The U.S. can improve its AI governance strategy by addressing online biases

Stakeholders in artificial intelligence must trace back to the roots of the problems, which lie in the lack of diversity in design teams and data that continues to carry on trauma and discrimination of the past, Nicol Turner Lee and Samantha Lai write...

...While roundtable participants didn’t have full consensus on the most commonly accepted definition of AI bias, they did offer perspectives on the outcomes that should be further investigated, especially those that seem to collide with the public interest and equity. Generally, diversity and inclusion are treated as afterthoughts in AI development and execution, and flagged when systems go awry, resulting in quick fixes that do not address the breadth of such harmful technologies...