Your task now is to draft a high-quality review for a top-tier Machine Learning (ML) conference for a submission:\n\nTitle:\n```\nHuman-Guided Fair Classification for Natural Language Processing\n```\n\nKeywords:\n```\nIndividual Fairness,Style Transfer,NLP,Crowdsourcing,Human Evaluation\n```\n\nAbstract:\n```\nText classifiers have promising applications in high-stake tasks such as resume screening and content moderation. These classifiers must be fair and avoid discriminatory decisions by being invariant to perturbations of sensitive attributes such as gender or ethnicity. However, there is a gap between human intuition about these perturbations and the formal similarity specifications capturing them. While existing research has started to address this gap, current methods are based on hardcoded word replacements, resulting in specifications with limited expressivity or ones that fail to fully align with human intuition (e.g., in cases of asymmetric counterfactuals). This work proposes novel methods for bridging this gap by discovering expressive and intuitive individual fairness specifications. We show how to leverage unsupervised style transfer and GPT-3's zero-shot capabilities to automatically generate expressive candidate pairs of semantically similar sentences that differ along sensitive attributes. We then validate the generated pairs via an extensive crowdsourcing study, which confirms that a lot of these pairs align with human intuition about fairness in the context of toxicity classification. Finally, we show how limited amounts of human feedback can be leveraged to learn a similarity specification that can be used to train downstream fairness-aware models. \n```\n\nFigures/Tables Captions:\n```\nFigure 1: Workflow overview. We begin by generating sentence pairs using word replacement, and then add pairs of sentences leveraging style transfer and GPT-3. Then, we use active learning and crowdworker judgments to identify pairs that deserve similar treatment according to human intuition.\nTable 1: Balanced accuracy and individual fairness evaluated on different C i (proportion of pairs (s, s ) \u2208 C i for which f (s) = f (s )) for a RoBERTa-based toxicity classifier f trained with CLP using different constraint sets C i in each row. Results are averaged over 5 runs and \u00b1 indicates the difference from the bounds of a naive 95% confidence interval assuming normally distributed errors.\nTable 2: Crowdsourced answers to questions about comment pairs (s, s ) for different methods for demographic group transfer: do the two comments deserve similar treatment, was the demographic group successfully transferred, and was content preserved apart from that? The first number represents the percentage of the answer across all queries, while the second number (in brackets) represents the percentage of comment pairs for which the answer was the majority vote across 9 queries.\nTable 3: Performance (in terms of accuracy, true negative/positive rate and balanced accuracy) for similarity classifiers\u03c6 trained on human fairness judgments with and without relabeling, evaluated on the test set T with different decision thresholds t. Results are averaged over 10 repetitions of training on the data labeled in the last step/the relabeled data. \u00b1 indicates the difference from the upper/lower bound of a naive 95% confidence interval assuming normally distributed errors.\nTable 4: Balanced accuracy and individual fairness (proportion of human-validated similar pairs (s, s ) \u2208 T with \u03c6(s, s ) = 0, for which f (s) = f (s )) for toxicity classifiers f trained with CLP and different sets of similar pairs. Results are averaged over 5 training runs. \u00b1 indicates the difference from the upper/lower bound of a naive 95% confidence interval assuming normally distributed errors.\nTable A.1: Human evaluation: Answers to questions about comment pairs (s, s ) grouped by different methods for demographic group transfer. The first number represents the fraction of the answer across all queries, while the second number (in brackets) represents the fraction of comment pairs for which the answer was the majority vote across 9 queries.\nTable A.2: Human evaluation: Answers to questions about comment pairs (s, s ) grouped along demographic group transfers along different axes. The first number represents the fraction of the answer across all queries, while the second number (in the brackets) represents the fraction of comment pairs for which the answer was the majority vote across 9 queries.\nTable B.1: Balanced accuracies of the group-presence classifier c for different labels\nTable B.2: Amount of generated pairs (s, s ) per generation method.\nTable C.1: Different architectures trained for one epoch on 5000 samples from a set of pairs (s, s ) generated using word replacement to distinguish demograpghic group transfer within the same category of gender and sexuality, race and religion vs across categories (\u03d5 2 ). \"Featurediff\" uses a linear model applied to the difference of model features produced for the respective first tokens in s and s . \"Bilinear\" uses a bilinear model on top of these feature differences instead. \"Merge\" appends s to s before tokenization and learns a linear model on top of the model features for this combined input. \"Concat\" operates similarly, but first tokenizes s and s and pads both to 64 tokens before feeding the concatenated tokens into the model. No dropout was used in the post-BERT layers for these experiments. All results averaged over 10 runs and \u00b1 indicates the difference from the upper/lower bound of a naive 95% confidence interval assuming normally distributed errors.\nTable C.2: Balanced accuracy for BERT classifier trained using a constant amount of 50k gradient steps and a constant amount of 10k queries. All results are averaged over 10 runs and \u00b1 indicates the difference from the upper/lower bound of a naive 95% confidence interval assuming normally distributed errors.\nTable C.3: Results for active learning to predict human fairness judgments, on test data T . Active learning classifiers are retrained 10 times on the last batch D 6 . Results are averaged and \u00b1 indicates the difference from the upper/lower bound of a naive 95% confidence interval assuming normally distributed errors.\nTable C.4: Results for active learning to predict human fairness judgments, using the separate test data S. Active learning classifiers are retrained 10 times on the last batch D 6 . Results are averaged and \u00b1 indicates the difference from the upper/lower bound of a naive 95% confidence interval assuming normally distributed errors.\nTable D.1: Balanced accuracy and individual fairness (proportion of similar pairs (s, s ) \u2208 C i for which f (s) = f (s )) for a Roberta-based classifier f trained with CLP using different constraint sets for training. Results reported with \u00b1 are averaged over 5 runs and \u00b1 indicates the difference from the upper/lower bound of a naive 95% confidence interval assuming normally distributed errors.\nTable D.2: Balanced accuracy and individual fairness (proportion of similar pairs (s, s ) according to \u03d5 1 for which f (s) = f (s )) CLP training after filtering C e using approximations of \u03d5 1 with varying error profiles. All results are averaged over 5 runs and \u00b1 indicates the difference from the upper/lower bound of a naive 95% confidence interval assuming normally distributed errors.\nTable D.3: Balanced accuracy and indvidual fairness (proportion of similar pairs (s, s ) according to \u03d5 2 for which f (s) = f (s )) for CLP training after filtering C e using approximations of \u03d5 2 with varying error profiles. All results are averaged over 5 runs and \u00b1 indicates the difference from the upper/lower bound of a naive 95% confidence interval assuming normally distributed errors.\nTable D.4: Balanced accuracy, percent of unfiltered pairs, and individual fairness on two different test sets for differently trained classifiers f . Fairness is measured as the fraction of pairs (s, s ) with \u03d5(s, s ) = 0 according to the majority of annotators, for which f (s) = f (s ). Active learning classifiers are retrained 5 times on the last batch D 6 and results are averaged over 5 training runs. \u00b1 indicates the difference from the upper/lower bound of a naive 95% confidence interval assuming normally distributed errors.\nTable E.1: Lists of words used for word replacementm, based on Smith et al. (2022)\nTable E.2: Examples of pairs produced by our style transfer approach\nTable E.3: Examples of pairs produced by GPT-3 (zero-shot)\nTable E.4: Examples of pairs produced by GPT-3 (edit mode)\nTable E.5: Examples of pairs produced by different methods for the same source comment and target demographic attribute.\nTable F.1: Mean and Maximum of absolute pairwise TPR and TNR gaps (Absolute differences between TPR, in percent) between comments mentioning different demographic groups for differently trained classifiers f .\n```\n\nMain Content:\n```\n\nSection Title: INTRODUCTION\n  INTRODUCTION With the rise of pretrained large language models ( Sun et al., 2019 ), text classifiers can now be employed in tasks related to automated hiring ( Bhatia et al., 2019 ), content moderation ( Rieder & Skop, 2021 ) and social science research ( Widmer et al., 2020 ). They are also part of machine learning pipelines for unsupervised style transfer (Reid & Zhong, 2021) or reducing the toxicity of language model outputs ( Welbl et al., 2021 ). However, text classifiers have been shown to often exhibit bias based on sensitive attributes such as gender ( De-Arteaga et al., 2019 ) or demographics ( Garg et al., 2019 ), even for tasks in which these dimensions should be irrelevant. This can lead to unfair and discriminatory decisions, distort analyses based on these classifiers, or propagate undesirable demo- graphic stereotypes to downstream applications. The intuition that certain demographic indicators should not influence decisions can be formalized in terms of the concept of individual fairness ( Dwork et al., 2012 ), which posits that similar inputs should be treated similarly by machine learning systems. While in a classification setting similar treatment for two inputs can naturally be defined in terms of both inputs being labeled the same, the notion of input similarity should capture the intuition that certain input characteristics should not influence model decisions. Key challenge: generating valid, intuitive and diverse fairness constraints A key challenge when applying the individual fairness framework is defining the similarity notion \u03c6. Indeed, the definition is often contentious, as fairness is a subjective concept: what counts as a valid demographic indicator, as opposed to a problematic stereotype? Counterfactual definitions of similarity ( Kusner et al., 2017 ) offer a principled solution, but they shift the burden towards the underlying causal model, whose definition can often be similarly contentious. While many other definitions have been proposed, it is widely recognized that the similarity of inputs can often be highly task dependent ( Dwork et al., 2012 ;  Barocas et al., 2019 ), e.g., two biographies that are identical except for indicators of gender may be considered similar in a professional context, but not in the context of online dating. In the context of text classification, most existing works have cast similarity in terms of word replacement ( Dixon et al., 2018 ;  Garg et al., 2019 ;  Yurochkin & Sun, 2021 ;  Liang et al., 2020 ). Given a sentence s, a similar sentence s is generated by replacing each word in s, that belongs to a list of words A j indicative of a demographic group j, by a word from list A j , indicative of another demographic group j = j. This approach has several limitations: (i) it relies on having exhaustively curated word lists A j of sensitive terms, (ii) perturbations that cannot be represented by replacing single sensitive terms are not covered, and (iii) many terms are only indicative of demographic groups in specific contexts, hence directly replacing them with other terms will not always result in a similar pair (s, s ) according to human intuition. Indeed, word replacement rules can often produce sentence pairs that only differ in an axis not relevant to fairness (e.g., by replacing \"white house\" with \"black house\"). In addition, they can generate so-called asymmetric counterfactuals ( Garg et al., 2019 ): sentence pairs (s, s ) that look similar but clearly do not warrant similar treatment. For example, in the context of toxicity classification, the text \"I don't like this movie. It is so old\" may not be considered toxic while \"I don't like this movie. It is so gay\" clearly is. This work: generating fairness specifications for text classification The central challenge we consider in this work is how to generate a diverse set of input pairs that aligns with human intuition about which inputs should be treated similarly in the context of a fixed text classification task. These pairs then induce fairness constraints that collectively define an implict fairness specification on a downstream classifier, as individual fairness postulates that they should be classified in the same way. We address this challenge via a three-stage pipeline, summarized in  Fig. 1 . First, we start from a training dataset D for the text classification task under consideration and generate a set C w of candidate pairs (s, s ) by applying word replacement to sentences s \u2208 D. Second, to improve diversity and expand on word replacement rules, we extend C w to a larger set of pairs C e by borrowing unsupervised style transfer ideas. We change markers of demographic groups, e.g., \"women\", \"black people\" or \"Christians\", in sentences s \u2208 D by replacing the style classifier used by modern unsupervised style transfer methods (Reid & Zhong, 2021;  Lee, 2020 ) with a classifier trained to identify mentions of demographic groups. In addition, we add pairs from GPT-3 ( Brown et al., 2020 ), prompted to change markers of demographic groups for sentences in D in a zero-shot fashion. Third, to identify which of the generated pairs align with human intuition about fairness in the context of the considered classification task, we design a crowdsourcing experiment in which workers are presented with candidate pairs and indicate if the pairs should be treated similarly for the considered task or not. Since obtaining human feedback is expensive, we label a small subset of the generated pool and train a BERT-based ( Devlin et al., 2019 ) classifier\u03c6 to recognize pairs that should be treated similarly, yielding a final set of filtered pairs\u0108 r \u2286 C e . To further reduce labeling costs, we use active learning similar to ( Grie\u00dfhaber et al., 2020 ) to decide which pairs to label. We also demonstrate that the final set of constraints\u0108 r can be used for training fairness-aware downstream classifiers, by adopting the Counterfactual Logit Pairing (CLP) regularizer of ( Garg et al., 2019 ). While our pipeline can in principle be used in the context of most text classification tasks, we instantiate it in the context of toxicity classification. Our experimental results, based on a large dataset for online content moderation, show that in this context our pipeline effectively generates a set of candidate pairs that covers more diverse perturbations than existing word replacement based approaches and successfully leverages human feedback to verify and filter these candidate pairs.\n\nSection Title: Main contributions\n  Main contributions We make the following contributions: \u2022 We introduce a method for generating datasets of diverse candidate pairs for individual fairness specifications. Towards that, we leverage GPT-3 and unsupervised style transfer to modify demographic attributes mentioned in sentences. \u2022 We show that human feedback can be used for training a classifier that automatically identifies pairs that align with human fairness intuitions for a considered downstream task. \u2022 We instantiate our framework in the context of toxicity classification. We experimentally show that the proposed pairs cover more diverse perturbations than word replacement, that crowdworkers agree with more than 75% of proposed pairs and that our learned approximate specification can effectively be used to train fairness-aware downstream classifiers.\n\nSection Title: RELATED WORK\n  RELATED WORK Bias in NLP Early work on bias in Natural Language Processing has focused on unwanted correla- tions between the word embeddings of identifiers for protected demographic groups and unrelated categories such as occupations ( Bolukbasi et al., 2016 ;  Caliskan et al., 2017 ). More recently, genera- tive language models have been found to harbor stereotypical biases ( Liang et al., 2020 ;  Nadeem et al., 2021 ;  Vig et al., 2020 ;  Smith et al., 2022 ). Specific to text classification, identity terms such as \"gay\" and explicit indicators of gender have been shown to significantly impact the outputs of classifiers trained to identify toxic comments ( Dixon et al., 2018 ) or to predict a person's occupation from their biography ( De-Arteaga et al., 2019 ).  Olteanu et al. (2017)  demonstrate that human perceptions of the quality of a toxicity classifier can depend on the precise nature of errors made by the classifier, as well as the annotators' previous experiences with hate speech. Similarly,  Blodgett et al. (2020)  recommend authors to explictly consider why, how and to whom the biases they identify are harmful. Language models for data augmentation  Perez et al. (2022)  use a language model to automat- ically generate test cases for another language model and  Ross et al. (2022)  automatically create contrast sets ( Gardner et al., 2020 ) with a language model perturbing sentences based on control codes.  Rios (2020)  use style transfer to change the dialect of African-American Vernacular English tweets to Standard American English in order to evaluate the sensitivity to dialect of offensive language detectors, but do not extend style transfer to mentions of demographic groups. Hartvigsen et al. (2022) use language models to generate a balanced dataset of benign and toxic comments about minority groups and demonstrate that finetuning a toxicity classifier on this dataset can substantially limit its reliance on spurious correlations between identity terms and toxicity. However, their dataset is non-parallel, hence it cannot be used for evaluating individual fairness. Meanwhile,  Qian et al. (2022)  train a perturber model to imitate human rewrites s of comments s that aim to modify mentions of demographic groups, and demonstrate that finetuning language models on the modified comments reduces demographic biases. Although this approach creates parallel data, it is limited by its reliance on large amounts of expensive human rewrites, which is likely why the authors only use it for perturbations along given demographic axes such as gender. In contrast, we allow for perturbations across axes and only require human annotations rather than rewrites. Learning fairness notions from data  Ilvento (2020)  provides an algorithm to approximate arbi- trary individual fairness metrics for N datapoints in O(N log N ) queries, which can be practically infeasible. Meanwhile,  Mukherjee et al. (2020)  suggest training a classifier to predict binary fairness judgments on pairs (s, s ) in order to learn a fairness metric \u03c6, but restrict themselves to Mahalanobis distances on top of a feature representation \u03be(s), limiting their expressive power. In contrast to our work, these works do not validate their learned fairness notions with human feedback. To that end,  Cheng et al. (2021)  present an interface to holistically elicit stakeholders' fairness judgments, whereas  Wang et al. (2019)  aim to learn a bilinear fairness metric for tabular data based on clustering human annotations. Another strain of work aims to directly learn fair classifiers without an explicit fairness metric: given access to similarity queries,  Jung et al. (2021)  propose an algorithm with gener- alization bounds for fairness and accuracy that requires polynomially many queries to cost-sensitive classification oracle, while other work ( Gillen et al., 2018 ;  Bechavod et al., 2020 ) focuses on online learning of individually fair models. Lastly,  Lahoti et al. (2019)  use examples of similar pairs (s, s ) to directly learn a representation that aims to ensure geometric similarity for similar pairs while preserving nearest neighbors in the input space. This approach is difficult to use for non-tabular data, in which nearest neighbor relations do not necessarily carry semantic meaning. In contrast to these works, we are not only interested in training fair classifiers, but also aim to learn the similarity function which approximates human intuitions about fairness for the task. Enforcing fairness constraints  Garg et al. (2019)  suggest enforcing fairness constraints via cen- soring terms indicative of demographic groups, and by extending logit pairing ( Kannan et al., 2018 ) to counterfactual logit pairing (CLP): during training, a classifier f with logits l is regularized by the term \u03bb||l(s) \u2212 l(s )|| 2 for similar datapoints s and s .  Yurochkin et al. (2019)  and  Yurochkin & Sun (2021)  use distributionally robust optimization and transport-based regularization respectively to train a toxicity classifier with distributional fairness guarantees for bilinear fairness metric similar to the ones from ( Mukherjee et al., 2020 ). ( Ruoss et al., 2020 ;  Yeom & Fredrikson, 2021 ;  Peychev et al., 2022 ) not only enforce, but also certify the adherence to individual fairness constraints expressed in logical formulas, weighted L p metrics or similarity sets defined in the latent space of a generative model. However, except for CLP and censoring, all of these methods require a known similarity metric with a specific functional form, which is not always available in practice.\n\nSection Title: METHOD\n  METHOD This section presents our end-to-end framework for generating candidate pairs for individual fairness specifications for a given text classification task, identifying candidates that indeed represent fairness constraints for that task and using them for training individually fair downstream classifiers. In Sec. 3.1 we expand on existing word replacement definitions of individual fairness in text classification by generating further candidate constraints. Next, in Sec. 3.2 we leverage human feedback to learn an approximate similarity function\u03c6 to identify a set of relevant constraints\u0108 r \u2286 C e . Finally, in Sec. 3.3 we train a fairness-aware classifier f using CLP on the filtered constraint set\u0108 r .\n\nSection Title: EXPANDING FAIRNESS CONSTRAINTS\n  EXPANDING FAIRNESS CONSTRAINTS We expand the word replacement based constraint set from ( Garg et al., 2019 ) by implementing three different ways to modify markers of demographic groups mentioned in a sentence s: an extended word replacement list, unsupervised style transfer, and zero-shot modification using GPT-3. Word Replacement First, we enrich the word replacement method by using the extensive lists of words associated with different protected demographic groups presented in ( Smith et al., 2022 ). The pool of terms is substantially larger than the 50 identity terms from ( Garg et al., 2019 ). We modify markers of group j in a comment s by replacing all words on the respective list of words associated with group j with words from the list associated with the target group j .\n\nSection Title: Unsupervised Style Transfer\n  Unsupervised Style Transfer Second, we use an unsupervised style transfer approach based on prototype editing (see ( Jin et al., 2022 ) for an extensive review on style transfer) to transform markers of a demographic group j in a sentence s to markers of another demographic group j , creating a new sentence s . Prototype editing identifies markers a of a source style A in a text s, and substitutes them by markers a of a target style A . It can achieve unsupervised style transfer with minimal modifications to a source sentence s. Importantly, modern prototype editing algorithms rely solely on a style classifier to define their notion of style, so that they can transfer mentions of demographic groups when used with a classifier trained to identify such mentions. Our approach consists of three phases. First, we train a multi-headed RoBERTa-based ( Liu et al., 2019 ) classifier c to predict the presence of mentions of demographic groups j in a sentence s. Second, following (Reid & Zhong, 2021), we train a BART-based ( Lewis et al., 2020 ) group-conditioned generator g(s t , j): given a sentence s consisting of n tokens that mentions group j, we remove mentions of demographic groups from s by masking tokens at positions k with above-average attention weights a k \u2265\u0101, where a k represents the maximum attention weight at position k in the penultimate layer of c and the average is taken over all token positions for the sentence s. After merging consecutive masks, this yields a template s t on which g(s t , j) is trained to reconstruct s. Third, we modify sentences s that mention group j to instead mention group j by first creating a template s t as in ( Lee, 2020 ): we iteratively mask the tokens in s for which masking most reduces the likelihood p c (j|s t ) of j according to the group-presence classifier c, until it falls below a fixed threshold T . Then, we generate s as g(s t , j ) using beam search with width 5 and selecting according to p c (j |s ) \u2212 p c (j|s ), the difference in likelihoods assigned to j and j for s by c. We use this approach rather than the attention-based masking from (Reid & Zhong, 2021) for the third step because the attention values a k are shared between the prediction heads of c for all groups j. This means that attention-based masking might mask tokens related to a third group j instead of tokens related to j for sentences s in which multiple demographic groups are mentioned. While unlikely to be very detrimental during the training of the class-conditioned generator g, using attention for creating templates s t for g can thus cause group transfer to target the wrong source group j. The unsupervised style transfer approach promises multiple advantages. First, style transfer is likely to reproduce terms encountered during training, helping it to pick up on rare demographic terms that are particular to its training distribution which can be chosen to equal the training distribution for downstream tasks. In addition, unlike concurrent work by  Qian et al. (2022) , unsupervised style transfer only requires labels y j (s) indicating the mention of demographic group j in a sentence s rather than large amount of expensive human-produced examples of demographic group transfer. This allows us to modify mentions of demographic groups across axes like gender, religion and race, rather than restricting ourselves to changes within each of these axes. GPT-3 Lastly, we make use of GPT-3 ( Brown et al., 2020 ) to transform markers of protected demographic groups in a zero-shot fashion. We use three methods for generating pairs based on GPT-3. First, we prepend an example s mentioning group j with the prompt \"Please rewrite the following sentence to be about j rather than j\". Second, we use GPT-3's edit mode 1 with a similar prompt. Lastly, we generate candidate modifications s by word replacement, and postprocess them using GPT-3's edit mode with the prompt \"Fix grammatical errors and logical inconsistencies\". While the GPT-3 approach does not automatically adapt to the relevant distribution of demographic terms, it does not require any additional data, or training of language models. To ensure that mentions of demographic group j were indeed replaced by j going from s to s , we use the same group- presence classifier c as for the unsupervised style transfer approach to heuristically identify successful group transfer and discard pairs (s, s ) for which group transfer failed, for all three of our approaches. Implementation details are described in App. B and App. E contains examples of generated sentences.\n\nSection Title: LEARNING THE SIMILARITY FUNCTION\n  LEARNING THE SIMILARITY FUNCTION In order to evaluate to what extent the proposed similarity criteria align with human intuition, we leverage human feedback, via a crowdsourcing study described in more detail in Sec. 4, to obtain labels \u03d5(s, s ) which indicate whether the pair (s, s ) should be treated similarly for the sake of individual fairness (\u03d5(s, s ) = 0) or not (\u03d5(s, s ) = 1). In particular, identifying which pairs align with human labelers' intuition about fairness can help detect asymmetric counterfactuals, as well as failed attempts at style transfer for which s cannot be interpreted as a meaningful modification of s. Since labeling all of C e can be prohibitively expensive, we train a probabilistic model p\u03c6(s, s ) on a labeled subset of C e and use it to predict \u03d5(s, s ) for the remaining pairs (s, s ). The similarity function \u03d5 that specifies individual fairness is then approximated as\u03c6(s, s ) := 1 \u21d4 p\u03c6(s, s ) > t for a given classification threshold t. Instead of using bilinear logits based on features for both s and s ( Mukherjee et al., 2020 ), we tokenize s and s and train a BERT-based classifier on the concatenated tokens. This allows for a more holistic comparison between s and s as attention heads can directly attend to differences between s and s in earlier layers (see App. C for more details).\n\nSection Title: Active Learning from human fairness judgments\n  Active Learning from human fairness judgments To make optimal use of costly human queries, we employ active learning when training the classifier\u03c6. We use the variation ratios 1 \u2212 max y p(y|x) to select the data points with the largest uncertainty about the correct label, an approach that is often dubbed Least Confidence (LC) and estimate p using a Dropout-based Monte-Carlo estimate ( Gal & Ghahramani, 2016 ;  Gal et al., 2017 ). As in ( Grie\u00dfhaber et al., 2020 ), we aim to save resources by precomputing features for the BERT-part of\u03c6 and performing Monte-Carlo dropout on the classification head of\u03c6 only. Concretely, after training\u03c6 on an initial randomly selected dataset D 0 \u2282 C e with labels \u03d5(s, s ), we iteratively select new unlabeled training data D i \u2282 C e \\ j<i D j with |D i | = 1000, based on the variation ratios, query labels for D i , and retrain\u03c6 on D i . As different annotators can disagree about whether or not two sentences s and s should be treated similarly, we use a majority vote for evaluation. Inspired by  Chen et al. (2022) 's approach for dealing with noise in crowdsourcing, we use a single human query per pair (s, s ) during active learning, and relabel pairs that are especially likely to be mislabeled after active learning has concluded.\n\nSection Title: TRAINING A FAIR(ER) CLASSIFIER\n  TRAINING A FAIR(ER) CLASSIFIER Finally, we train a fairness-aware classifier by accounting for the constraints defined by the learned sim- ilarity function. Specifically, we define the filtered constraint set\u0108 r = {(s, s\n```\n\n======\nYour task:\nCompose a high-quality peer review of an ML paper submitted to a top-tier ML conference on OpenReview.\n\nThe review form is shown below:\n\n1. Summary and contributions: Briefly summarize the paper and its contributions\n\n2. Strengths: Describe the strengths of the work. Typical criteria include: soundness of the claims (theoretical grounding, empirical evaluation), significance and novelty of the contribution, and relevance to the community.\n\n3. Weaknesses: Explain the limitations of this work along the same axes as above.\n\n4. Correctness: Are the claims and method correct? Is the empirical methodology correct?\n\n5. Clarity: Is the paper well written?\n\n6. Relation to prior work: Is it clearly discussed how this work differs from previous contributions?\n\n7. Reproducibility: Are there enough details to reproduce the major results of this work?\n\n8. Additional feedback, comments, suggestions for improvement and questions for the authors\n\n9.  Overall score:\nYou should NOT assume that you were assigned a representative sample of submissions, nor should you adjust your scores to match the overall conference acceptance rates. The \u201cOverall Score\u201d for each submission should reflect your assessment of the submission\u2019s contributions.\n10): Top 5% of accepted papers. Truly groundbreaking work.\n9): Top 15% of accepted NeurIPS papers. An excellent submission; a strong accept.\n8): Top 50% of accepted NeurIPS papers. A very good submission; a clear accept.\n7): A good submission; accept.nI vote for accepting this submission, although I would not be upset if it were rejected.\n6): Marginally above the acceptance threshold. I tend to vote for accepting this submission, but rejecting it would not be that bad.\n5): Marginally below the acceptance threshold. I tend to vote for rejecting this submission, but accepting it would not be that bad.\n4): An okay submission, but not good enough; a reject. I vote for rejecting this submission, although I would not be upset if it were accepted.\n3): A clear reject. I vote and argue for rejecting this submission.\n2): I'm surprised this work was submitted to NeurIPS; a strong reject.\n1): Trivial or wrong or already known.\n\n10.  Confidence score:\n5): You are absolutely certain about your assessment. You are very familiar with the related work.\n4): You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work.\n3): You are fairly confident in your assessment.  It is possible that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work. Math/other details were not carefully checked.\n2): You are willing to defend your assessment, but it is quite likely that you did not understand central parts of the submission or that you are unfamiliar with some pieces of related work. Math/other details were not carefully checked.\n1): Your assessment is an educated guess. The submission is not in your area or the submission was difficult to understand. Math/other details were not carefully checked.\n\n11. Have the authors adequately addressed the broader impact of their work, including potential negative ethical and societal implications of their work?\n\n12. Does the submission raise potential ethical concerns? This includes methods, applications, or data that create or reinforce unfair bias or that have a primary purpose of harm or injury. If so, please explain briefly.\n\n