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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, com...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
[Cambridge, Massachusetts] :
MIT Sloan Management Review,
2022.
|
| Edición: | [First edition]. |
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 245 | 1 | 0 | |a Manage AI bias instead of trying to eliminate it : |b to remediate the bias built into AI data, companies can take a three-step approach / |c Sian Townson. |
| 246 | 3 | |a Manage artifical intelligence bias instead of trying to eliminate it | |
| 250 | |a [First edition]. | ||
| 264 | 1 | |a [Cambridge, Massachusetts] : |b MIT Sloan Management Review, |c 2022. | |
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| 520 | |a 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. | ||
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Artificial intelligence |x Industrial applications. | |
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