
## Fairness via Group Contribution Matching
Tianlin Li, Zhiming Li, Anran Li, Mengnan Du, Aishan Liu, Qing Guo, Guozhu Meng, Yang Liu
Keywords: AI Ethics, Trust, Fairness: ETF: Trustworthy AI, AI Ethics, Trust, Fairness: ETF: Fairness and diversity
IJCAI/2023/Proceedings/0049 - Fairness via Group Contribution Matching.pdf

### 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?_

[10]

The authors do not provide their implementation nor are any practical details given in the implementation details paragraph of 4.1.

### 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?_

[3]

(3/3)

The authors state they use three open access datasets in section 4.1, provide citations on them with descriptions of the task. Statistics and direct links are missing. 

### 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?_

[5]

The authors state some of the hyperparameter values in 4.1. It is difficult to verify if all needed parameters are given. No acquisition strategy is specified.

### 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 state the metrics used with an explanation in 4.1. In figure 4 tehy state they present a 10 fold repition of the training procedure's averages. 

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

[3]

Requires experience in fairness to understand the experiment.
