Why Organizations Might Want to Design
and Train Less-Than-Perfect AI

Katherine Miller

This piece was originally published by the Stanford University Graduate School of Business.

Companies “need to change the way they motivate people in environments where parts of their jobs are done by AI,” argues a Stanford economics professor.

These days, artificial intelligence systems make our steering wheels vibrate when we drive unsafely, suggest how to invest our money, and recommend workplace hiring decisions. In these situations, the AI has been intentionally designed to alter our behavior in beneficial ways: We slow the car, take the investment advice, and hire people we might not have otherwise considered.

Each of these AI systems also keeps humans in the decision-making loop. That’s because, while AIs are much better than humans at some tasks (e.g., seeing 360 degrees around a self-driving car), they are often less adept at handling unusual circumstances (e.g., erratic drivers).

In addition, giving too much authority to AI systems can unintentionally reduce human motivation. Drivers might become lazy about checking their rearview mirrors; investors might be less inclined to research alternatives; and human resource managers might put less effort into finding outstanding candidates. Essentially, relying on an AI system risks the possibility that people will, metaphorically speaking, fall asleep at the wheel.

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