Utility as Fair Pricing
Fairness;generalised entropy;inequality;classification;imbalanced data;cost sensitive learning;fair pricing;utility.
On second thought, the terminology *unit reward rate* in place of *risk-free reward rate* would be both more accurate and shorter. Thank you.
Re: Notation: missing explanations and typos
- Line 364: Corollary 3.2.1 says that the index could be either decreasing of increasing in $\lambda$ depending on the parameter choices. Corollary 3.2.2. says that the index could have a maxima or minima in $\mu$. The turning point may or may not fall within the index domain described in equation (6) for a given value of $\lambda$. If the turning point falls outside of the domain then the index is monotonic in $\mu$. Thus, whatever relation we might expect/want the index to have with respect to $\lambda$ or $\mu$, it is possible for the index to display the opposite (increasing instead of decreasing, maxima instead of minima, for example) by choosing index parameters ($b_{ij}$ and $\alpha$) accordingly.

- Line 449: We could not find the incomplete sentence you mention. If you could paste the line, that would be helpful.

- Q1A: All prior works in Table 1 assume that accurate predictions are equally beneficial, that is, $b_{ij} =\mathrm{benefit}(\hat{y}=i, y=j) = ((1, b_{FN}),(b_{FP}, 1))$ where $b_{FN}$ and $b_{FP}$ are the false positive and negative benefits. All authors in Table 1 effectively assume that the *risk-free reward rate* (proportion of individuals receiving the unit reward) is the model accuracy (since only accurate predictions are awarded the unit benefit). Theorem 3.2 then shows that the index is a function of the risk-free reward rate (model accuracy in prior works) and mean benefit. This does not contradict the findings in Speicher et al. The index is linear in model accuracy for *fixed* mean benefit. Proposition 3.2 in Speicher et al. says the for non-perfect classifiers, the fairness and accuracy optimal classifiers do not coincide. This is true because in constructing one, they do not hold the mean benefit constant. Moreover, the classifier they construct to prove this proposition is not viable because it has an accuracy of less than 0.5.

We will address the remaining issues in a separate response.
cEeF comments and questions
- The index $I(\boldsymbol{b})$ is a measure of *individual* (overall/algorithmic) unfairness.
- The between-group component $I^G_{\beta}(\boldsymbol{b})$ a measure of *group* unfairness.
- In this paradigm unfairness  between groups is a portion of the overall (individual) unfairness which is given by the index.
Index components
Apologies for missing notation explanations and typos. We do believe the choice to overload $b$ is the right one - the confusion is caused by unintended errors/omissions in writing, rather than the overloading itself. We answer specific questions which relate to such issues below. We will tackle broader questions about the work in separate responses which will follow.

- Line 235 should have read: A benefit function can then be defined by simply assigning a non-negative benefit value, to each element of the matrix $b_{ij}=\mathrm{benefit}(\hat{y}=i,y=j)$.
- Line 243: $b\in$ {$b_-,b_+,1$} could perhaps more clearly read, $b_i,b_{ij}\in$ {$b_-,b_+,1$}?
- Line 297: We used $b(p,y)$ only in section 3.1 for brevity/readability, favoring traditional ML notation to demonstrate the connection between empirical risk and the generalized entropy index. Would $\mathrm{benefit}(p,y)$ be clearer?
- Line 313 was indeed a typo and should read: $b_i\in$ {$b_-, b_+, 1$}
- Lines 239 & 490: We believe the notation of $\hat{Y}$ is consistent across these lines. The predicted target is based on some model or algorithm, we refer to it as a model even if the output is binary.
- In general, we treat $i$, $j$, $x$, $p$ and $b$ as dummy variables, because often they are a natural choice in the local context. In the case where we have only binary predictions we use $\hat{y}$, if we have calibrated model score, we use $p$. We use capitals for multi dimensional arrays and random variables, lower case for scalars and lowercase bold typeface for one dimensional vectors. We can add this explanation to the paper if it would help.
- At the end of section 2.2 we add the following text to clarify the definition of the *risk-free reward rate*: We shall describe the proportion of individuals receiving the unit benefit as the *risk-free reward rate* and denote it as $\lambda$. We use the terminology *risk-free*, in the sense that the benefit is known in this case to be unity. In the other cases, we do not know what the rewards $b_{\pm}$ are, they may be more or less than unity. The risk-free (unit) rewards could correspond to a column, row or diagonal. In each case, $b_{\pm}$ correspond to different (remaining) elements of the benefit matrix $b_{ij}$.
- We agree that things get confusing when one must reorient their understanding of $\lambda$, $b_-$ and $b_+$. To remedy this, we have updated our theorems to be clear about their interpretations and specifically replaced the benefit subscripts $\pm$ with $TP$, $TN$, $FP$ and $FN$.
Notation: missing explanations and typos
Yes
No ethics review needed.
See above
The paper focuses on an interesting topic which is diving deeper into a theoretical investigation of inequality indices and why the adoption and usage of the indices appears to resolve the individual / group fairness conflict. The paper provides a compelling argument for the fact that the indices relate more to the accuracy fairness trade-off.
The paper builds on a previous result from Speicher et al. which provides a unified approach to quantifying unfairness at both the individual and group level. In the previous paper, the idea is to use inequality indices (from economics and social welfaire) to measure unfairness. The present paper draws connections between the inequality indices to empirical risk and cost sensitive learning. Most notably, the paper is arguing that previous work chooses arbitrary parameters for experiments, therefore this paper theoretically derives the range of index parameters and connect this to fairness guarantees. They reinterpret the original results in  Speicher et al. claiming to show that the previous empirical results do not necessarily relate to the group and individual fairness tradeoff but more generally to the trade-off between fairness and accuracy.
The paper could be greatly improved for exposition and organizational clarity throughout. 

There are also previous work showing problems with the accuracy fairness tradeoff perspective, and the paper does not seem to engage with this literature in making the argument. Why?
Yes
No ethics review needed.
For practical fairness evaluation, it is possible that we don't have ground-truth label or model accuracy under consideration during inference time but solely focus on individual fairness. In such a case, how would this insight presented help? Can this analysis be used for offline evaluation only?  

For individual fairness, I am not very convinced the within-group component described in Equation 2 reflects individual fairness. The two data points (individuals) may belong to different groups but have strong similarities and are treated differently. Is this decomposition valid for measuring individual fairness correctly?
The paper is clearly written and has many details and insights. The content is dense, causing some reading difficulty. But the overall reading experience is good.

The paper reveals the connection between the fairness metric (in terms of generalized entropy indices) and the model's performance (accuracy) with explicit expression. The explicit connection might be used to unlock future research direction on improving fairness proactively during model training (under the umbrella of cost-sensitive learning).

The paper states the potential problem of misusing generalized entropy indices with wrong parameter choice, which interests me. 

Overall, the paper presents many potentially interesting insights to many people working on fairness research.
This paper presents an in-depth analysis of generalized entropy indices, which reveals the relationship between generalized entropy indices and the predictive accuracy of ML models. As the author claimed in the paper, it provides an explicit connection between fairness metrics and cost-sensitive learning. The paper begins with a clear description of the generalized entropy index and its variants based on different $\alpha$; while the description is not part of the contribution, it provides the reader with a great foundation to continue the reading. The metric analysis is fascinating in revealing $I(\mathbf{b}|\alpha)$ as a function of model accuracy. This paper's analysis is solely based on mathematical derivation without empirical analysis; hence, no experiments are presented. However, I think it would be interesting to see the empirical connection.
One of the obvious weakness is that the paper lack empirical support on the analysis. While rational analysis is good, it is often hard to be linked to practical observation one may face. I think having small demonstrative experiments somewhere in appendix can help the understanding.

The content in this paper is way to dense compare to other work I reviewed. Probably a compressed paper from a journal length work? Probably moving things from appendix into main paper will help the narrative flow. I understand this is due to the paper length limitation, but it also indicate the paper is probably more suitable for a journal publication.

Notations: some notations used in the paper is not very clearly stated. E.g. $b_{i,j}= ((1,b_{-}), (b_{+}, 1))$. Line 323. I presume this is the benefit associated with confusion matrix. But it is better clearly stated.
Yes
No ethics review needed.
There appears to be several abuses of notation (as well as errors) that made reading confusing... can the authors please clarify on the following:

- Each individual $i$ has a benefit $b_i$ (line 122), $\mathbf{b}$ is a benefit array (line 123), but then benefits are described with two subscripts $b_{ij}$. , $b$ is in the set of $b_{-}, b_{+}, 1$ (line 238 and 243). Is $b$ is being used to describe both individual benefit (elements of the benefit array), as well as the benefit matrix?

- On line 297 $b$ is then called as a function $b(p,y)$. Perhaps using a different variable for the benefit function, the individual benefit, and the benefit matrix would add clarity?

- Line 313 $b$ is equal to the set of $b_{-}, b_{+}, 1$. I'm guessing this is just a typo where = was used instead of $\in$?

- The assignment $b_{ij} = ((1,b_{-}), (b_{+},1))$ (line 368) makes sense in context, but is confusing against the way $b_i$ was previously defined (line 122). Does it make sense to again use a different variable to denote the benefit matrix?

- The notation used changes in the discussion. For example, $\hat{Y}$ is defined as the predicted target (line 239), but then $\hat{Y}$ is a re-defined as a model (line 490). Throughout the paper $\lambda$ is the risk-free reward rate (line 342), but then $\lambda$ is re-defined as the model accuracy (line 515). Ultimately, these changes are understandable in context, but perhaps the authors could make the paper stronger/clearer if notation was unified throughout.
The core idea of the paper is strong: while the fairness community has been prolific in creating fairness metrics, there are still gaps in understanding the behavior of those metrics in different setting. I found myself wanting the same type paper, but for other well-known fairness metrics like EO, demographic parity, FPR/FNR ratios (these are not parameterized, but they do behave differently depending on underlying conditions like group base rates or classifier accuracy).

The authors also do a good job presenting theoretical claims and justifying them.
The authors conduct an extensive, data agnostic analysis of "generalized entropy indices" as a fairness metric. The metric, which has implications for both group and individual fairness, is dependent on parameters. Importantly -- as the authors point out -- there is little work on understanding how these parameters should be set, and what those settings imply for balances between group and individual fairness.
- Motivation: I am not fully convinced by the motivation of the paper re: why generalized entropy indices are important. The authors support their importance by noting that the metric has been implemented in libraries created by IBM, Microsoft, and Amazon — but this argument cuts both ways: it’s also true that other well known fairness packages like Aequitas and Microsoft FairLearn do not include generalized entropy indices. Instead, a much stronger way to motivate this paper would be to present a compelling example/scenario where generalized entropy indices are an ethically fair/correct fairness metric. In general, this is a core tension in individual fairness work, but it is navigable (see https://dl.acm.org/doi/abs/10.1145/3447548.3467349).

- Discussion: I found the discussion confusing and at times rambling and hard to follow. For example, on Lines 496-500, the authors write about viewing algorithmic fairness through the lens of derivative pricing — why derivative pricing? There are also strong statements like, “as a lawmaker we want to ensure the market is indeed free…” Likely many agree with statement, but it feels out of place in a paper about algorithmic fairness. I would recommend the authors choose lenses more appropriate for fairness settings.

- Discussion: In the last paragraph of the discussion, I was hoping for guidance to practitioners on how to set $\alpha$, but it fell short for me — perhaps because of the lack of context. For example, throughout the paper and discussion a “decision maker” is discussed (e.g. Line 533): who is this decision maker, what is the decision being made, what are their values, and what is the setting? If the authors grounded this discussion in a real-world fairness example, it would greatly strengthen the discussion.

- The paper ends abruptly. I would recommend the authors add a conclusion.

Overall, I am open to increasing my score if the authors can ground the paper’s motivation, findings and discussion in a compelling real-world example where generalized entropy indices are the correct choice of metric.
Yes
No ethics review needed.
1. The authors state that one primary contribution is to "argue that in order to represent individual fairness, the index must be orthogonal to model accuracy. For the parameter choices made Speicher et al. (2018), we show that the index is a linear function of model accuracy, and thus cannot represent individual fairness according to this independence constraint..." However, I can not find where exactly this is discussed in the main text. This appears to be a different finding/interpretation from the Speicher et al. paper, so it warrants thorough discussion. Is there somewhere I am missing where this occurs?
2. Theorem 3.5 provides a parameter selection characterization for individual fairness. Is there somewhere in the literature that has an analogous result for group fairness? If not, is this one potential follow up direction that could be pursued?
1. The authors do a good job explaining that while generalized entropy indices for fairness is common in practice, the parameter selection methods for these metrics are ad-hoc and often not well justified.
2. I appreciate the avoiding harm and avoiding undue credit as theoretical examples of how Theorem 3.3 and Theorem 3.4 can aid in a better parameter selection process.
3. Similarly, I can see how the characterization given in Theorem 3.5 would be useful for fairness parameter selection.
The primary goal of the work is to obtain a deeper understanding of the class of generalized entropy index based unfairness metrics proposed in [1]; the primary tool the authors use to achieve this is deriving more interpretable representations of the aforementioned unfairness metrics. The motivation for this process is to allow for better parameter selection in generalized entropy indices which are used to produce fair models. To support this, the authors use their theoretical contributions to provide a characterization parameters that allow for enforcement of individual fairness. Theoretical examples are also given.

[1] Speicher et al. A Unified Approach to Quantifying Algorithmic Unfairness: Measuring Individual & Group Unfairness via Inequality Indices. 2018.
1. The primary weakness of the work is the lack of any empirical evidence that demonstrate the usefulness of the results. While the authors take care to derive several new representations for the generalized entropy indices and provide some theoretical examples for how these representations can help with parameter selection for fairness criteria, basically no empirical evidence is provided. In particular, I believe that the work requires at least a couple experiments that (i): show that naive or standard parameter selection leads to poor fairness performance and (ii) Show that selection of parameters suggested by the authors theory alleviates this issue. It seems the major works theirs is based off all include experiments with real data, so I believe this is a reasonable ask.

2. In general, the writing needs work. I provide further suggestions on this below.

*More detailed writing comments*
1. I think the primary objective/motivation is not super clear. My interpretation is the point is to "aid with fairness parameter selection", but I don't think this is demonstrated well enough. One suggestion I have is to re-organize the main contributions list in the introduction; I think it is sufficient to pick the 1-2 broad points the reader should take away from the paper, rather than list every idea in the work.
2. Please provide a formal, mathematical definition in your notation for the risk free reward $\lambda$ (I understand that this can change based on choices for the benefit function, maybe this can be rolled in with point 4).
3. Line 364; please provide a further discussion on how each corollary demonstrates the observation that a poor choice of parameters leads to a metric that is behaving opposite to which it should. This observation is not immediate it to me just examining the corollaries.
4. Around line 220; I think it would be very helpful to have a plot or table demonstrating the relationship between the various choices to assign to $\lambda$, and the benefit map $B$. Similar to this, when a choice for $b_{ij}$ is made (for example line 378) it should be more clear what position in the Matrix corresponds to what instance (e.g false positive, false negative etc...). In general I found this hard to track throughout the paper
5. Line 449 has an incomplete sentence
6. The conclusion/discussion section has multiple ideas that require clarification. For example, the final bit " In some sense, the choice α = 0 results in a mis-pricing of an individual not dissimilar to the mis-pricing of vanilla options under the assumption of constant volatility demonstrated by Black Scholes and Merton’s log-normal model which empirically demonstrates the existence of volatility smile. Here the invalid assumption at the root of the mis-pricing, is that errors and group membership are uncorrelated." is an interesting analogy for the phenomenon being discussed, but I do not think that readers at an ML conference are familiar enough with Merton's log-normal model for this to be particularly meaningful. Moreover, references to these models are provided here. I felt this was a repeating pattern in this section.
Examination of the use of generalised entropy indices as utility functions.
@inproceedings{
anonymous2024utility,
title={Utility as Fair Pricing},
author={Anonymous},
booktitle={Submitted to The Thirteenth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=6jA1R0Z1G2},
note={under review}
}
In 2018, researchers proposed the use of generalized entropy indices as a unified approach to quantifying algorithmic _unfairness_ at both the group and individual levels. Using this metric they empirically evidenced a trade-off between group and individual fairness. The definition of the index introduces an array of new parameters; thus, while the construction of the metric is principled, its behavior is opaque. Since its publication, the metric has been highly reproduced in the literature, researched and implemented in open source libraries by IBM, Microsoft and Amazon; thus demonstrating traction among researchers, educators and practitioners. Advice or grounded justification around appropriate parameter selection, however, remains scarce. Nevertheless, the metric has been implemented in libraries with default or hard-coded parameter settings from the original paper with little to no explanation.

In this article we take an intentionally data agnostic (rational, rather than empirical) approach to understanding the index, illuminating its behavior with respect to different error distributions and costs, and the effect of placing constraints on it. By adding the simple requirement that the the resulting fairness metric should be independent of model accuracy, we demonstrate consistency between cost sensitive learning and individual fairness in this paradigm. By viewing a classification decision as a transaction between the individual and the decision maker, and accounting for both perspectives, we prove that, with careful parameter selection, the concepts of utility and (group and individual) fairness can be firmly aligned, establishing generalized entropy indices as a natural extension of utility, in the quest to mitigate bias in machine learning.
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Fairness generalised entropy inequality classification imbalanced data cost sensitive learning fair pricing utility.
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Utility as Fair Pricing
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