Sun Dec 8th through Sat the 14th, 2019 at Vancouver Convention Center
EDIT: I've read the rebuttal. My opinion/score of the paper hasn't changed: I still think the results are interesting, but the authors spend too long discussing the simpler results, and not long enough discussing the more complex/interesting results. However, the rebuttal has convinced me that the "simpler" results are more novel than I originally understood. Thanks for giving the extra line for Table 2. It demonstrates a continuation of the pattern for Error and D_TV, but not for accuracy parity, which seems unusual, so I'm not sure what to make of it. In this work, the authors present several information-theoretic bounds relating accuracy and fairness in the fair representation learning setup. They discuss properties of binary predictors, relating their accuracy, fairness (under demographic parity), and underlying base rates. They relate some of these properties to learned representations. Finally they present bounds relating the learned representation to the induced error learned by a post-hoc classifier on a downstream task. They also present a result regarding the differing levels of accuracy achieved on two groups from a learned representation. Finally, they present a small experimental section to compare to their theoretical results. The central results in the paper are in Sections 3.3 and 3.4. These results deal most directly with the nature of the learned representations – they are both the most interesting and the most novel results. The first result gives lower bounds on the error (weighted inversely by group size) in terms of several f-divergences between the learned representations for the two groups. This is a nice result. In particular, I haven’t seen the Liese and Vadja lemma used in this setting before; that seems useful. One question I have is regarding dimensionality of the representation space – when are these divergences feasible to calculate? For instance, in a high-dimensional continuous space, TVD is intractable. It would be interesting for the authors to address this question with some discussion. The main result of Section 3.4 is regarding accuracy parity – the gap in error rates achieved on the two groups in the data. This is, in my opinion, an understudied fairness metric, and I was pleased to see some work on it here. Roughly, the accuracy parity is upper bounded in terms of the inherent noise of the data + the distance between learned representations + the similarity of the two groups in the data. One thing is not clear to me: the TV term in this bound seems to be distance in X space (D_0(x), D_1(X)), but the authors describe it as distance in Z space – which is true? The earlier theoretical work (Sec. 3.1 and 3.2) regarding the properties of binary classifiers is, in my opinion, less interesting and less novel. As the authors state on line 219, these sections consider an adversary which only looks at Yhat, rather than the representation. This is a much less rich setup. These results seem very related to results regarding lower bounds on demographic parity from the cited papers by Madras et al. and Zhang et al. – if not overlapping, I would like to see some discussion on the relationship. It’s not so clear to me that these results really have much to do with the paper as presented – they are not really about representation learning. In this sense, it’s a shame that Sec 3.1 and 3.2 are much longer than the more interesting and relevant 3.3 and 3.4. Ideally, I would like to see more exploration of the ideas in 3.3 and 3.4 instead. Notes: - Title: should be plural – “Inherent Tradeoffs in Learning Fair Representations” - Line 44: I don’t’ think this is the first result quantitatively characterizing the fairness-accuracy tradeoff. See Menon and Williamson “The Cost of Fairness in Binary Classification”. I’d recommend making a more precise claim, or clarifying. - Line 128: I may be unfamiliar with notation here: what does it mean that P << Q and also Q << P? - Line 134: typo: “A similar impossibility…” - Table 1: should the Generator for KL divergence be logt, not tlogt? - Line 273: When can we expect optimal decisions to be insensitive to group membership? How does this differ from having identical distributions to begin with? - Experiments: This paper is mainly theoretical, which is fine. I’m not sure the experiments are very enlightening. It might be helpful to try more values of lambda and show a more consistent trend, or try to work in a setting where you can verify the bounds tractably.
The paper "Inherent Tradeoffs in Learning Fair Representation" analyses the trade-off between learning fair representations and the resulting loss for the targetted task. The analysis is done via lowerbounds of the prediction error from fair representations, by the use of f-divergence measures. It also gives an upper-bound of the discrepancy of accuracy across groups. My feeling is that the paper is interesting, theoretically well sounded and rather well written, but the findings are too much obvious. It has a great pedagogical interest, but do we really need all of this theoretical analysis to state that when gaining in fairness tend to reduce the prediction ability ? To strengthen the contribution, it would be required to propose a novel approach based on the bounds derived in the paper. However it is one of the first papers that gives a solid theoretical analysis of trade-offs between fairness and utility. While findings are not surprising, bounds given in the paper can inspire researchers in fairness. Authors of the paper give a possible direction in conclusion. Based on the other reviews and the authors feedback, I changed my mark from 4 to 6.
After Author Rebuttal ================ I agree with acceptance. It's nice to see such a clearly-written paper! That said I would love for the authors to include a discussion in the final version of the paper that addresses some of these questions in my review: I'd like to understand better how this relates to the results of Chouldechova  and Kleinberg et al., . - Is the result here stronger? - Would we expect solutions, such as the suggested instance-weighting future work, to circumvent the above results as well? I'd also like to know how these results generalize - Do the results here extend to equal opportunity? - Would collecting additional data circumvent this result? And more about future work: - Do you think the lower bound is a useful tool to understand base distribution drift over time? - Why does instance-weighting improve fairness? ================ 1. Summary This paper gives a lower bound on the error of fair representations when base rates are different, if they aren't, the fair representations lead to accuracy parity. 2. High level paper This paper is very clearly written. I think there are sufficient experiments. This paper nicely complements the trade-off results of Chouldechova  and Kleinberg et al., . It will certainly be interesting to people. 3. High level technical I enjoyed reading this paper. I have no complaints, but there are a few things I'd like to understand better: I'd like to understand better how this relates to the results of Chouldechova  and Kleinberg et al., . - Is the result here stronger? - Would we expect solutions, such as the suggested instance-weighting future work, to circumvent the above results as well? I'd also like to know how these results generalize - Do the results here extend to equal opportunity? - Would collecting additional data circumvent this result? And more about future work: - Do you think the lower bound is a useful tool to understand base distribution drift over time? - Why does instance-weighting improve fairness? 4. Review summary Overall, this paper is well-suited for publication at neurips.