“We’re committed to ethical AI” has become the tech equivalent of “thoughts and prayers”—a phrase uttered solemnly that often signifies precisely nothing in practice.
Every company with an algorithm now claims to practice “responsible AI,” but when you dig past the marketing, you’ll find that concrete ethical frameworks are as rare as unbiased training data.
I recently consulted with a fintech startup (let’s call them CreditFast) that proudly advertised their “ethical AI-driven lending decisions.” When I asked about their approach to algorithmic fairness, their CTO stared at me blankly before admitting: “We just make sure it maximizes approval rates while minimizing defaults.” That’s profit optimization, not ethics.
True ethical AI requires uncomfortable trade-offs and explicit value judgments. Take my client Sarah, who leads product at a healthcare AI company. Her team discovered their diagnostic algorithm performed 8% better for men than women across certain conditions. Fixing this disparity would reduce overall accuracy by about 2%. Most companies would quietly sweep this under the rug, but Sarah’s team explicitly chose equity over maximum accuracy—and documented this choice transparently.
The problem isn’t that companies are malicious; it’s that ethics requires deliberate design rather than afterthought compliance. When facial recognition company Clearview AI claimed they were “ethical” while scraping billions of images without consent, they weren’t twirling villainous mustaches—they simply defined “ethical” as “legal and profitable.” Without specific ethical frameworks, such circular definitions become inevitable.
So what does meaningful ethical AI actually look like in practice?
Microsoft’s Responsible AI team impressed me by instituting “consequence scanning” workshops during early development. Every new AI feature must go through exercises identifying potential misuse, unintended consequences, and differential impacts across populations—before coding even begins.
Less visible but equally important is the practice of “ethics by design” which rivals the more famous “privacy by design” approach. Financial services firm NorthOne embedded ethicist Maria Hupfield directly into product teams. Her presence transformed abstract discussions about fairness into concrete design decisions about default options and user consent mechanisms.
Perhaps most promising is the emergence of standards beyond vague principles. IEEE’s 7000-2021 standard for addressing ethical concerns during system design provides specific methods for ethical risk assessment that companies can actually implement rather than just nodding along to high-level values.
For your own organization, start with the “discriminatory proxy” test: identify which attributes in your data might serve as proxies for protected characteristics. Then implement routine algorithmic impact assessments that evaluate outcomes across demographic groups before deployment, not after problems emerge.
Remember that ethical AI isn’t about perfect solutions—it’s about transparent processes and deliberate choices. If your company can’t clearly explain which ethical trade-offs you’ve chosen and why, your “ethical AI commitment” is just another empty phrase in an already crowded field.