The AI ethics question

Customers deserve transparency if you are using automation

Jason Alexander

When I started ChiefAI, I thought the biggest challenge would be helping businesses choose and implement AI tools. The harder conversations, it turns out, are about questions without clear answers yet.

What happens when your AI makes a biased decision? Who’s responsible when an automated system gets something wrong? How much should customers know about what’s being automated? These aren’t hypothetical. They’re real issues New Hampshire businesses are navigating right now, mostly without guidance.

The temptation is to put these questions off until later. But that’s backwards.

Decisions you make early set patterns that are hard to change. Let’s talk about what responsible AI adoption actually looks like.

AI systems learn from data. If that data contains biases, the AI learns those biases, too. This isn’t a glitch; it’s how the technology works.

Example: Train an AI to screen job applications based on your hiring history. If past hiring inadvertently favored certain groups, the AI will replicate and amplify those patterns. It doesn’t know it’s being unfair; it’s just pattern-matching.

These biases aren’t always obvious. And because AI operates at scale, a biased system can affect far more people than a biased human ever could.

Three responses: First, recognize that bias is a risk whenever AI makes decisions about people: hiring, lending, customer service, marketing. Second, test your systems and examine outcomes. Are certain groups being treated differently? Third, maintain human oversight for consequential decisions.

AI can assist, but a person should make the final call on anything that significantly affects someone’s livelihood.

How much should you tell customers about your use of AI? No universal answer exists, but some principles hold.

People don’t like feeling deceived. If they’re talking to a chatbot, they should know it. If AI is affecting decisions about them, they deserve to know. Basic transparency builds trust.

That said, most customers care about outcomes, not implementation details. Something like “Our customer service chat is AI-assisted, with human support available when you need it” is honest without being alarmist.

The exception is high-stakes decisions. If AI is involved in creditworthiness, employment or health care, people deserve a clear explanation of how decisions are made and how they can appeal.

AI systems are hungry for data, but that doesn’t mean you should feed them everything, especially customer information.

Have clear policies about what you’re collecting, how you’re using it and who has access. This isn’t just good ethics; it’s increasingly required by law. Privacy regulations are tightening, and consequences for mishandling data are real.

Think carefully before sharing customer data with AI vendors. Read the terms of service. Some platforms use your data to train their models, meaning sensitive information could end up benefiting competitors.

Anonymize where possible, use the minimum necessary, and never put confidential information into public AI tools unless you’re comfortable with it becoming public.

Governance simply means clear rules about how AI gets used in your organization. For small businesses, it doesn’t need to be complicated.

Designate someone responsible for AI decisions. Write simple guidelines: what’s allowed, what isn’t, when human review is required before AI outputs reach customers. Review systems periodically, even quarterly, to verify they’re working as intended and catch unintended consequences early. The goal isn’t bureaucracy; it’s clarity.

AI systems will make mistakes. Have a plan for catching errors quickly. Know how to override or shut down systems if needed.

Don’t become so dependent on automation that you can’t function without it. When something goes wrong, own it. Customers don’t care that it was the algorithm’s fault. Apologize, fix it, explain what you’re doing differently.

Responsible AI isn’t just about avoiding problems. It’s a competitive advantage. Customers increasingly concerned about AI will trust businesses that demonstrate thoughtfulness. Employees want to work for companies with values. And building good governance from the start is far easier than retrofitting it later.

You don’t need all the answers right now.

But you need to be asking the questions. The businesses that thrive with AI won’t just be the fastest adopters; they’ll be the most responsible ones.

Jason Alexander is the CEO and founder of ChiefAI, an AI advisory and consulting firm helping small and medium-sized businesses implement AI solutions strategically. A serial entrepreneur for the past 20 years, he is passionate about democratizing AI technology.