Artificial intelligence, we’re told, is humanity’s greatest innovation since fire and the wheel. It’s here to save us—one app, algorithm, and overhyped corporate PowerPoint at a time. But as AI seeps into every corner of modern life, a curious phenomenon has emerged: the rise of AI ethics. With all the gravitas of a Vatican conclave, tech companies solemnly declare their commitment to fairness, accountability, and transparency. They host conferences, publish guidelines, and convene ethics boards.
But let’s drop the pretense. AI ethics is less about doing good and more about appearing to do good. It’s the corporate world’s new fig leaf—a smokescreen for profiteering, consolidation of power, and perpetuation of the same inequities AI is allegedly here to fix. In the tech industry’s version of moral theater, the language of ethics is weaponized to distract critics, placate regulators, and gain market advantage. Beneath the surface lies a swamp of contradictions, hypocrisies, and outright opportunism.
Still, like a Hollywood star delivering a TED Talk on carbon footprints while chartering private jets, these same companies rarely practice what they preach. The AI ethics movement, for all its lofty promises, often feels less like a moral revolution and more like a smokescreen—designed to distract us while the tech giants quietly rake in billions. Welcome to the Great AI Ethics Con, where words are cheap, hypocrisy reigns supreme, and algorithms are the new Wild West.
1. Ethical AI as the Corporate Fig Leaf
Let’s start with the obvious: corporations are in the business of making as much money as they can now and in the future, not prioritizing principles. That’s not a moral judgment; it’s a statement of fact. Yet these same corporations would have us believe they’ve taken a sudden interest in philosophy, crafting elaborate frameworks for “responsible AI” that conveniently align with their bottom lines. It’s a remarkable sleight of hand—wrapping aggressive expansion in the language of moral virtue.
Ethics, properly understood, requires limits. It demands a willingness to say, “This far and no further,” even when it’s inconvenient or unprofitable. Yet the ethos of Silicon Valley is precisely the opposite: move fast, break things, and figure out the consequences later. Ethical AI, as practiced, is the moral equivalent of a company claiming commitment to safety standards while quietly cutting corners to maximize profits. It’s not about doing less harm; it’s about looking good while doing it.
2. Self-Regulation: The Joke Everyone Is In On
The tech industry loves to assure us that it can police itself. To prove this, companies establish ethics boards, draft lofty principles, and announce new initiatives with great fanfare. But let’s not kid ourselves: self-regulation in AI is like asking a fox to guard the henhouse—except in this case, the fox gets to write the rulebook and decide when it’s had enough chickens.
Ethics boards are a particularly farcical invention. They exist primarily to generate good press, staffed with enough academics to provide legitimacy but with little actual power to influence corporate behavior. Their recommendations are “considered” but rarely implemented, and the boards themselves often disband the moment they become inconvenient.
Even the principles these boards produce are intentionally vague. Words like “fairness” and “transparency” abound, but there’s no clarity on how they’re defined, measured, or enforced. Worse still, these principles are designed to be flexible—adaptable to shifting market demands and priorities. In practice, this means they can be ignored whenever they conflict with profitability.
3. The Myth of Neutrality
One of the great lies perpetuated by AI ethics is the claim that algorithms are neutral. Unlike humans, we’re told, AI systems don’t harbor biases or prejudices. They simply crunch the numbers and deliver objective truths. But this myth, comforting as it may be, is demonstrably false.
AI systems are trained on data, and that data reflects the biases, inequities, and blind spots of the societies that produced it. Moreover, the very act of designing an algorithm involves subjective decisions—what to prioritize, what to ignore, and how to optimize outcomes. These decisions are shaped by the values, assumptions, and incentives of their creators.
Yet when AI systems produce discriminatory or harmful outcomes, the creators are quick to deflect responsibility. It’s not us, they insist—it’s the “black box” complexity of the system. This narrative absolves them of accountability while perpetuating the fiction that technology can somehow transcend the flaws of its creators.
4. Ethics Without Sacrifice
True ethics requires hard choices. It demands that those in power constrain their actions, even at the expense of short-term gains. But AI ethics, as practiced, is ethics without sacrifice—a set of principles designed to sound good without demanding anything in return.
Take the issue of bias in AI systems. Addressing bias requires more than technical fixes; it demands systemic change. This means rethinking data collection, involving Natural Rights champions in development, and fundamentally questioning the goals and assumptions baked into the technology. These changes take time, resources, and—most importantly—a willingness to slow down the relentless pursuit of growth. Yet companies routinely declare victory over bias with a few algorithmic tweaks, as if centuries of systemic Natural Rights violations can be fixed with a software update.
This refusal to grapple with the deeper issues isn’t just lazy; it’s cynical. Ethics without sacrifice isn’t ethics at all. It’s performance art—a way to placate critics without addressing the underlying problems.
5. The Weaponization of Ethics
AI ethics isn’t just a shield; it’s also a weapon. Companies use the language of ethics to consolidate power, marginalize competitors, and shape regulatory landscapes in their favor. By championing high standards, they position themselves as moral leaders while ensuring that smaller rivals can’t afford to comply.
This is particularly evident in the push for regulation. Large tech companies publicly support stringent ethical guidelines, knowing full well they have the resources to navigate the complexities. Meanwhile, smaller firms are squeezed out, unable to bear the cost of compliance. Far from leveling the playing field, ethics becomes a tool for entrenching monopolistic power.
Even worse, ethics is often invoked to justify restrictive policies under the guise of responsibility. These policies don’t actually address the root causes of harm; they simply reinforce the status quo, ensuring that the largest players remain dominant.
6. Selective Ethics: When Principles Stop at the Border
Another hallmark of AI ethics hypocrisy is its selective application. Ethical principles are trumpeted in markets where regulators demand accountability but conveniently ignored elsewhere. It’s ethics on demand, deployed where it’s useful and discarded where it’s not.
In wealthy, developed nations, companies loudly proclaim their commitment to privacy, fairness, and transparency. Meanwhile, in less scrutinized regions, they deploy the same technologies with little regard for local laws or cultural contexts. The ethical high ground, it seems, is geofenced.
7. The Danger of Ethical Monocultures
The current AI ethics paradigm suffers from a glaring lack of commitment to preserving and protecting Natural Rights. Ethical frameworks are typically crafted by a narrow set of voices—technologists, executives, and policymakers from a handful of wealthy nations. This creates an ethical monoculture that reflects the values and priorities of a small elite while marginalizing Liberty and more.
The result is a set of principles that feel universal but are, in fact, deeply parochial. They prioritize efficiency and scalability over justice and equity. They frame complex social issues as technical problems, to be solved by better algorithms rather than systemic change. And they fail to account for the ways in which AI disproportionately affects vulnerable populations.
8. Toward Genuine Ethical AI
If AI ethics is to mean anything, it must move beyond hollow platitudes and embrace genuine accountability. This requires:
- Independent Oversight: Ethics cannot be left to corporations to self-regulate. Independent bodies must have the authority to audit AI systems, enforce standards, and impose meaningful penalties for violations.
- Transparency: Companies must disclose how their systems work, how decisions are made, and what data is used. Without transparency, ethical claims are impossible to verify.
- Universal Application: Ethical principles should apply consistently across all markets, not just where it’s convenient.
- Trade-Offs: True ethics requires hard choices. Companies must be willing to prioritize Natural Rights over growth, even when it costs them.
- Inclusivity: Ethical frameworks must include voices from those most affected by AI systems, ensuring that blind spots are addressed.
Conclusion: The Real Test of Ethics
AI ethics is fast becoming the tech industry’s favorite buzzword. But for all the lofty talk of fairness and accountability, too often it’s a mask for business as usual. If companies truly care about ethical AI, they’ll need to stop using it as a shield for profit-driven agendas and start aligning their actions with their words.
Because in the end, ethics isn’t just about what you say in a glossy report. It’s about what you do when no one’s watching. And if the current state of AI ethics is anything to go by, it’s clear that for too many in the industry, the real algorithm is hypocrisy.
