Transcript of a talk (AI4CI series) discussing risks and ethics of using AI for customer profiling. Uses Amazon’s scrapped recruiting tool as a case study to show how training data bias produces discriminatory outcomes, then outlines operational risks (false patterns, perpetuated biases, lack of explainability), privacy/consent concerns, and practical mitigations including validation with customers, transparency, audits, and human oversight.
Back in 2018, before all the hype of generative AI and ChatGPT, Amazon scrapped an AI recruiting tool after discovering it was systematically discriminating against women. See, the system was designed to review job applications and rank candidates with one to five stars, kind of like product reviews on Amazon. But the AI had been trained on 10 years of resumes submitted to Amazon, most of which came from men. Because tech is historically a male dominated industry. Well, the system taught itself that male candidates were preferable. And so get this, it penalized resumes that included the word women's and downgraded graduates from all women colleges. Well, even after Amazon tried to fix these specific problems, they lost confidence that the system was truly gender neutral in other areas. And in the end, they had to abandon the entire project. Now, this illustrates something crucial about using AI for customer profiling. The technology is powerful, but it's not neutral. Every insight it generates, every pattern it identifies, every prediction it makes, it carries risks that go far beyond just getting the wrong answer. Let's start with the most obvious risk. AI can just be flat out wrong. It might identify customer segments that don't even exist. It might miss important differences between customer groups. It might make predictions based on coincidence rather than real patterns. When AI gets customer profiling wrong, you don't just waste time and resources. You can damage relationships with real customers by treating them in ways that don't match their actual needs or preferences. But being wrong, well, that's not the biggest risk. Sometimes the biggest risk is when AI gets things right, but in ways that cross ethical lines. AI can identify patterns that customers never intended to share. It can predict personal information that customers. Well, they might consider private. And it can group people in ways that feel maybe invasive or discriminatory. Consider Technova's taskflow users. AI might discover that customers who use certain features are more likely to cancel their subscriptions. Well, that's valuable business intelligence, no doubt. But what if the AI also discovers that customers from certain geographic regions or with certain usage patterns are more likely to have payment issues? Well, now you're kind of in an area where business insights, well, they might start to look like profiling in ways that could be unfair or discriminatory. There's another risk that's harder to spot, but just as dangerous. AI systems we know, they learn from historical data, which means they can perpetuate existing blind spots or biases. If your past customer data reflects biases and how you've marketed or sold your product, well, your AI will learn those biases and recommend that you continue them. So you end up reinforcing problems instead of solving them. Back to our case study with taskflow. If that's historically been marketed primarily to certain types of agencies, well, the AI might conclude that other types of agencies aren't good customers, even if that's just because they've never been given a real chance to try the product. Then there's the transparency problem. AI can identify customer patterns, yes, but it can't really explain why those patterns exist. You might know that customers who do X are likely to do Y, but you don't know the underlying reasons. And this creates a risk of making decisions based on correlations that might not represent real causation. You might change your product or marketing based on patterns that are actually just meaningless coincidences. Privacy is another major concern. The more detailed your customer profiles become, well, the more personal information you're collecting and analyzing. Customers might not realize how much you can infer about them from seemingly innocent data. And then there's the consent question. Just because customers agreed to let you collect their data doesn't necessarily mean they agreed to let you use AI to create detailed psychological profiles of them. The key is being thoughtful about how you use AI and putting safeguards in place to minimize the risks. I don't want to risk anything going wrong. And here are some ways you can do that. First, always validate AI generated insights with real customer feedback. Second, be transparent about the data you're collecting and how you're using it. Third, regularly audit your AI systems. Look for biases and unfairness. And fourth, put human oversight in place. AI should inform your decisions, not make them automatically. There should always be a sort of human in the loop who can question AI's recommendations and can consider the broader implications. Finally, remember that customer profiling is about serving customers better, not manipulating them. If AI insights are causing you to create better products and experiences, you're right on track. If they're helping you exploit customer vulnerabilities, you might have crossed a line there. The goal isn't to avoid these risks entirely. That's impossible. The goal is to be aware of them and use AI in ways that respect your customers privacy, dignity and autonomy. Remember, the most successful companies using AI for customer insights. They're not the ones who trust AI blindly. They're the ones who know how to spot and correct for AI's limitations.
