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Related Questions
- Can you provide an example of a prompt that was used to mitigate bias in a facial recognition model by removing demographics from the dataset.
- How was data curation used to detect and remove biased images and ensure a diverse set of images was used to retrain the model.
- What techniques were employed to ensure the model generalized well to different skin tones, ages, and expressions.
- How does debiasing the dataset affect the model's ability to recognize faces with atypical features such as wearing sunglasses or hats.
- Can you walk through the process of implementing adversarial training to reduce the model's bias and make it more robust.
- What are some other methods that can be used to mitigate bias in a facial recognition model beyond retraining the model on diverse data.
- In what ways can fairness in AI be evaluated, such as using metrics like precision, recall, and fairness scores.
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