How to Answer AI Ethics in Data Science at IBM

In designing a project using IBM Watson, I would start by identifying potential biases in the training data, such as demographic imbalances that could skew predictions. I would incorporate techniques like re-sampling or weighted training to mitigate these biases. Next, I would create a fairness evaluation framework that includes metrics like disparate impact and equal opportunity to measure model performance across different demographic groups. Continuous monitoring would be essential; thus, I would implement a feedback loop for updating the model as new data becomes available. Lastly, engaging with stakeholders, including affected communities, would help ensure that our approach remains transparent and aligned with ethical best practices.