Artificial intelligence (AI) has become an important tool in many fields in recent years, from medicine to business to industry. However, AI systems also have their limitations. One of the biggest challenges in their use is explaining the decisions these systems make. For example, it is a legal requirement that financial institutions disclose how AI-based lending decisions were made and the criteria used to approve or reject loans. In this article, we will therefore take a closer look at how AIs make decisions and how they can be influenced or guided, if necessary.

How does AI come to a decision?

AI systems are based on algorithms that are trained on a large amount of data. These can be structured or unstructured and can come from different sources, e.g. text, images or speech. The goal of training is to teach the AI system to recognize patterns in the data and make predictions based on these patterns.

There are several approaches to training AI systems. For example, in supervised learning, the AI system is presented with training data with known outcomes to train the system to predict those outcomes for new data. In unsupervised learning, on the other hand, the AI system is presented with data with no known results. Here, patterns in the data are to be detected and categorized. Finally, in reinforcement learning, the AI system is trained by exploring an environment and receiving feedback for its actions.

Regardless of the approach used, AI systems are represented by mathematical models. These models are often very complex and difficult to understand. This in turn leads to a situation where AI systems can make decisions that are difficult for humans to understand.

How can the decisions of AI be influenced?

The explainability of AI systems is an important factor limiting their use. However, being able to understand and influence the decisions can increase the adoption of these systems and maximize their potential. One way to do this is to bring transparency to the training process. This makes it possible to understand what data was used, what algorithms were applied, and what decisions were made. This then makes it easier to understand how the AI system arrived at its individual decision.

Another approach is to design AI systems to make decisions with a higher level of explainability. To this end, they can be configured to make decisions based on certain factors that are easier to understand. For example, an AI that makes medical diagnoses could be trained to make decisions based on certain symptoms or risk factors that are easier to explain. The use of so-called “interpretability tools” is also helpful. These can be used to visualize and explain the decisions made by AI systems. An example of such a tool is the so-called “Feature Importance Analysis”. It shows which features of the data contributed most to the decision.

The combination of AI systems with human intelligence also ensures transparency. This means that a human checks the decisions of the AI system and corrects them if necessary. This is also known as “human-in-the-loop” and is particularly important in critical areas such as medicine or aviation.

In conclusion, it is also important that AI systems are designed ethically. This means that they are set up in such a way that they do not make discriminatory decisions or misuse data. An example of an ethical challenge associated with AI systems is the use of data based on race or gender. AI systems should therefore be configured so that they do not include such data in their decision-making.

In summary, the explainability of AI systems is an important factor limiting their use. However, there are several approaches to increase the explainability of decisions. These can help improve understanding and adoption and maximize the potential of AI in a variety of domains.

If you would like to learn more about the explainability and application of AI models, simply contact our Artificial Intelligence experts.

About the author

Mathias Herrmann


Mathias Herrmann is an internet entrepreneur going back to the Internet’s early days with a deep interest in digital and future technologies. For over 20 years, he has been helping companies make the most of their data by forging innovative solutions – without forgetting the people behind the data.

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