Manage AI bias instead of trying to eliminate it to remediate the bias built into AI data, companies can take a three-step approach
The negative effects of bias in artificial intelligence models' underlying data has made headlines, and companies need to find ways to address it. But it's impossible to completely abolish bias in AI data to equitably account for diverse populations -- so instead, companies should remediat...
| Otros Autores: | |
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| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
[Cambridge, Massachusetts] :
MIT Sloan Management Review
2022.
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| Edición: | [First edition] |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009823024006719 |
| Sumario: | The negative effects of bias in artificial intelligence models' underlying data has made headlines, and companies need to find ways to address it. But it's impossible to completely abolish bias in AI data to equitably account for diverse populations -- so instead, companies should remediate it to deliberately compensate for unfairness. The author describes a three-step process that can yield positive results for leaders looking to reduce the impact of AI bias. |
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| Notas: | Reprint 64321. |
| Descripción Física: | 1 online resource (5 pages) |
