Artificial intelligence is changing our lives, our work and our decisions. More and more processes are being automated: Who is invited to a job interview? What medical treatment is recommended? What content do we see in our feeds? The technologies behind them often appear neutral, objective and efficient. But appearances are deceptive. AI does not make decisions in a vacuum – it is based on data, rules and assumptions made by humans. And this is where the ethical dilemma begins. Because what happens when these systems discriminate, act non-transparently or are used manipulatively? Then it becomes clear that it is not enough for AI to function technically. It must also be socially responsible. This blog post takes a closer look at how AI that is also ethically justifiable can be designed and the challenges involved.

What does “ethical AI” actually mean?

Ethical AI means more than just putting “good intentions” into a technical system. It should not only function efficiently, but also act fairly, transparently and responsibly. AI must be developed and used within the framework of social values. This differs from the technical or economic goals of AI developers, who often focus on efficiency and cost reduction. This does not take into account the significance of AI use for individuals. What does it mean for society? For minorities? Particularly in sensitive areas such as application procedures, medical diagnostics or criminal prosecution, it becomes clear how important ethical principles are.

If an algorithm pre-sorts applications, developers must ensure that it does not systematically disadvantage any groups. If an AI assists in cancer diagnosis, its decision must remain clear and understandable. And when automated systems assess people, organizations must establish clear rules to define responsibility. In short, ethical AI puts people at the center – not just as users, but as those affected. Only if technology respects our fundamental values can it create trust in the long term and enable genuine innovation.

What are the ethical challenges?

Ethical challenges in AI are not a marginal issue – they are at the center of the current debate. Because what happens when an algorithm is not neutral, but biased? This is exactly what happens when AI is trained on biased data, and this causes problems:

• Social prejudices creep into training data. The COMPAS algorithm in the USA assesses the recidivism risk of offenders and has systematically assigned lower scores to people of color (Rütten, 2018).
• Another problem: lack of transparency. Many modern AI models are so-called “black boxes”. Even developers often do not know exactly why a system comes to a certain decision.
• It is also questionable who bears the consequences if an AI makes an unethical or wrong decision. The manufacturer? The company? The user?
• Then there is the use of AI for surveillance and manipulation – from social scoring in China to deep fakes in election campaigns.

This shows that AI is not good or bad. It depends on how we design and use it. That’s why we must establish clear ethical guidelines—before the harm outweighs the benefits

What do we need for ethical AI?

For ethical AI, we need more than technical excellence – we need attitude, responsibility and clear framework conditions:

• Transparency: Explainable AI was developed to make AI systems comprehensible. It helps to make decisions understandable and thus promote trust – not only for developers, but also for users and those affected.

• Fairness: An ethical AI must not discriminate against people – neither consciously nor through its training data. This means that data must be regularly checked for bias. External audits and diverse development teams help to identify blind spots at an early stage.

• Data protection: The GDPR already shows what privacy-by-design can look like in concrete terms: Data minimization, anonymization and secure systems should be the standard – not the exception.

• Responsibility: Clear governance structures are needed: who develops, who checks, who is liable? Without defined responsibilities, accountability quickly falls by the wayside.

• Inclusion: Ethics are not universal – they also depend on cultural and social contexts. The more diverse the perspectives in AI development, the fairer the result. Standards and regulation play a major role here. The EU AI Act, for example, sets binding rules for the first time – and shows that ethical AI is not only possible, but feasible. We just have to want it – and implement it.

Who bears the responsibility?

Responsibility for ethical AI does not lie with a single group, but is a shared task. Developers and companies have a great responsibility: they decide how algorithms are built, trained and used. Anyone developing AI should not only ask themselves whether something is technically feasible – but also whether it is socially acceptable. At the same time, it is up to politicians. It must set the framework within which innovation can develop ethically. The EU AI Act is a good example of this: it makes it clear that certain risks are non-negotiable – for example when it comes to discrimination or surveillance.

We as users also have a role to play. How we interact with AI shapes its use. We must actively recognize both its opportunities and risks. Social debates play a crucial role, just as much as technological advances. Ethical AI emerges through collaboration. It requires contributions from experts in ethics, law, sociology, design, and business—tech expertise alone isn’t enough. Interdisciplinary teams are not a “nice-to-have”, but the basis for ensuring that AI really serves people – and not the other way around.

Conclusion: Can AI act ethically?

Ethical AI is not just a future concern or a “nice to have”—it is essential today. Trust and acceptance depend on it, as does long-term success. Only when AI operates fairly, transparently, and responsibly can it reach its full potential without causing harm. Companies that prioritize ethical guidelines and actively implement them enhance both their innovative strength and credibility, internally and externally. This commitment forms the foundation for sustainable value creation in an increasingly data-driven world.

There are already tools, frameworks and regulatory developments such as the EU AI Act that provide guidance. It is crucial that ethics is not outsourced, but integrated – into teams, processes and code. Now is the right time to take responsibility and help shape ethical AI. Because the sooner we set the course, the greater the chance that AI will not only become smarter, but also fairer. Technological progress doesn’t wait – we should shape it in such a way that everyone benefits from it.