Fairer machine learning in the real world: Mitigating discrimination without collecting sensitive data
Decisions based on algorithmic, machine learning models can be unfair, reproducing biases in historical data used to train them. While computational techniques are emerging to address aspects of these concerns through communities such as discrimination-aware data mining (DADM) and fairness, accounta...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2017-11-01
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| Series: | Big Data & Society |
| Online Access: | https://doi.org/10.1177/2053951717743530 |