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Related Questions
- What are some key considerations for designing contextual evaluation metrics that capture model bias and fairness in prompt engineering?
- How do alternative approaches like demographic parity, equal opportunity, and equalized odds compare to traditional metrics in evaluating model fairness?
- Can you explain the concept of 'disparate mistreatment' and how it can be used to identify biased models in prompt engineering?
- What are some challenges associated with using alternative approaches to context-independent metrics for evaluating model fairness in prompt engineering?
- How can we incorporate considerations of intersectionality and subgroups into alternative approaches to evaluating model fairness in prompt engineering?
- Can you discuss the trade-offs between using alternative approaches that are more nuanced and detailed, but also more complex and computationally expensive, versus simpler metrics that may be less accurate?
- What are some recent advancements and research directions in the field of fair prompt engineering, and how are they influencing the development of new evaluation metrics?
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