Yes, you're correct that human oversight and evaluation can introduce their own biases. However, including human oversight and evaluation in the AI development process can still help reduce the risk of introducing bias. By being mindful of their own biases, reviewers can help to counteract them and identify any biases in the AI system. Additionally, incorporating diverse perspectives and experiences into the review process can help to mitigate the impact of individual biases.

You're also correct that it is important to assume the presence of bias and to use models as a way to explore and understand the biases in the data. This requires being transparent and aware of one's own biases, as well as using techniques such as fairness analysis, bias testing, and algorithmic auditing to identify and mitigate the impact of biases in the AI system.

Ultimately, preventing bias in AI is a complex and ongoing process that requires a combination of technical solutions and human oversight. It is important to continuously monitor and evaluate the AI system to ensure that it is fair and unbiased.
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