The trolley dilemma in the age of AI: when machines have to make ethical decisions, is human judgment really always superior? The debate is still open.
Imagine a runaway train heading towards five people. You can pull a lever to divert it onto another track, but there is only one person there. What would you do?
But wait: what if that person was a child and the five were elderly people? What if someone offered you money to pull the lever? What if you couldn't see the situation clearly?
What is the Trolley Problem? Formulated by philosopher Philippa Foot in 1967, this thought experiment presents a seemingly simple dilemma: sacrifice one life to save five. But the variations are endless: the fat lady to push off the bridge, the doctor who could kill one healthy patient to save five with his organs, the judge who could convict an innocent person to stop a riot.
Each scenario tests our fundamental moral principles: when is it acceptable to cause harm in order to prevent greater harm?
This complexity is precisely what makes the ethics of artificial intelligence such a crucial challenge for our time.
The famous “trolley problem” is much more complex than it seems—and this complexity is precisely what makes the ethics of artificial intelligence such a crucial challenge for our time.
From the Philosophy Classroom to Algorithms
The trolley problem, formulated by philosopher Philippa Foot in 1967, was never intended to solve practical dilemmas. As the Alan Turing Institute points out, the original purpose was to demonstrate that thought experiments are, in essence, divorced from reality. Yet, in the age of AI, this paradox has taken on immediate relevance.
Why is it important now? Because for the first time in history, machines must make ethical decisions in real time—from autonomous cars navigating traffic to healthcare systems allocating limited resources.
Claude and the Constitutional AI Revolution
Anthropic, the company behind Claude, has tackled this challenge with a revolutionary approach called Constitutional AI. Instead of relying solely on human feedback, Claude is trained on a “constitution” of explicit ethical principles, including elements of the Universal Declaration of Human Rights.
How does it work in practice?
Claude self-criticizes and revises its own responses
It uses Reinforcement Learning from AI Feedback (RLAIF)
It maintains transparency about the principles that guide its decisions
An empirical analysis of 700,000 conversations revealed that Claude expresses over 3,000 unique values, from professionalism to moral pluralism, adapting them to different contexts while maintaining ethical consistency.
The Real Challenges: When Theory Meets Practice
As Neal Agarwal's interactive project Absurd Trolley Problems brilliantly illustrates, real-world ethical dilemmas are rarely binary and often absurd in their complexity. This insight is crucial to understanding the challenges of modern AI.
Recent research shows that the ethical dilemmas of AI go far beyond the classic trolley problem. The MultiTP project, which tested 19 AI models in over 100 languages, found significant cultural variations in ethical alignment: models are more aligned with human preferences in English, Korean, and Chinese, but less so in Hindi and Somali.
Real-world challenges include:
Epistemic uncertainty: Acting without complete information
Cultural biases: Different values across cultures and communities
Distributed responsibility: Who is responsible for AI decisions?
Long-term consequences: Immediate vs. future effects
Human Ethics vs. AI Ethics: Different Paradigms, Not Necessarily Worse
An often overlooked aspect is that AI ethics may not simply be an imperfect version of human ethics, but a completely different paradigm—and in some cases, potentially more consistent.
The Case of “I, Robot”: In the 2004 film, Detective Spooner (Will Smith) is suspicious of robots after being saved by one in a car accident, while a 12-year-old girl was left to drown. The robot explains its decision:
"I was the logical choice. I calculated that she had a 45% chance of survival. Sarah only had 11%. That was someone's child. 11% is more than enough."
This is exactly the kind of ethics AI operates on today: algorithms that weigh probabilities, optimize outcomes, and make decisions based on objective data rather than emotional insights or social biases. The scene illustrates a crucial point: AI operates with ethical principles that are different but not necessarily inferior to human ones:
Mathematical consistency: Algorithms apply criteria uniformly, without being influenced by emotional or social biases—just like the robot that calculates survival probabilities
Procedural impartiality: They do not automatically favor children over the elderly or the rich over the poor, but evaluate each situation based on the available data
Transparency in decision-making: The criteria are explicit and verifiable (“45% vs. 11%”), unlike human moral intuition, which is often opaque.
Concrete examples in modern AI:
AI healthcare systems that allocate medical resources based on the probability of therapeutic success.
Matching algorithms for organ transplants that optimize compatibility and probability of survival.
Automated triage systems in emergencies that prioritize patients with the best chance of recovery
But Maybe Not: The Fatal Limits of Algorithmic Ethics
However, before celebrating the superiority of AI ethics, we must confront its inherent limitations. The scene from “I, Robot” that seems so logical hides profound problems:
The Problem of Lost Context: When the robot chooses to save the adult over the child based on probabilities, it completely ignores crucial elements:
The social and symbolic value of protecting the most vulnerable
The long-term psychological impact on survivors
Family relationships and emotional bonds
The unfulfilled potential of a young life
The Concrete Risks of Purely Algorithmic Ethics:
Extreme Reductionism: Turning complex moral decisions into mathematical calculations can remove human dignity from the equation. Who decides which variables matter?
Hidden biases: Algorithms inevitably incorporate the biases of their creators and training data. A system that “optimizes” could perpetuate systemic discrimination.
Cultural uniformity: AI ethics risks imposing a Western, technological, and quantitative view of morality on cultures that value human relationships differently.
Examples of real-world challenges:
Healthcare systems that could apply efficiency criteria more systematically, raising questions about how to balance medical optimization and ethical considerations
Judicial algorithms that risk perpetuating existing biases on a larger scale, but could also make existing discrimination more transparent
Financial AI that can systematize discriminatory decisions, but also eliminate some human biases related to personal prejudices
Criticisms of the Traditional Paradigm
Experts such as Roger Scruton criticize the use of the trolley problem for its tendency to reduce complex dilemmas to “pure arithmetic,” eliminating morally relevant relationships. As an article in TripleTen argues, “solving the trolley problem will not make AI ethical”—a more holistic approach is needed.
The central question becomes: Can we afford to delegate moral decisions to systems that, however sophisticated, lack empathy, contextual understanding, and human experiential wisdom?
New proposals for a balance:
Hybrid ethical frameworks that combine computation and human intuition
Human oversight systems for critical decisions
Cultural customization of ethical algorithms
Mandatory transparency on decision-making criteria
Human right of appeal for all critical algorithmic decisions
Practical Implications for Businesses
For business leaders, this evolution requires a nuanced approach:
Systematic ethical audits of AI systems in use—to understand both benefits and limitations
Diversity in teams that design and implement AI, including philosophers, ethicists, and representatives from diverse communities
Mandatory transparency on the ethical principles embedded in systems and their rationale
Continuous training on when AI ethics works and when it fails
Human oversight systems for decisions with high ethical impact
Appeal rights and correction mechanisms for algorithmic decisions
As IBM points out in its 2025 outlook, AI literacy and clear accountability will be the most critical challenges for the coming year.
The Future of AI Ethics
UNESCO is leading global initiatives for AI ethics, with the 3rd Global Forum scheduled for June 2025 in Bangkok. The goal is not to find universal solutions to moral dilemmas, but to develop frameworks that enable transparent and culturally sensitive ethical decisions.
The key lesson? The trolley problem serves not as a solution, but as a reminder of the inherent complexity of moral decisions. The real challenge is not choosing between human or algorithmic ethics, but finding the right balance between computational efficiency and human wisdom.
The ethical AI of the future will have to recognize its limitations: excellent at processing data and identifying patterns, but inadequate when empathy, cultural understanding, and contextual judgment are needed. As in the scene from “I, Robot,” the coldness of calculation can sometimes be more ethical—but only if it remains a tool in the hands of conscious human supervision, not a substitute for human moral judgment.
The “(or maybe not)” in our title is not indecision, but wisdom: recognizing that ethics, whether human or artificial, does not allow for simple solutions in a complex world.
Sources and Further Reading
Initial Inspiration:
All The Trolley Problem - Clarified Mind (Video)
Absurd Trolley Problems - Neal Agarwal (Interactive Project)
Academic Research:
Constitutional AI: Harmlessness from AI Feedback - Anthropic
Industry Analysis:
Claude's Constitution - Anthropic
AI's Trolley Problem Problem - Alan Turing Institute
Regulatory Developments:
Welcome to Electe’s Newsletter - English
This newsletter explores the fascinating world of how companies are using AI to change the way they work. It shares interesting stories and discoveries about artificial intelligence in business - like how companies are using AI to make smarter decisions, what new AI tools are emerging, and how these changes affect our everyday lives.
You don't need to be a tech expert to enjoy it - it's written for anyone curious about how AI is shaping the future of business and work. Whether you're interested in learning about the latest AI breakthroughs, understanding how companies are becoming more innovative, or just want to stay informed about tech trends, this newsletter breaks it all down in an engaging, easy-to-understand way.
It's like having a friendly guide who keeps you in the loop about the most interesting developments in business technology, without getting too technical or complicated
Subscribe to get full access to the newsletter and publication archives.
