Venue: ICLR 2023 Conference

Reviews:

final_score: rating: 5: marginally below the acceptance threshold
review_content: summary_of_the_paper: This paper unravelled the complex effects that dataset imbalance can have on the model bias for face identification systems. 

This paper separately considered imbalance in terms of identities and images per identity in both the train set and the test set.

This paper thoroughly explored the effects of each kind of imbalance possible in face identification, and discuss factors which may impact bias.
strength_and_weaknesses: Strength:

Bias in face recognition is a timely topic.

This paper provided comprehensive statistics regarding bias in the training set and test set.

Weakness:

In Section 7.1, 'We observe that both models have higher male performance when the test set is perfectly balanced'. This observation is not explored in depth. Is it because female images usually contain make-up?

In Fig 6, there is no black line in the figure. The legends (identity, image)  are confusing. It is better to show male identity, male image and so on.

There is no reference to MobileFaceNet, CosFace and ArcFace.

The technical contribution of this paper is limited as only sampling strategy is used with some simple statistics on the results.

There are some useful conclusions in Section 8, but it is not very insightful for training unbiased face recognition models
clarity,_quality,_novelty_and_reproducibility: The writing is clear and straightforward. But the technical contribution of this paper is limited. This paper is easy to reproduce the statistics reported in the paper.
summary_of_the_review: Bias in face recognition is a timely topic. This paper employed data sampling techniques on different dimensions, such as training data, test data, identity and image. Extensive results supported some conclusions on the bias problem of face recognition. However, the technical contribution of this paper is limited and some observations are not in-depth.
review_title: null

final_score: rating: 3: reject, not good enough
review_content: summary_of_the_paper: The paper can be categorized into an "analysis type" paper, which doesn't propose anything new; instead, it demonstrates that standard image classification modeles, such as RestNet, MobileNet etc., can lead to biased predictions on face recognition tasks and datasets.

strength_and_weaknesses: Strengths:

1. Fair number of experiments done on a standard dataset.
2. Multiple models investigated.
3. Extensive analysis conducted

Weaknesses:
1. Limited novelty in the sense that this is purely an analysis paper, and that too this type of analysis has been done before if not on the same dataset.
2. Paper not well-written, e.g., even the task isn't clearly described (see the summary).
3. No insights is provided into what exactly is the (likely) reason for the observations.
4. No attempt to alleviate the bias is made.


clarity,_quality,_novelty_and_reproducibility: The novelty is limited (see the summary).

Clarity is not good in terms of precisely defining the task and the protected attributes. 
summary_of_the_review: The paper can be categorized into an "analysis type" paper, which doesn't propose anything new; instead, it demonstrates that standard image classification modeles, such as RestNet, MobileNet etc., can lead to biased predictions on face recognition tasks and datasets.

The paper is poorly written. It's not even clear after reading Section 3 (Face Identification Setup) that what the task actually is. Is the model trained to predict a particular class (ot of say p ones), i.e., $\theta: \vec{x} \mapsto \mathbb{Z}_p$ or is the task that of metric learning, i.e., $\theta: \vec{x} \times \vec{z} \mapsto \mathbb{R}$?

In addition to gender, there're plenty of other attributes in the CelebA dataset that can be considered for stereotypical biases (checking for parity in the posteriors), e.g., "attractiveness" is one such feature, and there could be easily be biases discovered by a combination of more than one attribute, e.g., "females wearing glasses are less attractive" etc.

The paper reports the biases but doesn't propose a method to alleviate them, which in my opinion, severly limits its contributions in terms of novelty.

The paper lacks any kind of analysis. What's presented as actionable insights, such as "overrepresenting the target demographic group can sometimes hurt that group...", an academic paper should provide insights into why is that the case? What's the difference between the performance from different models? Is MobileNet more biased than ResNet? If so, why, if not then why not?
review_title: null

final_score: rating: 3: reject, not good enough
review_content: summary_of_the_paper: This paper aims to explore the problem of data set imbalance in face identification systems. They specifically focus on imbalance with respect to gender presentation. Aiming at the imbalances(both in terms of identities or images per identity) in the train set and the test set, the author conducted several experiments on the Celeb A dataset and got some conclusions.
strength_and_weaknesses: Strength:
1. The problem of data imbalance is a good direction and deserves further study.
2. The authors have carried out experiments on the problem of data imbalance in different situations and draw conclusions according to the experimental results.

Weaknesses:
1. The author carried out experiments (session 4 and session 5) respectively under the condition that the training set and the test set are not balanced, but the conclusion obtained is relatively shallow. At the same time, in the case of imbalanced training set images, the abnormal occurrence is caused by resampling, that is, the bias is partly caused by human factors and the result is not rigorous enough.
2. The authors conclude in session 7.1 that females are generally more difficult to identify than males, but the authors only conducted experiments on the Celeb A dataset.  There are differences in the difficulty of different data sets.  If the experiment can be carried out in more datasets, the conclusion will be more convincing.
3. In session 7.2, the author argues that there are reasons for bias in humans and machines are different. But the author does not analyze what reason is and why they are different. I think this conclusion is meaningless.
clarity,_quality,_novelty_and_reproducibility: Clarity: This paper is clear and easy to understand
Quality: This paper is of average quality and has some loopholes
Novelty: The direction of this paper is innovative
Reproducibility: This paper is reproducible
summary_of_the_review: The authors have carried out various experiments, but the conclusion is relatively simple. The authors reveal many common errors in bias calculation in face recognition tasks, but do not provide effective strategies to avoid these problems
review_title: null

Meta-review: metareview:_summary,_strengths_and_weaknesses: The paper studies face identification and demonstrates how various types of data imbalance can lead to model bias. AC agrees with the reviewers that albeit it’s an important problem, limited technical contribution (how to resolve existing model failures) and a narrow application domain (the paper studies face identification) are two critical issues that place the contributions below acceptance bar. We hope the reviews are useful to improve the manuscript.
Decision: Reject