Achieving Fairness in ML Model with Fairlearn Package

Correct Answer: B.

Option A is incorrect because algorithms of the Fairlearn package (or any other existing algorithms) are not capable of mitigating unfairness issues on their own, without human considerations and fine-tuning.

Option C is CORRECT because mitigating fairness always requires a certain level of human consideration and intervention.

Machine algorithms can be very effective in the quantitative part, but they cannot be used to eliminate disparities totally because fairness is a complex phenomenon which always needs qualitative assessment performed by human actors.

Reference:

The answer is B. No, a properly configured Fairlearn algorithm cannot automatically eliminate any biases and disparities in your ML model on its own.

Fairlearn is a Python package that helps to assess and improve fairness in ML models. It provides algorithms that enable the measurement of bias and unfairness and allows for the development of models that mitigate these issues.

However, to effectively use Fairlearn, you must have a clear understanding of the sources of bias and unfairness in your data and model. Fairlearn provides tools to measure and visualize different types of bias, such as group fairness, individual fairness, and counterfactual fairness. Still, it does not automatically detect and correct them on its own.

Therefore, it is essential to review the Fairlearn results carefully and make adjustments to your model manually based on your domain expertise and ethical considerations. Fairness in ML is a complex and multifaceted issue that requires careful consideration of the context and the stakeholders involved.

In summary, Fairlearn is a valuable tool in promoting fairness in ML models, but it cannot eliminate biases and disparities automatically without human intervention. It requires thoughtful consideration and ethical decision-making to ensure that the resulting models are fair and ethical.