## Generalizing Group Fairness in Machine Learning via Utilities
Jack Blandin, Ian A. Kash
Keywords: 
JAIR/2023/Proceedings/14238 - Generalizing Group Fairness in Machine Learning via Utilities.pdf
Project URL: 

### Implementation
_Given the documentation given by the authors on the method, how much time investment would it be to re-implement the method from scratch?_

[1]

They provide a link to the repository https://github.com/jackblandin/group-fairness-in-machine-learning-via-utilities/. They have instructions on installation and execution. It seems like they did good function descriptions for core code. That is why I do not add anything for this point. 

### Data
_Given the data description in the documentation, how much effort take to either: Find the same dataset the authors used, or similar datasets and defend the comparability, or acquire one from scratch?_

[2]

(P/N)

I think they use two data sets and I think they are public (they do not provide an url, just a reference.) They provide specifications to the data set in the appendix, but no url. (+1) There are statistics. 

### Configuration 
_Given the (hyper)parameters, including semantic parameters, of the method: How much effort would it take to acquire the algorithm configurations used for their results, and compare against their budgetary constraints?_

[8]

I looked a few minutes for specifications concerning hyperparameters and I havent found any in the text and in the repo. (+3+4) "UFP parameters I, Z, M, and C can be inferred from the MDP as follows." (+0)

### Experimental Procedure
_Given the experimental set-up of the work, how difficult is it to set up a new experiment, similar to those presented in the original work, with the same procedure?_

[1]

The metrics they are using look quite complicated (or fairness-specific), but it seems like they explain them.
They explain the strategy.
I think it is clear how the results are aggregated. 

### Expertise
_How much effort would it take to acquire the expertise required to reproduce the work independently relying on the available documentation?_

[5]

I would set this to a 5. They explain a lot concerning fairness literature. However, you need to have some knowledge concerning reinforcement learning. Both the fairness definition and the reinforcement learning part need mathematical knowledge.
