Answer the following questions for each result obtained for COMPAS and FairFace datasets, separating your answers into two parts: COMPAS dataset and FairFace dataset.

In Part A $($COMPAS dataset$)$ of your project,

Explain the model$$s prediction to help stakeholders understand the factors and reasoning behind it$.$

Compare fairness metrics across groups within the two models to determine whether unfairness is a problem.

Assess if the models resulted in unfairly biased against certain groups.

Propose mitigation procedures to address a significant unfairness issue discovered.

Create an audit report that includes the following:

A one$-$page summary of the general model performance information.

From your summary, identify communities most needing public assistance for the COMPAS dataset.

In Part B $($FairFace dataset$)$ of your project,

Explain the model$$s prediction to help stakeholders understand the factors and reasoning behind it$.$

Compare fairness metrics across groups within each model to determine whether unfairness is a problem.

Assess if the model resulted in unfairly bias against certain groups.

Propose mitigation procedures to address a significant unfairness issue discovered.

Create an audit report that includes the following:

A one$-$page summary of the general model performance information.

From your summary, identify groups with the most image processing errors in FairFace dataset.