| Autor: |
Werder, Karl, Cao, Lan, Ramesh, Balasubramaniam, Park, Eun Hee |
| Předmět: |
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| Zdroj: |
Communications of the ACM; Dec2024, Vol. 67 Issue 12, p31-34, 4p |
| Abstrakt: |
The lack of diversity in the artificial intelligence (AI) field exacerbates social biases in AI systems, which often reflect the perspectives of their creators. Addressing these biases requires more than technical solutions; it necessitates a social approach to tackle their root causes. Diverse teams have been shown to improve innovation, performance, and fairness by broadening perspectives and reducing blind spots in AI development. To achieve meaningful change, organizations must implement robust strategies, such as fostering diversity skills, mirroring stakeholder representation in teams, and developing sustainable talent pipelines, while avoiding superficial efforts like "diversity washing." |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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