule-based approach offers predictability and helps with legal liability, but nobody — including professional moral philosophers — can agree on what the complete list of rules should be. A utilitarian approach is more flexible and can be implemented through cost functions, but it raises uncomfortable questions about whether unfair outcomes can be justified for the greater good, even in ordinary events like running a red light. Contractarian algorithms inspired by Rawls’s veil of ignorance offer more nuance, weighing maximum risks to each party, while risk-based frameworks such as the ethical trajectory planning algorithm developed by researchers at the Technical University of Munich try to distribute danger fairly among all road users.

Hybrid approaches have proliferated precisely because no single theory suffices. The Moral Machine experiment, which collected roughly 40 million decisions from 2.3 million people across 233 countries and territories, revealed that moral preferences cluster into Western, Eastern, and Southern patterns — a finding that pits cultural moral relativism against moral universalism and whose reliability has itself been questioned, prompting some researchers to turn to virtual reality simulations instead. More refined hybrids, such as the agent-deed-consequences model developed by Veljko Dubljević and Eric Racine, combine elements of virtue ethics, deontology, and utilitarianism into a workable framework that has been tested on how humans actually judge moral scenarios involving automated cars. But Gibea and Uszkai identify a gap: even the best hybrid methods lack an explicit integration of virtue ethics, and that omission matters most in the coordination problems of everyday traffic.

The philosophical heart of the paper is Aristotle’s Nicomachean Ethics. Virtues, in the Aristotelian account, are dispositions acquired through long habituation, and the intellectual virtue that makes them possible is phronesis — practical wisdom, the ability to deliberate well about what to do in a particular context. A driver with phronesis is more than merely skilled or rule-abiding. Good drivers respect traffic rules because they benefit everyone impartially, but when confronted with genuinely novel situations, they often cling to formal rules that do not fit. A phronimos at the wheel — a driver possessing practical wisdom — can set good goals and achieve them realistically, adapting to local driving cultures. In Bucharest, reacting instantly at a green light is expected; in Thessaloniki, where mopeds and scooters swarm the streets, the same aggression would be reckless, and the wise driver adjusts out of care for more vulnerable motorcyclists. Anticipation, character assessment, and cultural knowledge all feed into the phronimos’s core competence: knowing what is permissible, how, and when, in a particular place.

Here the authors confront a serious objection from within their own tradition. Philosophers Mihail Constantinescu and Roger Crisp have argued that artificial agents cannot be genuinely virtuous, because virtue requires acting for the right reasons, in the right circumstances — reasons that must be phenomenally integrated into an agent’s way of being. An autonomous vehicle infers patterns from data; it does not deliberate, lacks moral character developed over a lifetime, and fails both the control condition and the epistemic condition for moral agency. Simply behaving in functionally indistinguishable ways from a virtuous human — what John Danaher calls ethical behaviorism — says nothing about why the agent acted. Gibea and Uszkai accept this critique but refuse to abandon the project. An autonomous vehicle cannot be a phronimos, they concede, but it can be trained to display the functional properties of one: behavioral competence without comprehension.

Their blueprint borrows an idea from the entertainment industry: digital duplicates, semi-autonomous digital recreations of real people powered by large language models and generative AI. Applied to driving, the proposal is to develop autonomous vehicles as digital duplicates of idealized versions of the median driver in specific parts of the world — Bucharest, Cairo, New York — each equipped with driving virtue modules carefully curated for the place where the car operates. Because traffic problems are mostly local coordination problems, a car trained this way would emulate the practical wisdom of the best local drivers: recognizing when informal conventions override formal rules, when apparent free-riding is actually efficient, and when tolerance beats rigid rule-following. The authors are alert to the danger of hard-coding widespread but bad behavior as virtuous; they stress that the “is” of how people drive must not ground the “ought” of design, and that stakeholders including pedestrians and cyclists must shape the curation process.

Two case studies from Bucharest illustrate the idea. In the first, a road that widens from three lanes to four creates an unregulated merge where cars from two different lanes compete for the right-turn lane. Good drivers disagree about who yields, and authorities eventually installed a barrier — a fix that could fail if traffic patterns change. A driver with phronesis recognizes a genuine practical dilemma rather than selfish free-riding, and responds prudently. In the second, drivers at a congested intersection informally agree to cross a painted island to free up an extra lane, solving a coordination problem that formal rules handle badly. An autonomous vehicle trained purely on traffic rules as filters would be less effective than humans in both cases; one trained with functional driving phronesis could recognize when exceptions apply.

The takeaway for engineers, philosophers, and policymakers is a design milestone: before autonomous vehicles are deployed in mixed traffic, they should be conceived as avatars of how wise, ordinary drivers solve coordination problems in everyday scenarios. Instilling moral values, the authors note, is like human moral development — a long process that must begin at the earliest stages of system design, not bolted on later as an ethics update. Whether machines can ever truly possess practical wisdom remains an open question, but as robotaxis multiply in cities where humans still drive, the paper suggests that the smartest thing a self-driving car can learn is not the rules of the road, but the judgment of the people who master them.

Subject of Research: Applying Aristotelian virtue ethics and practical wisdom (phronesis) to the ethical design of autonomous vehicles in mixed traffic

Article Title: Driving phronesis as a milestone for engineering autonomous vehicles

Article References: Gibea, T., & Uszkai, R. (2026). Driving phronesis as a milestone for engineering autonomous vehicles. AI & SOCIETY. https://doi.org/10.1007/s00146-026-03250-z

Image Credits: AI Generated

DOI: 10.1007/s00146-026-03250-z

Keywords: autonomous vehicles, driving phronesis, virtue ethics, Aristotle, digital duplicates, trolley problem, mixed traffic, machine ethics, practical wisdom, traffic coordination, AI ethics, imitation learning

The research team, comprising Professor Dirk Lindebaum of the University of Bath’s School of Management, Professor Natarajan Balasubramanian of Ohio State University, Dr Mehreen Ashraf of Cardiff University and Dr Patrick Haack of the University of Lausanne, set out to model how the growing presence of generative AI in the workplace interacts with the processes through which managers acquire and refine practical wisdom. Their paper, titled A Process Model of Managerial Phronesis in the Age of Generative AI, argues that the appeal of these systems lies in their speed, but that speed is precisely what makes them risky when they are treated as substitutes for thinking rather than as aids to it.

Professor Lindebaum’s central point is that generative AI, however fluent its outputs may appear, cannot replicate the lessons that come from first-hand experience. Unlike humans, AI does not experience the world, understand the consequences of decisions, or grasp the social and emotional complexities that shape behaviour in workplaces. Instead, it produces responses based on patterns found in existing data. That distinction matters because managerial phronesis is not a body of information that can be retrieved; it is a capability that accumulates through acting, observing outcomes and reflecting on them, often in situations where the right answer depends on context, relationships and moral considerations that no dataset fully captures.

The danger, according to the researchers, is that as managers increasingly outsource their thinking to generative AI, whether for idea generation or when a practical problem arises at work, they may rely less on their own judgement. Over time, this could reduce their ability to learn from experience, think critically, and anticipate what kinds of actions are needed now to meet future goals. The concern is not that AI produces wrong answers in every case, but that the habit of deferring to it weakens the mental muscles that managers need precisely in the situations where AI is least reliable: ambiguous, novel and morally charged decisions.

To describe this dynamic, the team coined the concept of epistemic de-skilling, a process in which people gradually lose knowledge-related capabilities because they outsource too much of their thinking to generative AI. The researchers suggest this is most likely to occur when managers are under intense time pressure and use the technology as a shortcut rather than engaging deeply with a problem themselves. In such situations, managers may stop asking important questions, seeking different perspectives or learning from real-world interactions. Instead of developing a nuanced understanding of employees, customers or organisational challenges, they may come to depend on AI-generated answers that lack the context and moral judgement needed for complex decisions.

The mechanism matters as much as the outcome. Practical wisdom, in the Aristotelian tradition from which the term phronesis derives, is built through the repeated cycle of confronting a concrete situation, judging what is at stake, acting, and reflecting on the results. If the first and most demanding step of that cycle is handed to a machine, the subsequent steps lose their raw material. A manager who no longer wrestles with a problem before consulting an AI system has nothing of their own to test the system’s output against, and the reflective part of the loop collapses into passive acceptance. The study frames this as a process model precisely because the damage is cumulative: each shortcut makes the next one easier, and the capability fades through disuse rather than through any single dramatic failure.

Crucially, the researchers do not conclude that generative AI is inherently corrosive to managerial judgement. They also developed the idea of how the technology may be used to develop the judgement of managers, although they note this is likely to prove more challenging. Generative AI can strengthen managerial judgement when it is used as a tool for reflection rather than a replacement for thinking, a process the researchers dubbed epistemic up-skilling. Rather than accepting AI outputs at face value, managers can use them to challenge assumptions, explore alternative scenarios and test the reasoning behind their own decisions. Because AI systems often struggle to explain why they produce particular answers, the gaps in those explanations can encourage people to think more deeply about their choices and the consequences of their actions.

That up-skilling path, however, demands deliberate effort. Professor Lindebaum emphasised that it requires persistent effort on the part of managers to fill the explanatory gaps that AI leaves behind. The study suggests this beneficial outcome is most likely when managers know they will be held accountable for their decisions. In workplaces where individuals must justify their actions and explain their reasoning, generative AI can become a trigger for deeper reflection instead of a substitute for judgement. As Lindebaum put it, it is that which generative AI cannot satisfactorily explain that managers must explain to themselves and others. Accountability, in this framing, functions as a safeguard: the obligation to give reasons forces the manager back into the reflective cycle that passive reliance on AI would otherwise short-circuit.

The implications for organisations are direct. It is becoming increasingly clear, the researchers argue, that simply introducing AI tools will not automatically improve decision-making or organisational performance. Instead, organisations need to carefully design roles, responsibilities and workflows to ensure employees continue developing the human skills that AI cannot replicate. That design challenge extends to how performance is evaluated and how time pressure is managed, since the study identifies intense time pressure as a key condition under which de-skilling takes hold. Workplaces that reward speed above deliberation may inadvertently push managers toward the shortcut that erodes their judgement, while those that build in reflection and accountability can turn the same tools into instruments of learning.

The study, published in the Academy of Management Review with the DOI 10.5465/amr.2024.0582, arrives at a moment when generative AI is being embedded into professional work faster than the norms governing its use are being established. Its message is neither technophobic nor celebratory. The same system that can hollow out a manager’s capacity for judgement under time pressure and passive reliance can, under conditions of accountability and active reflection, sharpen that capacity by exposing the limits of machine-generated answers. The difference lies not in the technology itself but in how it is woven into the daily practice of management, and in whether organisations treat AI as a replacement for experienced human thinking or as a sparring partner for it.

Subject of Research: The effects of generative AI on managers' practical wisdom and judgement in the workplace

Article Title: AI could undermine managers' judgement unless used carefully, University of Bath-led study warns

Article References: AI could undermine managers' judgement unless used carefully, University of Bath-led study warns. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: generative AI, managerial phronesis, epistemic de-skilling, epistemic up-skilling, managerial judgement, University of Bath, Academy of Management Review, workplace decision-making, accountability, ChatGPT, organisational behaviour, practical wisdom