When an artificial intelligence system makes a "bad decision," most organizations can't really explain why. They aren't entirely sure which data trained it, where the error was, which version it was running or which third-party components were involved.
When an artificial intelligence system makes a "bad decision," most organizations can't really explain why.
They aren't entirely sure which data trained it, where the error was, which version it was running or which third-party components were involved.
This is an ethical and regulatory blind spot that creates risk for tech leaders in districts of all sizes, and it's driving interest in a newer framework called the AI bill of materials.
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When an artificial intelligence system makes a "bad decision," most organizations can't really explain why. They aren't entirely sure which data trained it, where the error was, which version it was running or which third-party components were involved. This is an ethical and regulatory blind spot that creates risk for tech leaders in districts of all sizes, and it's driving interest in a newer framework called the AI bill of materials.
Many organizations have not yet thought about applying…
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