Bias Detection and Mitigation Quiz

What you'll be asked

1. Which of the following is the most direct way to detect bias in a machine learning model's predictions?
2. When addressing bias in a dataset, what does 're-weighting' involve?
3. A model is showing high accuracy overall but consistently fails for a specific minority group. Which mitigation strategy is best suited to address this during the post-processing phase?
4. Why is 'blindness' (removing sensitive attributes like race or gender from the input) often ineffective at preventing bias in machine learning models?
5. In the context of the 'Fairness-Accuracy Trade-off,' which statement best describes the challenge faced by MLOps engineers?

Answers and explanations come after you submit.