What Ethical AI Isn't
To understand what ethical AI is, it helps to start with what it isn’t. Unethical AI harms people: not just those who are already vulnerable, but anyone. Even celebrities have been impacted by deepfake software. And across industries, we’ve seen the consequences play out in real life.
Facial recognition tools have misidentified Black faces at disproportionate rates, leading to false arrests. Amazon’s hiring algorithm once penalized résumés that mentioned “women.” Tools meant for housing applications, mental health counseling, even warfare—AI is already being used in all of these areas, and real people are being harmed.
These aren’t edge cases, they’re signals of the times we live in, and they’re happening now.
Bias Isn’t a Bug, It’s Baked In
Machines aren’t neutral. They reflect the people who build them and the data they’re trained on. That means bias isn’t a one-off risk: it’s an expected outcome unless we actively intervene.
I’m not advocating for a world without AI, far from it. I believe its potential is vast, and we’re only just beginning to scratch the surface. But the more powerful our tools become, the more intentional we have to be about how we use them.
AI Doesn’t Follow the Old Playbook
When I started in tech in 2010, most software followed a clear lifecycle: you’d build a feature, get it reviewed, test it, and then ship it to production. Bugs would appear, but they were fixable. The path was mostly linear.
AI development isn’t like that.
Language models hallucinate—meaning they make things up. You can’t test them like traditional software because they don’t behave predictably. Model evaluations help, but even with dozens of tests, it’s easy to miss the one prompt that generates something false, biased, or harmful.
That unpredictability is exactly why ethics needs to be part of the design process, not an afterthought.
Thoughtful Design Is Ethical Design
Companies don’t set out to build harmful products. But without deliberate checks, unintended consequences are inevitable.
If you’re building with AI, ask yourself:
- Who could be hurt if this system fails, or worse, if it succeeds in the wrong way?
- Have I tested this product with people from different backgrounds and experiences?
- Do I understand the limitations of the model I’m using?
Diverse user testing isn’t just a checkbox: it’s a form of risk mitigation. No single person can anticipate every blind spot, but a broader test group can surface problems before they reach production.
You Don’t Need to Have All the Answers Yet
We’re not going back to a pre-AI world, nor should we. The field is exciting, and the possibilities are immense. But as builders, we have a responsibility to ask better questions before the harm happens.
And if you’re a founder with a new AI idea, especially one navigating a complex space like healthcare, legal tech, or education, you don’t have to do this alone.
At Hyperfocus AI, we help you build real products that work responsibly. So let’s build something that reflects the world as it is: complex, diverse, and human-centered.