What is the most interesting topic covered in your domain this quarter?

One of the most interesting topic covered in my domain so far are the different pre-processing, on-processing, and post-processing bias migitation techniques there are and how each techniques has its own uses and drawbacks.

Describe a potential investigation you would like to pursue for your Quarter 2 Project.

I would investigate whether restaurant names with ethnic associations receive disproportionately harsher inspection ratings than other names. This would involve gathering data on restaurant names, inspection scores, and any available details that could influence ratings (such as location, type of cuisine, etc.), analyzing patterns in inspection outcomes by name and potentially other intersecting factors like location or income levels.

What is a potential change you’d make to the approach taken in your current Quarter 1 Project?

In Quarter 1, we used medical expenditures as our primary focus. For Quarter 2, I would likely adjust the variables to better align with the fairness and bias themes relevant to restaurant inspections. This would include modifying the model to account for different types of bias and correcting any potential disparities based on those variables.

What other techniques would you be interested in using in your project?

I'm not entirely certain about specific techniques yet, but I’ll explore them in more depth as the topic becomes clearer. This exploration could include a deeper dive into fairness-aware methodologies and bias mitigation techniques relevant to restaurant inspections.