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- AI bias refers to biased results in artificial intelligence systems due to human biases in training data or algorithms1. Some sources of fairness and non-discrimination risks in AI include:
- Implicit bias
- Sampling bias
- Temporal bias
- Over-fitting to training data
- Edge cases and outliers2.
Learn more:✕This summary was generated using AI based on multiple online sources. To view the original source information, use the "Learn more" links.What is AI bias? AI bias, also called machine learning bias or algorithm bias, refers to the occurrence of biased results due to human biases that skew the original training data or AI algorithm—leading to distorted outputs and potentially harmful outcomes.www.ibm.com/topics/ai-biasTo list some of the source of fairness and non-discrimination risks in the use of artificial intelligence, these include: implicit bias, sampling bias, temporal bias, over-fitting to training data, and edge cases and outliers.www.weforum.org/agenda/2021/07/ai-machine-lear… - People also ask
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