Venue: ICLR 2023 Conference

Reviews:

final_score: rating: 6: marginally above the acceptance threshold
review_content: summary_of_the_paper: This paper introduces a workflow/methodology to generate pairs of
similar sentences that differ only wrt target/protected populations
such as gender or race. The methodology inclues increasingly
sophisticated steps, such as word replacement, unsupervised style
transfer and generation using GPT3. Crowdsourcing and active learning
is used to filter the generated pairs. The methodology is demonstrated
in the context of toxic text prediction. Using the pair-wise sentences
that vary only along the protected groups, a RoBERTa model is trained
by enforcing the logits for the two pieces of text to be similar
(since the toxic text prediction should not depend on the protected
subgroup used in the sentence).
strength_and_weaknesses: Strengths:
- The methodlogy could be used in different contexts for generating pairs of similar senteces that differ only in the protected group
- Lots of experiments to support the methodology

Weakness:
- Some ad-hoc decisions (e.g., shortening the max sequence size) that are not well explained
- Lots of experimental results that are not well explained
clarity,_quality,_novelty_and_reproducibility: In general, I enjoyed reading the paper	and learning about the
proposed methodlogy. I had a hard time processing the multitude of
experimental results. In particular, it's not clear what the numbers
represent in each table. I would clearly describe in the caption of
each figure what the rows/columns represent and what metric is
used. In particular, the paper uses "fairness" without clearly
explaining how fairness is measured.

Some of	the design decisions are not well explained. In	particular, I
am curious about the maximum sequence size of 64 (which is quite
limiting). Is that to ensure that the style transfer and the GPT3
generation produce quality results?

Not clear what WR50 means.

In Table 1, all columns except for BA represent some measure of
fairness? If that's the case and if I understand the results WR (which
I'm guessing stands for word replacement) seems quite performant. How
do you justify the complexity of adding the style transfer and GPT3 on
top. What is the real advantage of the full C dataset?

The fairness metric used seemed a bit ad hoc. One of the more
standard metrics could be use (see Fairness Definitions Explained and
the following paper for an example of using equalized odds: Your
Fairness May Vary: Pretrained Language Model Fairness in Toxic Text
Classification - analysis of many LMs wrt fairness and model
size/training size/random seed (in the context of toxic text
prediction)).

Last but not least, I think "fairness specifications" is misleading
and quite an overblown term for what the paper is about; it made me
think of some theoretical formalism/specification. Similar pairs of
text across protected groups is a much more honest description of what
the paper generates.
summary_of_the_review: This paper introduces a methodology to generate pairs of similar
sentences that differ only wrt protected group. The pairs generated
can be used to train fairer classifiers by imposing similar logits for
similar sentences.
review_title: null

final_score: rating: 8: accept, good paper
review_content: summary_of_the_paper: The problem this paper makes progress in solving is that of generating data to drive individual fairness constraints on text classification models, with a case study of toxicity classification. The authors make use of a data generation pipeline, such that, starting with an initial dataset of text with mentions of demographic information. First, they create an initial set of counterfactual examples using word replacement, GPT-3 (with three variants of sub-pipelines), and an unsupervised style transfer model. For the last two, they also train a BERT model to detect group presence in a text, which is used as an initial filter for GPT-3 generations and used to train the style transfer model. They then train a "similarity" model to detect, given an original text and its generated counterfactual, if both should be treated the same. This similarity model is trained using active learning. They then use this similarity model to iteratively filter out the initial set of pairs to a final set, which is used to train the downstream classifier. 
Using toxicity classification, they show that classifiers trained on this method was able to improve on fairness, and showed that the generated pairs generally aligned with human annotations' intuition of fairness. They also did ablation studies on downstream model performance given each initial method of data creation, and the effect of active learning on the similarity model. 
strength_and_weaknesses: The paper addresses several important issues in evaluation text classification models. The issue with evaluation datasets is that there is no evaluation data for certain demographics, let alone parallel data for individual fairness evaluation. Secondly, word replacement methods (which is often used in prior literature) does not check if the parallel data is indeed valid. Especially for nuanced topics like hate speech or t toxicity classification, swapping words is not enough, since a stereotype about one group may not exist for another group (e.g. older people are nurses is not as much of a stereotype as women being nurses in the US). The paper is generally strong in terms of rigor of methodology, novelty of method, and in contribution. 

For weaknesses, the authors could be clearer on what fairness metric is being used for tables such as Table D.3. Additionally, an ablation study could be added in terms of how changing the similarity classification threshold affects performance of the similarity model (and the downstream performance of downstream classifiers)? Finally, I would have liked to see in the appendix table E1-4, the generations from the three methods on the same examples. 

There are also a few nits, for example, make sure to reference all variables in graphs (for example, D in Figure 1). 

As a note, the authors should reference this paper as well for automatic generation of evaluation data: https://arxiv.org/pdf/2202.03286.pdf. 

clarity,_quality,_novelty_and_reproducibility: This paper is fairly original, clear,, and is of high quality. 
summary_of_the_review: I think this is an important problem, and this work will help equip future researchers with another method to generate high-quality data, which can be used for both evaluation of text classification models or to train them.
review_title: null

final_score: rating: 8: accept, good paper
review_content: summary_of_the_paper: The paper proposes a new framework to satisfy individual fairness notion by utilizing style transfer along with zero-shot techniques. They also perform crowdsourcing experiments to verify how much aligned results those techniques generate results compared to human intuition. They also use these human signals as means to improve they system for filtering pairs as well as training downstream models. 
strength_and_weaknesses: **Strengths:**

1. The paper studies interesting and timely problem.
2. The paper is written clearly and is well-organized.
3. The approach is simple and easy to follow.

**Weaknesses:**

1. It seems like the approach would be really task dependent. I think it is good for authors to acknowledge this somehow and mention how the task can affect the outcomes.
2. Related to my above comment, authors only study toxicity problem. It would be good to study other tasks and compare the differences and provide more in depth discussion on what the differences are in terms of the filtering approach per task as well as human alignment results.
3. While the approach is effective in achieving more "fair" outcomes, the approach imposes loss on accuracy.
4. Given that the task of toxicity detection is a subjective task and in many cases culturally dependent, what mechanisms authors take into account in their human studies other than recruiting more than one worker to label a hit and taking the maximum. Please provide some discussion on this.

**Additional Minor Comment:**

I would suggest authors refer to fairness through awareness instead of individual fairness as really what authors do is to study groups in their tasks (e.g., Muslim vs Christian) but using the notion of fairness through awareness, so to avoid this minor confusion, I would suggest changing the naming.
clarity,_quality,_novelty_and_reproducibility: **Clarity**:

The paper is written clearly, is well organized, and the approach is simple and straightforward.

**Quality**:

The paper is of good quality, but it would be better if additional experiments are added to investigate more various tasks.

**Novelty**:

Although the problem of word replacement has been previously investigated, the addition of style transfer and zero-shot capabilities is an interesting touch. 

**Reproducibility**:

The Appendix has some info for reproducibility. 
summary_of_the_review: The paper studies an important problem and is written clearly; however, some additional experiments can benefit the paper. Including some discussions around my concerns raised in the weaknesses section.
review_title: null

final_score: rating: 8: accept, good paper
review_content: summary_of_the_paper: The paper presents a framework for generating a dataset of diverse candidate pairs for individual fairness specifications by a set of methods (e.g., an extended word replacement list, unsupervised style transfer, and zero-shot modification using GPT-3). To align with human fairness intuitions for a considered downstream task, the authors propose active learning approaches to improve the quality of the datasets. Empirical studies are conducted to show the proposed dataset is of high quality.
strength_and_weaknesses: Strengths:
1. The paper considers a novel approach to bridge the gap between human intuition about input perturbations for individual fairness and the formal similarity specifications capturing them.
2. The methods to diversify expressive candidate pairs and active learning for quality control of the dataset are technically sound.

Weaknesses (see more details in the section below):
1. The paper needs further improvement in clarity.
2. Beyond individual fairness, a discussion of how the proposed dataset with individual fairness would affect group fairness notions is needed.
3. The broader impact of how relevant stakeholders would use the generated workflow is unclear. 

clarity,_quality,_novelty_and_reproducibility: In this section, I will provide more details of my comments.
First of all, I think the paper is technically sound overall. The tasks and paper considered in the paper are a novel combination of well-known techniques. In particular, the paper bridge the gap between human intuition about input perturbations for individual fairness and the formal similarity specifications capturing them.

However, I think the paper could be further improved in terms of clarity and discussion of multiple related notions (e.g., group fairness) and the potential broader impacts.

While the paper is fairly readable, there is substantial room for improvement in clarity. The techniques involved in this paper span multiple domains, requiring the reader to have a good knowledge of algorithmic fairness, NLP, user study, etc. I have seen the authors put good effort into giving details in the main text and appendix. However, it is hard to understand the experiment sections without referring to the appendix in detail. For example, in Table 2, the authors do not provide any description of the "Metric/Method" column in the main text. Similar problems happen in Table 3. I also have a question about evaluating the diversity of generated pairs: the paper uses the fairness metric as the evaluation criterion for models trained on different constraint sets. The word choice of "diversity" sounds strange, given the evaluation criterion. A better word choice might be something like "coverage". 

Since the paper studies fairness. I am also interested in how the generated intuitive fairness specification affects group fairness notions such as demographic parity and equalized odds. Besides, since the fairness specification is intuitively generated by humans, how do you provide more documentary specifications on this to the relevant stakeholders, such as ML/NLP practitioners and model users, to trust the overall workflow?

Lastly, I have some questions regarding reproducibility: I only see the codes in supplementary materials. Will the dataset be released? If yes, how do you plan to release them in order to provide a better potential impact in the long run? 

summary_of_the_review: The paper is novel and technically sound but needs improvements on clarity, and discussion of broader impact, given that the paper studies fairness.
review_title: null

Meta-review: metareview:_summary,_strengths_and_weaknesses: This work introduces a novel methodology for generating data pairs of semantically similar sentences that differ along sensitive attributes, with a case study of toxicity classification. Reviewers all agreed that this work is technically solid and novel with comprehensive experiments; it focuses on a very important research question, and the writing is clear and easy to follow. Reviewers all recommend acceptance. I’d like to encourage the reviewers to incorporate reviewers’ suggestions to improve the revised version in terms of some clarity and broad impact/limitation discussion. 

justification_for_why_not_higher_score: The spotlight is recommended based on the reviewers' comments and ratings towards "novelty and significance". In addition, there are a few weaknesses pointed out by reviewers such as task dependence and lack of clarity towards certain design choices, and broad impact/limitation discussion. All of these led to a "spotlight" recommendation, rather than an "oral". 


justification_for_why_not_lower_score: Reviewers all agreed that this work focuses on a very important research question, and is technically solid with comprehensive experiments. All of these make it suitable for a spotlight representation, rather than a poster.
Decision: Accept: notable-top-25%