As AI adoption soars and organizations in all industries embrace AI-based tools and applications, it should come as little surprise that cybercriminals are already finding ways to target and exploit those tools for their own benefit. But while it’s important to protect AI against potential cyberattacks, the issue of AI risk extends far beyond security. Across the globe, governments are beginning to regulate how AI is developed and used—and businesses can incur significant reputational damage if they are found using AI in inappropriate ways. Today’s businesses are discovering that using AI in an ethical and responsible manner isn’t just the right thing to do—it’s critical to build trust, maintain compliance, and even improve the quality of their products.
The Regulatory Reality Surrounding AI
The rapidly evolving regulatory landscape should be a serious concern for vendors that offer AI-based solutions. For example, the EU AI Act, passed in 2024, adopts a risk-based approach to AI regulation and deems systems that engage in practices like social scoring, manipulative behavior, and other potentially unethical activities to be “unacceptable.” Those systems are prohibited outright, while other “high-risk” AI systems are subject to stricter obligations surrounding risk assessment, data quality, and transparency. The penalties for noncompliance are severe: companies found to be using AI in unacceptable ways can be fined up to €35 million or 7% of their annual turnover.
The EU AI Act is just one piece of legislation, but it clearly illustrates the steep cost of failing to meet certain ethical thresholds. States like California, New York, Colorado, and others have all enacted their own AI guidelines, most of which focus on factors like transparency, data privacy, and bias prevention. And although the United Nations lacks the enforcement mechanisms enjoyed by governments, it is worth noting that all 193 UN members unanimously affirmed that “human rights and fundamental freedoms must be respected, protected, and promoted throughout the life cycle of artificial intelligence systems” in a 2024 resolution. Throughout the world, human rights and ethical considerations are increasingly top of mind when it comes to AI.
The Reputational Impact of Poor AI Ethics
While compliance concerns are very real, the story doesn’t end there. The fact is, prioritizing ethical behavior can fundamentally improve the quality of AI solutions. If an AI system has inherent bias, that’s bad for ethical reasons—but it also means the product isn’t working as well as it should. For example, certain facial recognition technology has been criticized for failing to identify dark-skinned faces as well as light-skinned faces. If a facial recognition solution is failing to identify a significant portion of subjects, that presents a serious ethical problem—but it also means the technology itself is not providing the expected benefit, and customers aren’t going to be happy. Addressing bias both mitigates ethical concerns and improves the quality of the product itself.
Concerns over bias, discrimination, and fairness can land vendors in hot water with regulatory bodies, but they also erode customer confidence. It’s a good idea to have certain “red lines” when it comes to how AI is used and which providers to work with. AI providers associated with disinformation, mass surveillance, social scoring, oppressive governments, or even just a general lack of accountability can make customers uneasy, and vendors providing AI based solutions should keep that in mind when considering who to partner with. Transparency is almost always better—those who refuse to disclose how AI is being used or who their partners are look like they are hiding something, which usually doesn’t foster positive sentiment in the marketplace.
Identifying and Mitigating Ethical Red Flags
Customers are increasingly learning to look for signs of unethical AI behavior. Vendors that overpromise but underexplain their AI capabilities are probably being less than truthful about what their solutions can actually do. Poor data practices, such as excessive data scraping or the inability to opt out of AI model training, can also raise red flags. Today, vendors that use AI in their products and services should have a clear, publicly available governance framework with mechanisms in place for accountability. Those that mandate forced arbitration—or worse, provide no recourse at all—will likely not be good partners. The same goes for vendors that are unwilling or unable to provide the metrics by which they assess and address bias in their AI models. Today’s customers don’t trust black box solutions—they want to know when and how AI is deployed in the solutions they rely on.
For vendors that use AI in their products, it’s important to convey to customers that ethical considerations are top of mind. Those that train their own AI models need strong bias prevention processes and those that rely on external AI vendors must prioritize partners with a reputation for fair behavior. It’s also important to offer customers a choice: many are still uncomfortable trusting their data to AI solutions and providing an “opt-out” for AI features allows them to experiment at their own pace. It’s also critical to be transparent about where training data comes from. Again, this is ethical, but it’s also good business—if a customer finds that the solution they rely on was trained on copyrighted data, it opens them up to regulatory or legal action. By putting everything out in the open, vendors can build trust with their customers and help them avoid negative outcomes.
Prioritizing Ethics Is the Smart Business Decision
Trust has always been an important part of every business relationship. AI has not changed that—but it has introduced new considerations that vendors need to address. Ethical concerns are not always top of mind for business leaders, but when it comes to AI, unethical behavior can have serious consequences—including reputational damage and potential regulatory and compliance violations. Worse still, a lack of attention to ethical considerations like bias mitigation can actively harm the quality of a vendor’s products and services. As AI adoption continues to accelerate, vendors are increasingly recognizing that prioritizing ethical behavior isn’t just the right thing to do—it’s also good business.
