
## The Measure and Mismeasure of Fairness
Sam Corbett-Davies, Johann D. Gaebler, Hamed Nilforoshan, Ravi Shroff, Sharad Goel
Keywords: 
JMLR/2023/Proceedings/221511 - The Measure and Mismeasure of Fairness.pdf
Project URL: https://github.com/jgaeb/measure-mismeasure

### 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?_

[3]

The authors provide a link to the implementation on JMLR website and the acknowledgements (https://github.com/jgaeb/measure-mismeasure). In the readme they state installation instructions. Code needs more comments and structure is vague.

### 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]

(2/2)

The authors conduct a case study on a dataset in 5.3. where they provide a lengthy description and citations and a link to the dataset in footnote 28, which is actually a synthetic data set closely mirroring the real dataset which we will accept. Statistics are minor. In 3.2 a diabetes dataset is considered, same information.

### 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?_

[1]

No method parameters.

### 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?_

[2]

The authors anaylse a dataset and present the result in figure 6 and 7. Variation in fig 6 is not explained.

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

[5]

Requries expertise on fairness in ML. 
