Thanks to AI, faster results and new insights can already be achieved today. AI learns both autonomously - i.e. without constant supervision - and adaptively: depending on what we show it, it will find and learn different things.

This may sound positive, but it can also have negative effects. Awell-known example of this is the so-called AMS algorithm, which divides jobseekers into three classes: high, medium and low chances of finding a permanent position within the next six months. Critics accuse this system of "cementing" existing social grievances, among other things.

This means that AI makes decisions on an ideological basis that is considered to have already been overcome. This means that AI touches on ethical issues which - if not properly addressed - can quickly lead to problems and even public criticism.

The biggest technical challenges at the moment

The topic of AI and ethics is currently reflected in the following three areas in particular.

1. Bias: Put simply, these are prejudices and resentments cast in algorithms. The consequences are, for example, that an AI system only suggests men or only people with light skin for vacancies with the same qualifications.
2. Lack of transparency: There are situations in which it is not possible to determine how algorithms arrive at certain outputs from certain data inputs. To get to grips with this problem, researchers are working on the topic of "Explainable AI".
3. Data protection: This is a good, but also a right that needs to be protected, especially in the context of AI. Technical solutions such as anonymization or regulatory approaches should help here.

How to prevent ethically questionable results

To minimize the risk of an AI solution delivering morally undesirable results, companies can take three simple approaches.

1. Raise awareness: There needs to be an awareness and understanding of these issues. This also paves the way for a strategic approach when it comes to the use of AI. It also prevents AI from being blindly implemented first and then having to put out fires later.
2. Take a stand: There needs to be a statement backed by management - a manifesto of sorts - on where the company stands on ethics and AI and what the basic conditions are, aside from laws, that it feels obligated to follow.
3. Establish processes: Processes are needed to systematically and regularly review the risk potential of AI applications. These range from conception, planning and development through to use.

How industrial SMEs are planning AI solutions

Once the foundations have been laid in terms of AI and ethics, the next step is concrete planning. Here, coordination with the corporate strategy is at the top of the list. The key question is: How does AI fit into our organization and how can it help us achieve our goals?

Planning does not only include technical aspects: Both the business and human aspects are equally important. The latter addresses the impact of AI decisions on users.

The next step is a good roadmap. In practice, it has been shown that companies should ideally start with quick wins - where there is enough data of sufficient quality and people have already been able to draw useful conclusions from this data without much effort.

Last but not least, competent partners are needed during implementation to ensure that AI projects are not only successful from a technical and organizational point of view, but also with regard to ethical issues.

Conclusion: public criticism and loss of credibility greet us

Anyone working with data-driven AI, for example to classify produced goods into "OK" and "rejects", is always making a very strong generalization claim. This is because the basic premise here is that an accurate statement can be made about future events based on observed examples. This means that AI solutions can be scaled well. At the same time, however, errors and ethically questionable results are also scaled.

As a result, AI projects need to be very well planned. Embedding AI in the existing organization means a serious and long-term change process that not only encompasses technical aspects, but also calls into question some fundamental corporate convictions.

Companies that do not approach the issue with the necessary caution run the risk of losing their 'license to operate'due to public criticism and a lasting loss of credibility. For customers today, added value is no longer defined solely by functionality, but increasingly also by ethical aspects and consideration of social expectations.