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AI is explaining itself to humans. And it's paying off
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The why of AI: LinkedIn’s subscription revenue has increased 8 percent after it adopted explainable artificial intelligence (XAI) that not only predicted clients at risk of canceling, but also explained how it arrived at the conclusion. (Unsplash/Souvik Banerjee)
E
xplainable AI is on the rise for accurate results about consumers’ decisions, but some experts are concerned that it will do the opposite.
Microsoft Corp-owned LinkedIn boosted subscription revenue by 8 percent after arming its sales team with Artificial Intelligence (AI) software that not only predicts clients at risk of canceling, but also explains how it arrived at the conclusion.
The system, introduced last July and described in a LinkedIn blog post on Wednesday, marks a breakthrough in getting AI to "show its work" in a helpful way.
While AI scientists have no problem designing systems that make accurate predictions on all sorts of business outcomes, they are discovering that to make those tools more effective for human operators, the AI may need to explain itself through another algorithm.
The emerging field of explainable artificial intelligence, or XAI, has spurred big investment in Silicon Valley as start-ups and cloud giants compete to make opaque software more understandable. It has also stoked discussion in Washington and Brussels, where regulators want to ensure that automated decision-making is done fairly and transparently.
AI technology can perpetuate societal biases like those around race, gender and culture. Some AI scientists view explanations as a crucial part of mitigating those problematic outcomes.
United States consumer protection regulators, including the Federal Trade Commission, have warned over the last two years that AI that is not explainable could be investigated. The EU could pass next year the Artificial Intelligence Act, a set of comprehensive requirements that includes that users be able to interpret automated predictions.
