Venue: NeurIPS 2022 Conference

Reviews:

final_score: rating: 5: Borderline accept: Technically solid paper where reasons to accept outweigh reasons to reject, e.g., limited evaluation. Please use sparingly.
review_content: summary: This paper discusses how to learn a constant feature that can be concatenated to a frozen pre-trained embedding to promote statistical group fairness in a downstream classifier. These extra features, called the “fairness trigger”, are jointly trained with the prediction model with the aim of inducing a demographic parity or equalized odds property in the final solution.
strengths_and_weaknesses: Strengths
* Demonstrates that reprogramming techniques can be adapted to target group fairness metrics in CV and NLP contexts.
Interesting qualitative analysis of the resulting models [Figs 6, 7] suggesting that the learned concatenated constant features cause the downstream classifier to focus less on spurious features (in this case, features that indicate demographic membership). 
* Provides a conceptual proof of concept for reprogramming in a simple bag-of-words classification setting where data are generated according to an anti-causal graphical model [Figure 2].
Weaknesses
* I found the proposed method interesting and I think the neurips community will as well. My main complaint is that the paper, in my opinion, oversells this approach as strictly superior to previous methods. When we apply the fairness trigger in the original input space (e.g. CV), don’t we still need to backpropagate through the entire network to learn the fairness trigger? If yes, there could be a memory savings as we don’t tune as many parameters, but it’s not clear to me that the method would train substantially faster than a finetuning baseline. On the other hand I can see why you would save on compute when applying the fairness trigger *in* the embedding space (e.g. NLP)
* The paper mainly compares against fine tuning methods, but there are other feasible approaches such as adding a linear probe on the frozen embedding (which I don’t believe is considered the experiments). When we teach linear regression we often bring up two alternate parameterizations: (a) \hat y = x^T W + b and (b) \hat y = (\tilde x)^T W. These are equivalent if we define \tilde x as x with an extra constant feature. By analogy, I wonder if there is some equivalence between the fairness trigger (learned bias) and a linear probe/transformation applied on the embedding (learned projection). If the fairness trigger is learning a constant perturbation along some subspace with lots of demographic information, is it possible that a similar solution could be found by simply projecting away that subspace using a linear probe? It seems possible to me, and makes me wonder if the experimental gains are due to the fact that the fairness trigger optimizes a lower dimensional parameter space that would be less prone to overfitting given the limited data available for post-processing. Even if this were the case (might be possible to find out by adding linear probes as baselines in the experiments) the paper would be interesting, but might suggest that fairness reprogramming is best under a constrained data budget, not best in general. 

questions: * I am slightly concerned that the in/post-processing methods could be doing worse simply because of the instability of adversarial training, or due to overfitting given the limited number of data available during fine tuning for the post-processing method [line 244]. I would suggest adding an MMD baseline as an additional baseline (e.g. as applied in https://arxiv.org/abs/1511.00830). This would hopefully be more stable, but may also suffer from overfitting.
* In my opinion, the claim of achieving “lower bias scores over two fairness criteria in the CelebA dataset” [lines 54–55] is not meaningful without defining the prediction task, since many different tasks (related to fairness and otherwise) have been proposed that use the crowdsourced attributes of CelebA. The authors do a good job of providing this context in Section 4 [lines 219–200]; I would suggest adding a basic description of the task to Section 1 wherever the results are summarized.
* I’m unclear on why the JTT paper [12] is cited as a paper that “[learns] task-specific embedding prompts concatenated to the inputs” [lines 36–37]. My understanding of this paper is that it uses the error cases of an ERM reference model to train a robust model via importance weighting. Could the authors clarify the citation (in this context) or remove it?
* I was interested in the experiment showing that the method is not overly sensitive to random seed [lines 291–300]. Calling this a “transfer” is a bit generous—there is no notion of a distribution shift or new prediction task, or a change in the model architecture—but it is good to know that a learned prompt can be reused if the model is retrained on the same data. The claim at the end of the paper that the method “enjoys great transferability” [line 356] seems like an overstatement. I would be especially concerned that adding a single constant feature could suffer from covariate/distribution shift, which was not examined. 
* In Figure 1 it seems that the captions and images should be swapped. I.e. Fig 1a is the border trigger, not the patch trigger, if I understand correctly.

limitations: Yes, the authors discuss several limitations in the appendix. I would encourage including them in the main paper.
review_title: null

final_score: rating: 5: Borderline accept: Technically solid paper where reasons to accept outweigh reasons to reject, e.g., limited evaluation. Please use sparingly.
review_content: summary: This paper introduces a model reprogramming based fairness promoting method, consisting of a fixed ML model and optimizing a set of vectors concatenated on inputs to boost model fairness. An information-theoretic framework is also introduced to explain the rationales of fairness booster. Experiments on one NLP and another CV dataset demonstrate the utility of this approach. 
strengths_and_weaknesses: Strengths: 
1. This paper tackles the fairness issue with reprogramming which is a relatively less explored area in fairness-aware learning.
2. The proposed method can finetune the pretrained model with computational efficiency. 
3. Experiments show the effectiveness of proposed method.

Weaknesses:
1. The paper is not self-contained with several key information in Appendix for a good understanding of the claimed contributions. For example, without the understanding of "It can be shown (in Appendix C) that the posterior distributions.." and "it can be shown (in Appendix C) that the posterior distribution..", the claimed information-theoretic framework remains unclear. 
2. With the above, it is not clear whether fairness can be really achieved by the proposed method.
3. Some related work on reprogramming fairness (for example "Reprogramming FairGANs with Variational Auto-Encoders: A New Transfer Learning Model") is not mentioned in the literature review. This work also related to incremental fairness but lacks of relevant discussion (for example "FAHT: An Adaptive Fairness-aware Decision Tree Classifier").  
4. The size ratio for civil comments is 1/5 which does not align with the motivation that infeasible to retrain or finetune well-trained large-scale models.
5. The caption of Figure 1(a) and 1(b) are misplaced. 




questions: 1. Many biased datasets are tabular represented, how does your work apply on tabular data? Appending additional dimension of vector directly? 
2. Can you summarize why the appended information can always cut off the biased information path? 
3. How do you distinguish this work with pre processing approaches?  
limitations: See comments above.
review_title: null

final_score: rating: 6: Weak Accept: Technically solid, moderate-to-high impact paper, with no major concerns with respect to evaluation, resources, reproducibility, ethical considerations.
review_content: summary: Traditional approaches for fairness in neural networks suggest training or fine-tuning the entire weights of the network to achieve desired fairness criteria. This paper, instead, proposes a reprogramming based technique, called FairReprogram. Considering the neural network fixed, FairReprogram appends a global fairness trigger to the input to achieve fairness improvement with respect to existing fairness metrics. The results are backed up with theoretical analysis and experimental evaluations in NLP and CV datasets.
strengths_and_weaknesses: Strength

- The paper is very well written. Statements are supported with examples. The paper is also well-motivated as reprogramming towards fairness improvement is less costly than retraining or fine-tuning with fairness objectives. 

Weakness:

- How are correlations among features handled in FairReprogram?

- What is the intuition behind adding noise as fairness trigger, such as in patch trigger and border trigger? Does this mean demographic information is confined either in the border of the image or in a specific area of an image covered by the patch?

- In Figure 1, perhaps the captions are wrong as 1(a) should be about border trigger and vice-versa. A careful recheck is encouraged.

- Does the method extend to tabular data with fixed set of features in matrix form? 

- A comparison with existing fairness improvement techniques such as pre-processing, in-processing, post-processing fairness algorithms should be discussed. In which family of fairness algorithm does this approach belong to?
questions: Please address questions in the `weakness` of the paper above.
limitations: There is no negative societal impact of the work, as far as I know.
review_title: null

Meta-review: metareview: Overall the reviews are more or less positive towards weak accept. While there are some remaining concerns (e.g., a wording in the abstract), I think many of the raised concerns are addressed properly and some of them are checked. Hence, I believe this paper is worth being published. 
confidence: Less certain
Decision: Accept