
## Anatomizing Bias in Facial Analysis
Richa Singh, Puspita Majumdar, Surbhi Mittal, Mayank Vatsa
Keywords: Face Analysis, Bias And Fairness, Face Recognition
AAAI/2022/Proceedings/21500 - Anatomizing Bias in Facial Analysis.pdf

[X] Theoretical

### 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]

The authors analyse and provide an anatomy on bias in other works regarding facial analysis. No empirical evaluation is done, so no implementation.

### 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?_

[1]

(0/0)

The authors do not apply an empirical evaluation on a data set, however they do analyse prorperties of data sets used by other methods. On these datasets they provide a full table of used details (table 1). Furthermore, they do a meta analysis of accuracy by various other works, of which the source data of the graph is not provided. However it is relatively simple and could be recollected, yet an appendix table with the value per method would be nice.

### 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]

Not applicable.

### 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]

No emperical evaluation is done in this work, data is directly collected on previous works and analysed.

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

[2]

Requires an understanding of biases in data and its impact on AI methods.
