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Learn more about the latest technologies.

Explainable AI: The next stage of human-machine collaboration

In brief Many artificial intelligence applications today are effectively “black boxes” lacking the ability to “explain” the reasoning behind their decisions.

As AI expands into areas with large impact on people, such as health care, it will be critical to subject the technology to greater human scrutiny.

Explainable AI won’t replace human workers; rather, it will complement and support people, so they can make better, faster, more accurate decisions.

Use cases for Explainable AI include detecting abnormal travel expenses and assessing driving style, based on Accenture Labs research.
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This technology is able to comprehend language in its natural form (whether that is a legal contract or a spoken question).
Early attempts to program a computer to understand language involved a series of rules.
While this is fine for some basic concepts, it becomes complicated as you cater for exceptions to the rules.
Increasing computer power means that instead of trying to codify thought processes as rules,
the latest machine learning tools use statistical pattern recognition techniques to create their own rules (known as predictive algorithms) from large volumes of examples.
For example, if you show such a system a collection of documents and their translations into another language,

the system can determine the statistical patterns between the documents and work out how to translate from one language to the other,
without having to understand what the individual words mean or the underlying rules of grammar.