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Explainable AI Explained - by an AI

We believe explainable AI is a crucial concept for the responsible and ethical development and use of AI. Thus, we work to spread awareness and knowledge about this field. As a starting point, we want to provide an explanation of explainable AI in simple terms. To emphasize why it is so urgent, that we understand and apply this: we asked the openAI Chat to write the explanation.

Here it is, completely unedited; highlights added for easier reading.



Explainable Al, also known as transparent Al or interpretable Al, is a branch of artificial intelligence research that focuses on developing algorithms and systems that can provide clear explanations for their predictions and actions.


The idea behind explainable Al is to make it easier for people to understand how Al systems work and why they make certain decisions, which can help to build trust and increase the accountability of Al systems.


This is particularly important in fields like healthcare, finance, and criminal justice, where the decisions made by Al systems can have significant consequences for people's lives. Some of the key principles of explainable Al include:

  • Transparent decision-making: Explainable Al systems should provide clear explanations for their predictions and decisions, which should be easy for people to understand. This can help to build trust and increase the accountability of Al systems.

  • Human-centered design: Explainable Al systems should be designed with the needs and preferences of human users in mind. This means considering factors like how people think and make decisions, and how they prefer to receive information.

  • Explainability as a first-class goal: Explainability should be an integral part of the design of Al systems, rather than an afterthought. This means considering explainability throughout the entire development process, from the initial design phase to the deployment and maintenance of the Al system.

  • Trade-offs between accuracy and explainability: In some cases, it may be necessary to sacrifice some accuracy in order to improve the explainability of an Al system. This trade-off should be carefully considered and balanced in order to achieve the best overall results.


To spark discussion, we chose to publish this entirely unedited. What do you think: has openAI explained explainable AI correctly?

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