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Related Questions
- What are the key factors that contribute to successful collaborations between human evaluators and AI developers for bias-reduced models?
- Can you give an example of a model that was improved through co-design with human evaluators, and what specific bias-reduction strategies were applied?
- How did human evaluators and AI developers work together to implement explainability and transparency features in a model, reducing potential biases?
- What are some best practices for human evaluators to effectively communicate their findings to AI developers, ensuring they are addressed in model revisions?
- In a scenario where human evaluators identified a bias in an AI model, how could AI developers use this input to improve the model while also ensuring it remains inclusive and fair?
- Can you describe a process for human evaluators to assess the fairness and efficacy of an AI model during development, and how they could provide actionable feedback for AI developers?
- What are some strategies human evaluators and AI developers could employ to mitigate the negative consequences of bias in machine learning models, such as discrimination or perpetuation of existing social inequalities?
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