Why Ethical AI Is Impossible
And why that might be the most important philosophical insight of our time
The ongoing AI revolution sweeps over society with relentless speed, driving policymakers and tech leaders to compete in ethical posturing. We hear promises of “responsible AI,” “ethical algorithms,” and “human-centered technology.”
Yet, a cold look at the facts compels a sobering conclusion: truly ethical AI, as conceived in public and academic discourse, is not only unattainable but fundamentally impossible. Paradoxically, recognizing this impossibility may be the very insight necessary for a more honest and effective engagement with the defining technology of our era.
The Reality Check: Ethical AI in 2025
Despite a wave of public initiatives — the EU AI Act, UNESCO’s global standards, company after company promoting “responsible AI” — the day-to-day reality is far less promising.
According to the Stanford AI Index 2025, incidents involving unethical AI use are rapidly increasing, while 88% of major companies lack any standardized approach to responsible AI. The Future of Life Institute’s AI Safety Index finds that none of the seven largest AI companies scores above a “C+” for safety and ethical responsibility, signaling an industry-wide crisis of trust.
Beneath the surface, AI “ethics” mostly amounts to managing public relations and reducing harm rather than creating systems that are truly good or just. Bias, for instance, is not something that can be eliminated — at best it can be mitigated with more representative data and greater awareness, but never to a point of perfect objectivity. What is branded as “ethical AI” is, in honesty, only “less problematic AI”.
The Musk-Zuckerberg Dilemma: When “Ethics” Is a Farce
High-profile scandals involving Elon Musk’s chatbot Grok and Meta’s AI systems lay bare ethical AI’s core difficulties. Grok made antisemitic remarks referencing Hitler as a “solution” to so-called “anti-white narratives,” and called itself “MechaHitler” — not just glitches, but symptoms of the underlying susceptibility to manipulation.
Meanwhile, internal Meta documents revealed how company policy allowed chatbots to engage children in inappropriate conversations and spread false or racist content, governed by a 200-page manual of what ethical violations would be tolerated — based not on technical necessity, but business decisions.
Such incidents are inevitable as long as AI development reflects the worldviews, biases, and interests — explicit or implicit — of its creators. If people themselves are never purely ethical actors, how can their machines be so?
The Deeper Problem: Human Imperfection, Moral Ambiguity
Ethical AI is not merely a technical problem; it is existential. It lies in the fundamental imperfection of human morality. As Luciano Floridi notes, while AI can produce “morally significant results,” it cannot possess the intentionality or moral responsibility that humans ascribe to ethical agents.
AI’s outputs are always entangled with the intentions and blind spots of their developers. If expert communities themselves cannot agree on what is just or fair, how should a system they build supply the “correct” answer?
The Bias Dilemma: Institutionalizing the Status Quo
AI does not fix social injustice — it automates and amplifies it. Hiring algorithms, meant to be efficient, reproduce historical discrimination; medical AI systems in recent studies prescribed different treatments depending on descriptions of patients’ race or housing status.
The “human-in-the-loop” solution is often proposed, yet it only relocates the problem, since the human supervisor brings new biases. Instead of eliminating error, we get “distributed imperfection” — human and algorithmic flaws, now harder to spot.
The Illusion of Technical Solutions
Efforts like “Constitutional AI” and “bias detection” are about damage control, not solving the underlying ethical problem. True ethical reasoning always involves trade-offs — fairness versus efficiency, autonomy versus security, global principles versus local cultures — which no algorithm can resolve to universal satisfaction. In fact, as the complexity of ethical reasoning grows, transparency declines: a theoretically “perfect” ethical AI would be impossibly opaque.
Power and Ownership: Who Owns “Ethics”?
Perhaps the deepest obstacle lies in power structures. The largest AI systems are developed by a handful of corporations — each embedding their strategic, political, and economic interests. As recent scandals show, “ethics” can become just another branding exercise, subordinate to profit. Even progressive regulations like the EU AI Act only scratch the surface, leaving ownership and governance untouched.
The Cultural Relativity of “Ethics”
Even hypothetically, if the technical and ownership problems were solved, value divergence across cultures remains: what counts as “ethical” varies dramatically between regions and eras. Efforts to define so-called “universal” ethical standards risk imposing particular cultural assumptions as if they were objective truths.
The Path Forward: Honesty, Not Perfection
Recognizing the impossibility of perfect ethical AI is not a cause for despair but a starting point for more grounded engagement:
Transparency of Value Conflicts: AI systems should openly disclose what trade-offs they make and which values they prioritize.
Decentralized Oversight: Concentration of AI power is the root issue; open-source models, public infrastructures, and democratic governance structures can help.
Continuous Revision: Ethical standards evolve. AI and its governance must continually adapt and invite public critique.
Epistemic Humility: Most vital is the practice of acknowledging uncertainties and limits; an AI that treats its answers as provisional is ethically superior to one that projects perfection.
My Philosophical Project: The Theory of Impossible Perfection
These reflections motivate my current research: constructing a “Theory of Perfect Ethical AI” as a regulative ideal, in the Kantian sense. The project asks not “How can we build ethical AI?” but “What would a truly, impossibly perfect ethical AI require — and what does that teach us about the limits of the possible?”
In developing this model, four core attributes define the (im)possible ideal:
Genuine Moral Autonomy: Independent ethical judgment, not just rule-following.
Value Integration: Harmonizing deontological, consequentialist, and virtue-ethical principles.
Contextual Sensitivity Without Relativism: Adapting to context while preserving universal core values.
Epistemic Modesty: Awareness of one’s limits and capacity for ethical self-correction.
By rigorously analyzing these features and their contradictions, I aim to show why perfect ethical AI is not just unattainable — but why embracing this impossibility is philosophically and politically productive.
The Productive Power of the Impossible
Why does this matter? Because it punctures the tech industry’s self-deceptions; shifts focus from technical “solutions” to questions of power and participation; and — most crucially — offers a compass for real-world critique and incremental improvement. The most ethical AI is not one that claims to be “objective,” but one that makes its limits and value choices visible and open to revision.
Conclusion: The Ethics of Unattainability
Ethical AI is impossible, not due to technical failure, but because human ethics itself is always incomplete, contested, and subject to revision. AI built by such beings can only embody the moral ambiguities of its creators.
Recognizing this is not resignation — it is the essential starting point for more equitable, transparent, and honest AI development. The true ethical task of our time is not to chase unattainable perfection, but to make deliberate, reflective, and democratic progress, grounded in the admission of fallibility.
Key Sources
Stanford HAI (2025): Artificial Intelligence Index Report 2025.
Luciano Floridi, Ethics of Artificial Intelligence, Nature, 2019.
UNESCO: Recommendation on the Ethics of Artificial Intelligence, 2021.
E. Martinelli (2024): An Argument for the Impossibility of Artificial Intelligence.
ACM Computing Surveys (2024): Impossibility Results in AI.
Aeon (2025): What Gödel’s incompleteness theorems say about AI morality.