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Understanding how an artificial intelligence system generates its predictions is crucial for avoidance of bias, says a startup that has been developing what it calls “explainability in AI.”
A person’s application for a loan, for example, being rejected or approved by AI needs to be analyzed for fairness.
“You can actually create a dystopian world where some people get really great decisions from your systems and where some people are left out,” said Krishna Gade (pictured), co-founder and chief executive officer of Fiddler Labs Inc.
Modeling data can be old or sourced incorrectly. That, among other things, causes problems. Gade believes that the black-box nature of an AI or machine learning engine, where one can’t simply open up the code and read it like you can traditional software, needs special tools.