With AI adoption soaring in all industries and organizations adopting AI-based tools and applications, it is no surprise that cybercriminals are already looking for targets and leveraging these tools for their own benefit. But while protecting AI from potential cyberattacks is important, AI risk issues go far beyond security. Globally, governments have begun to regulate how AI is developed and used, and if it is found to be discovered in an inappropriate way, companies can cause huge reputational damage. Businesses today find that using AI in an ethical and responsible way is not just the right thing to do – building trust, maintaining compliance, and even improving product quality is crucial.
The regulatory reality of AI
For suppliers that provide AI-based solutions, a rapidly growing regulatory landscape should be a serious concern. For example, the EU AI Act was adopted in 2024 and adopted a risk-based approach to regulation of AI and considered that systems engaging in practices such as social scoring, manipulation and other potentially immoral activities to be “unacceptable”. These systems are completely prohibited, while other “high-risk” AI systems should comply with stricter obligations for risk assessment, data quality and transparency. The fine for non-compliance is serious: Companies found to use AI in an unacceptable way can be fined up to €35 million, accounting for 7% of their annual turnover.
The EU AI bill is only a piece of legislation, but it clearly illustrates the huge cost of failing to meet certain ethical thresholds. State-owned states such as California, New York, Colorado have developed their own AI guidelines, most of which focus on factors such as transparency, data privacy, and prevention of bias. Despite the lack of the implementation mechanism enjoyed by the Government, it is worth noting that all 193 UN members unanimously confirmed that in the 2024 resolution, the “human rights and fundamental freedoms” must be respected, protected and promoted throughout the life cycle of the artificial intelligence system. Worldwide, human rights and moral considerations are growing in AI.
Reputational Impact of Poverty AI Ethics
Although the compliance issues are very real, the story doesn’t end. The truth is that prioritizing ethical behavior can fundamentally improve the quality of AI solutions. If the AI system has inherent bias, it is unfavorable for ethical reasons, but it also means that the product is not working well. For example, some facial recognition technologies have been criticized for failing to recognize dark-skinned faces as well as light-colored faces. If facial recognition solutions fail to identify the majority of subjects, this creates a serious ethical problem, but it also means that the technology itself does not provide the expected benefits and that the customer will not be satisfied. Resolving bias can both alleviate ethical problems and improve the quality of the product itself.
Concerns about bias, discrimination and fairness may carry regulators’ hot water suppliers, but they also erode customer confidence. It is best to have certain “red lines” when it comes to using AI and which providers to work with. AI providers associated with disinformation, mass surveillance, social scoring, oppressive governments, and even general lack of accountability can upset customers, and vendors offering AI-based solutions should keep this in mind when considering who to work with. Transparency is almost always better – those who refuse to reveal how AI is used or their partner looks like something hidden, which usually doesn’t promote positive emotions in the market.
Identify and mitigate moral red flags
Customers are increasingly learning to look for signs of immoral AI behavior. Over-promoted but impatient vendors are not honest about what their AI capabilities might actually do about their solutions. Data practices (e.g., excessive data scratches or the inability to opt out of AI model training) can also cause red flags. Today, vendors using AI in their products and services should have a clear open governance framework and a responsible mechanism. Those who force arbitration (or worse, no recourse at all) may not be good partners. The same is true for vendors who are reluctant or unable to provide metrics that they evaluate and address bias in 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 suppliers who use AI in their products, it is important to convey ethical considerations to their customers. Those who train their own AI models need a strong bias prevention process, while those who rely on external AI vendors must prioritize partners with a reputation for fair behavior. It’s also important to provide customers with options: many people are still uncomfortable trusting their data to AI solutions and provide an “option out” for AI features so they can experiment at their own pace. Transparent sources of training data are also crucial. Again, it’s ethical, but it’s a good business too – if customers find that the solution they rely on is trained in copyrighted data, they can be open to regulatory or legal behavior. By putting everything in public, vendors can build trust with their customers and help them avoid negative results.
Prioritizing ethics is a smart business decision
Trust has always been an important part of every business relationship. AI hasn’t changed that, but it introduces new considerations that vendors need to address. Ethical issues are not always the most important for business leaders, but when it comes to AI, immoral behavior can have serious consequences – including reputational damage and potential regulatory and violations. Worse, the lack of attention to ethical considerations such as bias mitigation can actively harm the quality of supplier products and services. With the acceleration of AI adoption, vendors are increasingly aware that prioritizing ethical behavior is not just the right thing, it is also a good thing.
