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230302 ||| eng |
| 050 |
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4 |
|a Q335
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| 100 |
1 |
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|a Townson, Sian
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| 245 |
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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
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| 246 |
3 |
1 |
|a Manage artifical intelligence bias instead of trying to eliminate it
|
| 250 |
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|a [First edition]
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| 260 |
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|a [Cambridge, Massachusetts]
|b MIT Sloan Management Review
|c 2022
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| 300 |
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|a 5 pages
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| 653 |
|
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|a Artificial intelligence / Industrial applications
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| 653 |
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|a Artificial intelligence / Industrial applications / fast
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| 653 |
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|a Intelligence artificielle / Applications industrielles
|
| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b OREILLY
|a O'Reilly
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| 500 |
|
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|a Reprint 64321
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| 856 |
4 |
0 |
|u https://learning.oreilly.com/library/view/~/53863MIT64321/?ar
|x Verlag
|3 Volltext
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| 082 |
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|a 006.3
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| 520 |
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|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
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