Reproducibility study of FairAC
Abstract: This work aims to reproduce the findings of the paper "Fair Attribute Completion on Graph
with Missing Attributes" written by Guo et al. (2023) by investigating the claims made in
the paper. This paper suggests that the results of the original paper are reproducible and
thus, the claims hold. However, the claim that FairAC is a generic framework for many
downstream tasks is very broad and could therefore only be partially tested. Moreover,
we show that FairAC is generalizable to various datasets and sensitive attributes and show
evidence that the improvement in group fairness of the FairAC framework does not come at
the expense of individual fairness. Lastly, the codebase of FairAC has been refactored and
is now easily applicable for various datasets and models.
Submission Length: Regular submission (no more than 12 pages of main content)
Previous TMLR Submission Url: https://openreview.net/forum?id=FHQBENZo9W
Changes Since Last Submission: We adjusted the typesetting to conform to the TMLR guidelines.
Assigned Action Editor: ~Mingming_Gong1
Submission Number: 2215
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