How to answer ethical concerns about AI - by Adam Faik

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How to answer ethical concerns about AI<br>Listen to the strongest version of the concern. Sort it into one of four kinds. Concede what’s true, fix what you control, correct what’s mistaken, respect what’s a value.

Adam Faik<br>Jul 26, 2026

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Every time I talk about AI with someone, the conversation ends up in the same place. Not on models. Not on prompts. On ethics. Someone says the data centers are draining water tables. Someone says the training data was scraped from people who never agreed. Someone says they became a designer to design, not to review a machine’s output. The objections that stick aren’t about capability anymore, they’re about conscience.<br>In early 2025, I watched a French designers’ collective called Designers Éthiques lay out every one of these objections in one 40-minute talk. It’s stayed with me since. Not because it was contrarian, but because it wasn’t. These were rigorous professionals making the strongest version of the case: an eco-design specialist, a design researcher, an ergonomist. No trolling, no panic. It’s the sharpest compact map I’ve found of what your own team members are thinking and mostly not saying in the meeting.<br>If you’re leading a team through an AI transition, you’ve met these objections too. Maybe in a retro, maybe in a 1:1, maybe as a silence that never turns into usage. And you’ve probably been handed exactly one playbook for them: overcome the resistance. I think that playbook is wrong. Your skeptics are mostly raising real problems, and the fastest way to lose them is to debate.<br>The better move: sort each concern into one of four kinds. Some are true, and you change what you adopt. Some are risks you control, and you change how you adopt. A few are misconceptions, and you correct them with evidence instead of marketing. One or two are values, and you respect them. The payoff isn’t a converted team. It’s AI use your team can defend out loud.<br>The playbook, at a glance:

The trap. I’ll show you why winning the argument against a skeptic loses the team.

The sort. Here’s the four-kind triage that replaces the debate: true, fixable, misread, or a value.

The map. You get the seven concerns you’ll hear most, each with an honest verdict, the move that follows, and the one resource worth forwarding.

The practice. What a defensible team AI posture looks like once the sorting is done.

By the end, you’ll have a verdict and a concrete next move for the seven objections coming your way this quarter, plus the one sentence that keeps a skeptic on your team. You stop dreading the ethics conversation and start using it to make your team’s AI practice sharper. No slides required, no philosophy degree either.<br>Let’s sort this out.

Why winning the argument loses the team

The talk opens with a moment I can’t stop thinking about. At a green-IT conference workshop, in front of the most skeptical crowd available, the speakers ran a session asking “AI: in or out?” Almost every group landed on “in,” reasoning that it’s here anyway, there’s no choice, so let’s make it as clean as possible. One speaker was troubled by exactly that phrasing. ”We have no choice” is not what agreement sounds like, it’s what resignation sounds like. If your team adopts AI in that spirit, you didn’t win them. They just stopped telling you things.<br>There’s a second reason the debate is rigged before you open your mouth. Your team members already met AI adoption as users, and it wasn’t polite. The design researchers at Limites Numériques documented the pattern: AI buttons pushed front and center, features switched on by default like Strava’s Athlete Intelligence, dialogs that offer “try it” and “not now” but never “no.” Sparkles and purple everywhere, the visual vocabulary of magic. When you pitch AI to your team with vendor enthusiasm, you pattern-match to the forced adoption they already resent. They’ve heard “this will make everything better” before, from a button they couldn’t refuse.<br>Organizational research has said this plainly for years. In their 2008 Academy of Management Review paper, Ford, Ford and D’Amelio argued that resistance to change isn’t a defect in the resisters. Change agents cause a good share of it themselves, and resistance is better treated as a resource: engagement, feedback, proof that people take the change seriously. The person pushing back is often the person paying the most attention.<br>If you read How to lead a tech team through the AI shift, you might spot a tension here. I argued there for the 20-60-20 rule: pour your energy into your champions and the watching middle, and stop exhausting yourself arguing with the resistors. I stand by every word, and this article doesn’t change the math. Sorting is not arguing, and what follows is not a conversion campaign. It’s for the conversations that find you anyway: the 1:1 where a concern lands on the table, the team meeting where a hand goes...