Venue: NeurIPS 2022 Track Datasets and Benchmarks

Reviews:

final_score: rating: 5: Marginally below acceptance threshold
review_content: summary and contributions: I think this dataset may be adopted by the community as a new tabular fairness benchmark, which will be a very useful contribution. However, I have a major concern regarding potential implicit bias in the original dataset that is not discussed by the authors, which unfortunately means I can not recommend accepting the paper at this point. 
strengths: The authors create a new and interesting tabular dataset. The dataset is synthetically generated based on a real dataset to conserve privacy. 

I agree with the authors that new, well-documented tabular datasets are a very useful contribution to the wider research community in general and the algorithmic fairness community in particular. Although this dataset is domain-specific, I think it can be used by many researchers for evaluation purposes and has the potential to be a significant contribution. 

The authors do not share the real dataset but do describe the generative process in detail. The code for sampling from the model is available making the dataset more versatile for other users. 

The datasheet describing the dataset is comprehensive and easy to read.

weaknesses: I have one major concern regarding this paper. I did not find, in the paper or the datasheet, a description of how the fraud label is generated. I can imagine two ways:

1) The dataset includes both accepted and rejected applications, and the fraud label is assigned to applications that were rejected as they were suspected of fraud. 

2) The dataset only includes accepted applications and the applications that turned out to be fraudulent are labelled. 

Both of these cases will lead to implicit bias. The first is only an assumed ground-truth label, based on a decision-making model that may already be biased. The second has a selection bias. 

Ideally, the dataset should contain all applications, a flag to say if the application was rejected due to suspected fraud and another variable to note accepted applications that ended up as fraudulent. You would still have the issue of a missing groud-truth for the rejected applications, but getting that label is unrealistic, as is the case in many real-world applications. 

I can appreciate why the authors created synthetic biases for this dataset, but they did not present an investigation of the different biases or error rates that exist in the original dataset, both for ground-truth label and the other variables. 

More minor concerns:
-- I did not think the authors needed to spend as much space as they did detailing issues with existing benchmarks. I think that space would have been better used to describe existing datasets within the broad domain (in fraud detection/banking etc) and explain how the new dataset is superior to those. 
-- The authors give interesting domain information, for example, stating the fraudsters prefer to put a higher age in the application to receive higher credit but don't connect this information to what can be observed in the data itself. 
correctness: The variables seem to be well constructed, as stated above, I have concerns regarding the target label. A more detailed discussion of how the variables in the original dataset were constructed and any limitations or known errors would also be extremely useful.
clarity: I found the paper clearly written. 
relation to prior work: I think mentioning similar existing datasets (in addition to popular benchmarks) and highlighting differences would improve the paper further. 
additional feedback: I think a section with a descriptive analysis of the original dataset (distribution of values of the different features, correlations between features) would be quite useful for the reader. 

Just a suggestion: if used as a benchmark, I think it will be nice to potentially evaluate fairness on a non-binary decision label. For example, instead of accept / reject, it can be: accept with credit / accept without credit / reject. This may represent a more realistic scenario, and will also be very useful as most fairness benchmarks do not go beyond the binary label.
review_title: Fraud Detection Dataset for Fairness Evaluation

final_score: rating: 6: Marginally above acceptance threshold
review_content: summary and contributions: This paper studies the detection of fraudulent bank-opening applications. The authors created a synthetic dataset by selecting the most important features of a machine learning algorithm (LightGBM) and then using GANs to generate new data. Benchmarking variants were created using under/oversampling or by appending new columns. The same algorithm that was used for the feature reduction step was also used to provide an empirical analysis.
strengths: This is a real-world bank account opening application dataset, which could complement existing fraud detection datasets. The use of GANs for the development of anonymised datasets with variations is an interesting idea. 

[UPDATE: Following the rebuttal, I increased my score from 5 to 6, acknowledging that clarifications and new text (e.g., in related work) have been added. I also welcome the effort of the authors to benchmark more (tree-based) methods, which is in the right direction.]
weaknesses: Besides clarity, correctness and ethics, which are discussed in separate sections of this review, the presented dataset is biased towards a specific method, which limits its usefulness. In specific:
* Line 201: Different methods would probably return different features as important. 
* Line 308: The empirical observations are based on the algorithm used to create the dataset and this may harm generalisation - the same dataset may not work well/similarly for other algorithms. 
correctness: The claim for feature reduction is not reasonable, the selection of the evaluation methods is not clear and the evaluation design is problematic. In specific: 
* Line 200: Removing features which are not important to an algorithm isn't necessarily reducing privacy issues. 
* Line 187: Defining a threshold imposed by clients encodes subjectivity, allowing any study to define a different threshold. Also, I find it hard to believe that the clients agree on a single value. At least AUC should have been reported.
* Line 228: Omitting to test on real data sounds the wrong way to me (and couldn't find previous studies that followed it). 
* The models could have been assessed on non-anonymised data, to quantify the performance gap.
clarity: The paper is well written but some points need to be clarified. 
175 The "original strategy" is unclear.
194 This ratio is not clear to me.
202 Random search as a process was not clear to me in this case. On the models? But these should be the best ones. Or were the models trained on random data samples?
230 Is this correlation per feature (real - generated) averaged? If not, please explain.
242 Why did you choose the uniform? Any deviation from the true class ratio could work.
259 It is not clear to me how can you (over/under) sample in a way that only the target group is affected and no other. How is this verified?

relation to prior work: Related work was focused outside fraud detection, which sounds wrong. This paper is about fraud, so the analysed related work of a more general domain, is not that helpful and can be misleading. Furthermore:

* Line 111: "[S]everal documentation issues" is not "same and other" that is stated above (line 101). Where are the other issues mentioned above, regarding sampling, etc.?
* Line 122: Does it or does it mean that existing datasets should first be studied in the light of fairness before we start making new ones? 

additional feedback: **Questions**
* Does this apply disregarding whether money is stolen from the bank or from another client? Please clarify.
* If the bank covers the expenses of any fraudulent applications (line 154), the societal impact is assumed to be due to false positives (the user is not fraudulent but the bank thinks it is), because everyone should be able to open an account. But it sounds hard to believe that a false positive cannot be solved with minimum effort; either the applicant should be arrested or open the account. Please elaborate.
* 181 How do you motivate your selection of 50 years?

**Comments** (line number shown)
* 57 one "for" should be deleted
* 79 was > were
* Page 7: More informative variant names could help the reader (e.g., I --> disparity).
* Caption of Fig. 1 (4th line): Does "performance" refer to Recall or FPR?
* 344 "the first regards" or "with regards to"
* 551 Placeholder text is not deleted.
review_title: New synthetic dataset for fraudulent bank-opening applications

final_score: rating: 5: Marginally below acceptance threshold
review_content: summary and contributions: The authors present a collection of tabular datasets. These datasets were generated by applying CTGAN on anonymized bank account application data. Particular subsets contain prespecified bias patterns that the authors enumerate in their work. The authors also provide results for recall and FP ratios in a fairness-blind LightGBM model.

strengths: The work addresses a well-motivated problem in a mostly clear manner. Additionally, the authors have developed a nice repository and datasheet.
weaknesses: I have the following concerns:

- The authors state “_We follow the original strategy for the evaluation of models in the dataset, by 176 training on the first six months of data and validating the models on the last two months._” – What is the reasoning behind this decision / what are potential pitfalls? Could the difference in prevalence between the two time periods be an issue?
- Whether the original relationships between the features and the targets in the generated datasets is preserved or not is unclear. In particular, I was a bit confused by Table 2 in the appendix and what exactly it is showing.

correctness: The approach appears reasonable and clear.
clarity: The paper is clear and well-motivated.
relation to prior work: The authors clearly outline shortcomings of existing tabular datasets in fairness-related work, such as the Adult, German credit, and COMPAS datasets.
additional feedback: Note that in the compressed data link, the download file name is `bank-acount-fraud.zip` as is missing a second “c”.
review_title: bank-account-fraud

final_score: rating: 6: Marginally above acceptance threshold
review_content: summary and contributions: In the paper, authors share an anonymized and privacy preserving suite of tabular datasets from the field of fraud detection. The authors claim that the datasets can be used for studying fairness of different ML algorithms in practical decision making. Being generated from the field of fraud detection, the datasets fall under tabular data domain, the domain in which, the studies for fairness in ML is lacking, compared to computer vision and NLP tasks.    

To support the claim, the authors have discussed about the properties of datasets under 5 different bias conditions and have shown the fairness (in terms of false positive rate ratio of different age based segments) of LightGBM model on each of these datasets. As mentioned in the paper, a good dataset to study the impact of an ML technique on fairness should have certain properties that include the closeness of use case under study with the real world, impact of ML based decisions on people’s life (e.g. criminal justice, financial service etc.), attributes used to model, scale and recency of datasets etc. The commonly used datasets for studying fairness of ML for tabular data do not fit one or more of the given criteria. The datasets generated by authors do satisfy most (if not all) of these criteria and hence are a good fit to study bias in ML for tabular datasets. 

The contributions of this submission include public sharing of an anonymized and synthetic dataset from a real fraud detection use case. Even though the datasets in this submission are generated from fraud detection field, they can not be used for any real fraud detection task or to study the effectiveness of ML in fraud detection, due to anonymizations. Rather, the datasets are released to be used for domain independent study and comparison of fairness and performance in dynamic conditions from different ML approaches. The authors have released 5 different variants of the base dataset by adding different types of biases based on sample count or fraud rate in minority and majority groups. The variants are targeted to stress test ML methods for their fairness tradeoffs. 
strengths: The datasets released by the authors are worth getting added to the list of (~15) real world datasets that are commonly used to study fairness-aware machine learning. The datasets are available from GitHub but require the user to download, and then load from the zipped files using a parquet reader. 

The authors have taken multiple steps to ensure the protection of real person’s identity in their data generation process. The datasets are generated using CTGAN and any instance that had similarities from the real data were removed to ensure that the datasets don not contain information abut any real person. To help stress test the fairness-aware ML techniques under different biases, the authors provide 5 different varieties of datasets based on varying group, prevalence, and separability biases. 
weaknesses: It is unclear how exactly does the dataset under review adds value to ~15 real world datasets in the research of fairness-aware ML (https://wires.onlinelibrary.wiley.com/doi/full/10.1002/widm.1452). In Section 2.1, the authors merely discuss 3 datasets from related work with missing citations of other related datasets. No empirical comparison of different datasets over the chosen criteria for fairness ML is provided in the submission. Lack of such comparison makes it harder to understand the additional benefit to the fairness-aware ML research community over what already exists today. 

A weakness around data accessibility is lack of a standard code to load and transform the datasets from author’s GitHub repository. Both paper and GitHub repository lack a data sample and in order to see a data sample, the user requires to load the datasets using a parquet reader. 

Apart from the above, other weaknesses include a lack of contribution in the advancement of data generation techniques or in the advancement of prevention of bias in ML. These two factors are not used for deciding the rating as these factors are not a mandatory requirement for this track. While the novelty in data generation technique is not expected, given the topic of the research, a short discussion and comparison with different datasets and different algorithms used for the prevention of bias can be useful to see the impact of the released dataset. 

An additional critique I have for authors (and that is easily modifiable) is that the title and abstract of the paper mention “for fraud detection”, while it is discussed in the paper that the dataset is not relevant for fraud detection studies, but for fairness in ML studies. 

The impact of public release of information about the distribution of fraud/non fraud population among different groups is not discussed. For example, the authors mention that no instance in generated dataset can reveal any information about any real users at the individual level. But, at the population level, whether the datasets can reveal the information about fraud that can later be used by attackers to understand patterns of fraud caught by industry, and use that to design new fraud patterns is not discussed. 

correctness: The datasets are constructed in a sound way. The authors have taken steps to ensure the protection of real person’s identity in their data generation process. The claim about no dataset exists that can meet given selection criteria lacks verification. 
clarity: The paper is easy to follow and clearly written. 
relation to prior work: The comparison with previous contributions is mentioned in the Section 2.1 but the claims about the added value of given datasets over what is currently used by research community is lacking a detailed justification or/and an empirical analysis. The authors only discuss 3 such datasets from previous contributions.  
additional feedback: The suggestions are shared in the answers to other review questions. The authors are welcome to share their concern about any feedback, suggestion for improvement or about stated strength/weakness.
review_title: Tabular dataset synthesized from real fraud datasets to be used for comparing fairness aware machine learning techniques

final_score: rating: 6: Marginally above acceptance threshold
review_content: summary and contributions: The paper presents a suite of datasets for bank-account fraud detection based on data generated by a generative model.
The datasets pose an interesting and important challenge for fraud detection as they deviate from other datasets due to strong class imbalance and distribution shifts over time, induced by the dynamic/adversarial nature of fraud detection datasets. Such benchmarks are therefore relevant and pose interesting new challenges for e.g. AutoML systems.

This synthetic nature of the data has the (possible) advantage of better protecting an individual's privacy as only synthetic data is published. 
This can however not guarantee the reconstruction of individuals in the data. Two possible downsides are that a) some relevant information might be destroyed in the process of variable selection and GAN fitting and b) the individual's data can not be further enriched in practice because relevant identifiers might be missing.

The paper tackles an important gap, and the approach to generating the data is sound. I would however like to see some additional analysis (see strengths & weaknesses) to be fully convinced that the new datasets indeed provide valuable insights.
strengths: The paper is generally well-structured and well-written. It tackles an important problem and I could therefore see such benchmarks being useful for the broader community. I also like that the paper specifically addresses privacy concerns.

The paper motivates and proposes a fairness metric to go with it.I think this is important and relevant. I am however not entirely sure how fairness connects to fraud, I think this should be motivated more thoroughly, e.g. by providing an example scenario where fairness considerations are relevant.

I like that the paper proposes a metric and several concrete settings for study. This allows users to refer to specific studies. I am missing a concrete definition of the evaluation protocol (train/test splits, stratification, ...) which I would deem equally important to ensure similar results and enforce best practices during evaluation.

weaknesses: Making decisions about fraud in dynamic settings can lead to feedback loops as labels are only collected for transactions not deemed fraudulent previously, while denied transactions are likely not evaluated further. I think this should be dicsussed in the paper.

I am not sure that I agree with the approach to enforce separability disparity as this essentially introduces new information that is likely not present in real datasets (noise is correlated with group and label). Additionally the effect of this new information is not easy to quantify. Furthermore, this does not change separability in the other attributes. I think a better approach e.g. based on sampling from the data is needed. 

I think the quality of the generated data is not sufficiently explored. I would expect the following things to hold:

Ranking of e.g. classifier performances is close between the real and simulated datasets

Datasets are reasonably similar when projected to a 2-D space (e.g.  using T-SNE)

Marginal Distributions of generated data are close to real data.

By investigating those aspects, you could make a more compelling case.

Another (minor) concern that could be remedied by additional experiments is the omission of variables using feature importance. 
I think it would be relevant to show how much performance is lost by dropping those variables in order to get a feeling of how strongly this changes the optimization problems (if the dataset is to be used for benchmarking bias mitigation techniques). 
correctness: The dataset is a synthetic version of a private dataset that can not be published due to privacy concerns. In my opinion, the quality of the synthetic data could be more thoroughly investigated (see weaknesses).


clarity: The paper is generally well-structured and well written. 
I think the "FPR Ratio" should be explained more clearly but other then that I could not spot major problems.
relation to prior work: I think the section could be improved by e.g. referencing other settings where synthetic data was created for such benchmarks ( if they exist) but other than that, prior work I am aware of is sufficiently discussed.
additional feedback: I think the applicability could be drastically improved by providing access to the trained GAN model allowing the user to sample points. This could e.g. be achieved by providing access to / wrapping  the GAN converted to an ONNX model. This would allow the user to construct additional scenarios for interesting usecases or e.g. allow to simulate systems querying for additional datapoints in a specific subgroup. I can see that this might come with additional privacy concerns. 

The 1st and 2nd disparities arer obbtained through ... ?oversampling? why is that required if you have access to a generator. This seems odd to me. 

Is there any way to conduct a privacy analysis of the generated data given that you have access to the real dataset, e.g. via k-anonymity? If the source data is differentially private that should mean that any GAN trained on it is also differentially private

The paper should perhaps have a link to the repository. I could not find one in the paper.
review_title: Synthetic Data for Bank Account Fraud

final_score: rating: 7: Good paper, accept
review_content: summary and contributions: Various machine learning methods are used today for many high-stake decision making applications.  In such contexts algorithmic fairness is an important topic.  To test and evaluate ML methods wrt their fairness, benchmark data sets are needed.  While there exist numerous of these for computer vision and NLP tasks, there are only few for tabular data sets.  However, a number of high-stake decision making use cases, such as the considered one of detecting whether the online opening of a bank account was fraud or not, are based on tabular data.  This paper presents the first publicly available, large-scale, and privacy preserving suite of tabular data sets for fraud detection which is very valuable for evaluating ML methods and fair ML interventions.

strengths: The generated data sets address a gap wrt existing benchmarks. These were generated based on a real-world bank account opening application data set, and the properties of the generated data were preserved wrt to the original one.  The different variants generated, correspond to common problems in the real-world, like distribution shifts.  Hence it is expected that these data will be used for evaluating fairness of ML methods in a relevant and realistic scenario.
weaknesses: The limitations of the work are well outlined: (i) There are no theoretical guarantees on privacy as there is no upper bound limit for the metric of differential privacy, and (ii) the information was provided by applicants which could be, intentionally or not, wrong.

The authors also point out that the data set is only meant for evaluating fairness, but cannot be directly employed to real-world fraud detection scenarios.  Given the highly dynamic context here, this was to be expected.
correctness: The construction of the data set is sound.
clarity: The paper is very well written and easy to follow.
relation to prior work: The shortcomings of existing data sets has been discussed, which include: 
- age of the data itself
- measurement bias
- missing values
- label leakage
- use of data sets for a purpose they were not intended for initially.

additional feedback: none
review_title: New dataset for evaluating ML methods and fair ML interventions

Meta-review: metareview: This contribution releases synthetic data generated by a GAN from a real-world bank fraud dataset to provide 6 datasets (each with 1M instances) for studies of machine-learning fairness. The submission generated many discussions between authors and reviewers. It could play an important role as it would contribute to fairness research were there are only few (~15) real-world datasets.

Several issues were raised, such as the adequate consent of the individuals behind the data, which was asserted to be GDPR-compliant, and the present of possible bias in the detect bias where the authors claimed no false positive because accounts labeled as fraud are given the chance to prove that they are legitimate.

The released data is a GAN-generated synthetic one and there are questions to how well it reflects the statistical properties of the original data. In this respect it is to be regretted that the paper comes without plots showing the marginals.

confidence: 2: The area chair is not sure
Decision: Accept