You are building a machine learning model to predict the willingness to repay loans among the customers of a large bank.
The binary classification model delivers very good performance, but before deploying it to production, you want to make sure that the model is free of age, gender and other sensitive biases, using the Fairlearn unfairness mitigation package.
While evaluating the fairness of the model, you need to balance between...
Click on the arrows to vote for the correct answer
A. B. C. D.Correct Answer: C.
Option A is incorrect because reduction and post-processing are two types of unfairness mitigation algorithms used by the Fairlearn package you have to choose from.
Option B is incorrect because accuracy is one of the performance metrics of an ML model.
Balancing is not applicable between them.
Option C is CORRECT because unfairness mitigation algorithms always affect the performance of the model.
Depending on the “level of unfairness” you want to achieve, certain levels of trade-off between the performance (e.g.
accuracy) and the level of disparity achieved.
Option D is incorrect because “fairness” is the generic term for the un-biased model behavior while disparity is a metric of (un)fairness.
No balancing applies here.
Reference:
When evaluating the fairness of a machine learning model, it is important to balance between reducing bias and maintaining model performance. Fairness can be defined as the absence of unwanted bias or discrimination in the model's predictions.
The Fairlearn unfairness mitigation package provides tools to evaluate and mitigate bias in machine learning models. One common approach is to use metrics such as demographic parity, equalized odds, and equal opportunity to measure the fairness of the model.
When balancing between reducing bias and maintaining model performance, it is important to consider the trade-offs between these two objectives. For example, reducing bias may require the model to sacrifice some degree of accuracy, which could affect its overall performance. On the other hand, focusing too much on model performance may result in biased predictions.
Therefore, the correct answer to the question is C. Model's performance and disparity. It is important to evaluate both the model's performance, as well as the degree of disparity across different subgroups (e.g., age, gender) to ensure that the model is fair and unbiased. This can involve iteratively adjusting the model to reduce disparities while maintaining acceptable levels of performance. By considering both performance and disparity, we can ensure that the model is not only accurate but also fair and unbiased.