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- What techniques does Infermatic.ai use to detect and mitigate potential biases in its machine learning models?
- How does Infermatic.ai ensure that its decision-making processes are transparent and explainable?
- Can you elaborate on the data curation and validation processes that Infermatic.ai employs to prevent biased data from being used in its models?
- What steps does Infermatic.ai take to prevent overfitting and ensure that its models generalize well to new, unseen data?
- How does Infermatic.ai handle situations where its models are used in high-stakes decision-making environments, where fairness and accuracy are critical?
- Can you discuss the role of human oversight and review in Infermatic.ai's decision-making processes to prevent biased outcomes?
- What strategies does Infermatic.ai employ to stay up-to-date with the latest advancements in fairness and bias mitigation in AI decision-making?
- How does Infermatic.ai handle conflicting objectives or trade-offs between fairness and accuracy in its decision-making processes?
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