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Explainable Artificial Intelligence (XAI)

Last updated: May 31, 2022

What Does Explainable Artificial Intelligence (XAI) Mean?

Explainable AI (XAI) is artificial intelligence that explains how specific outcomes were generated in a way that can be understood by a human, provides users with a certain level of confidence in the accuracy of its outputs -- and is only used under the conditions for which it is intended.

The goal of XAI is to make sure that artificial intelligence programs are transparent regarding both the purpose they serve and how they work. Explainable AI is a common goal and objective for data science engineers and others trying to move forward with artificial intelligence progress.

Explainability provides transparency by allowing data scientists to screen data and algorithmic outcomes for unacceptable outcomes, including those that have inadvertent bias. It is one of the five major principles that characterize trust in AI systems. The others four principles are resiliency, lack of bias, reproducability and accountability.

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Techopedia Explains Explainable Artificial Intelligence (XAI)

The idea behind explainable AI is that AI programs and technologies should not be “black box models” that people cannot understand. Too often, experts say, artificial intelligence programs sacrifice transparency and explainability for experimental sophisticated results. Powerful algorithms may turn out useful results without actually showing how they arrived at these results.

Explainable artificial intelligence is also a key part of applying ethics to AI. As tech leaders like Elon Musk have pointed out, artificial intelligence may end up having net positives or negatives, depending on how it is used by humans, and how it is built by humans.

Explainable AI helps in the pursuit of ethical AI, because it shows how machines are performing computational work and where there may be problems.

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Synonyms

Transparent Artificial Intelligence, Explainable AI

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Artificial Intelligence Data Science

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