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Related Questions
- What are some common biases that machine learning models can exhibit, and how can human evaluators identify them in model outputs?
- How can human evaluators assess the fairness of machine learning model outputs in the context of sensitive attributes such as race, gender, and age?
- What are some best practices for human evaluators to ensure that their own biases do not influence their evaluation of machine learning model outputs?
- How can human evaluators evaluate the equity of machine learning model outputs in terms of their impact on different demographic groups?
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- How can human evaluators assess the explainability of machine learning model outputs to improve fairness and equity?
- What are some strategies for human evaluators to use to ensure that machine learning models are aligned with human values and ethics in terms of fairness and equity?
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