6 tips for facilitating ethical AI in the enterprise
As the clamor for so-called “ethical AI” nears its crescendo, consultants recommend steps for creating fair and balanced algorithms. Hint: There’s no magic bullet.
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Artificial intelligence (AI) is potentially the single most disruptive technology of the digital era, as enterprises explore ways to harness machine learning (ML) and other AI tools to mine customer insights, identify talent and secure corporate networks. And while IT departments can quickly roll out and benefit from most technologies, evidence suggests that CIOs should exercise extreme caution when implementing AI, including employing technologies with strong ethical considerations.
The reason? AI suffers from a big bias problem. In one example, Amazon.com scrapped a recruiting tool after it failed to fairly rate women for software developer jobs and other technical posts. In another example, MIT and University of Toronto researchers found that the company’s facial recognition software mistakes women, especially those with dark skin, for men.
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Biases abound in AI
Amazon.com is not alone, as AI issues have surfaced in other companies and across other high-stakes domains. A Facebook program manager encountered algorithmic discrimination while testing the company’s Portal video chat device. ProPublica showed that software used across the U.S. to predict future criminals was biased against African-Americans. A study of fintechs by UC Berkeley found that both face-to-face decisions and algorithms used in mortgage lending charged Latinx/African-American borrowers higher interest rates.
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