Fairness through Feedback: Addressing Algorithmic Misgendering in Automatic Gender Recognition

Camilla Quaresmini; Giacomo Zanotti

REVIEW 4 major objections 4 minor 61 references

Fairness through Feedback: Addressing Algorithmic Misgendering in Automatic Gender Recognition

T0 review · 4 major / 4 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read Automatic gender recognition systems should let users confirm or correct the predicted gender label, making algorithmic misgendering correctable in the way human misgendering is.

desk verdict A clearly argued, honest conceptual proposal for user-correction in AGR, but the timeout default, the undefended self-identification premise, and the lack of any baseline keep it from being more than a promising sketch. read the letter →

arxiv 2506.02017 v1 pith:PPUQBVYY submitted 2025-05-28 cs.CV

Camilla Quaresmini , Giacomo Zanotti This is my paper

classification cs.CV

keywords automaticgenderrecognitionalgorithmicmisgenderingfairnessthroughfeedbacktransgenderandnon-binaryself-identificationfaciallabelincompleteness

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper argues that Automatic Gender Recognition (AGR) systems misgender people not only because their classifiers are inaccurate, but because gender itself cannot be inferred from facial features. Its central proposal, Fairness through Feedback (FtF), adds a step in which the system shows its predicted label and lets the user confirm it or replace it with the correct one, including non-binary labels. The authors draw on the analogy of human-human misgendering, where correction is normally possible and final, and argue that AGR should recreate that dynamic. If adopted, the system's output would no longer be a verdict on the user but a claim the user can override, positioning the technology as a tool for self-definition.

What carries the argument

The central mechanism is the Fairness through Feedback (FtF) loop: after a convolutional neural network (MobileNetV2 pretrained on ImageNet) predicts a label from a face image, the system asks the user to confirm or correct the label, with a timeout $t_1$ that auto-confirms the prediction, and validates the user's input against a label set (man, woman, non-binary) that can be extended. The load-bearing identity is the epistemic claim that gender is constituted by self-identification ('if x perceives herself as a woman, then x is a woman'), which makes the user's correction the correctness criterion rather than a mere preference. The mechanism also includes a controlled update policy in which collected feedback can be used to improve accuracy for previously zero-accuracy classes such as non-binary, with periodic assessment before the system is updated.

What would settle it

A deployment study that logs every user correction and follows up with repeated self-identification prompts would falsify the ground-truth assumption if a large share of users give inconsistent corrections across sessions without identifying as gender-fluid; any such inconsistency would show that user feedback is not a stable ground truth for gender.

Watch

Extended reading notes

Core claim

The central claim is that AGR fairness can be substantially increased by making the classification loop interactive: the user's self-identification, not the classifier's inference, is the ground truth for gender. The paper distinguishes sex, gender, and gender expression, observes that gender is a matter of self-identification, and notes that current AGR systems output labels that map onto sex categories while purporting to predict gender. Because human misgendering is open to correction, the paper proposes that algorithmic misgendering should be too. The Fairness through Feedback algorithm displays the predicted label and prompts the user to confirm or correct it within a short time window, with the corrected label returned as final. The authors acknowledge this reduces the system's autonomy and efficiency, but frame that as the price of fairness, and they restrict legitimate AGR use to contexts where the output does not gate access to services or benefits.

Load-bearing premise

The mechanism assumes gender is defined by self-identification alone, so a user's correction is always the correct answer; if gender is instead taken to mean legal sex, assigned sex, or a context-specific membership, user override is no longer the correctness criterion and the fairness rationale collapses.

Editorial extensions

If this is right

If AGR output can be corrected by the user, algorithmic misgendering becomes a correctable event rather than a final classification, reducing the psychological harm documented in misgendering research.
With binary training data, the initial accuracy of the non-binary class will be near zero, but feedback-driven data collection is expected to raise it; the paper proposes periodic accuracy checks before updating the system.
The utility of an AGR system over time is governed by the balance between accuracy growth and label-set incompleteness; unless accuracy grows fast enough, utility can decline even as accuracy improves.
AGR systems should not be deployed in contexts where their output grants or denies access to services, because that is where override can be abused and where misclassification is discriminatory.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

One could test whether the mere presence of a feedback prompt changes user trust or felt respect compared to a system that returns a single label, even when the user never corrects the output.
The FtF design implicitly places the burden of correction on the user; a testable extension would measure whether repeated correction is itself fatiguing or harmful for trans and non-binary users over time.
The label set is fixed at man/woman/non-binary in the prototype, so an extension would compare this constrained set against open-ended user-generated labels for both respect and data quality.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

I read the paper. Short version: it's a well-written conceptual proposal to add a user-correction step to AGR systems, framed as recreating the correction dynamic of human-human misgendering. That framing is genuinely new in the AGR fairness literature, which has mostly operated on labels and datasets. The authors are also admirably honest: they explicitly say the U∝A/L relation is untested, acknowledge the burden the mechanism places on marginalized users, and constrain legitimate AGR use to contexts where the output doesn't gate services. Credit where due: the paper does a good job distinguishing sex, gender, and gender expression, and the Giggle case discussion is clear-eyed.

The soft spots are real but not disqualifying. The most concrete one is in Algorithm 1: if the user does not respond within five seconds, the predicted label is auto-confirmed. That means silence counts as agreement, which breaks the analogy to human correction and weakens the claim that misgendering is no longer definitive. The stress-test is right about this. The fix is simple—return no label on timeout rather than the predicted one—and the authors should at least discuss why they did not choose that default. Second, the premise that gender is constituted by self-identification is asserted, not defended. If someone thinks AGR is meant to track something else, the fairness rationale collapses. The authors need to engage that question directly. Third, there's no baseline against simply asking users for their gender, an alternative the paper mentions only to set aside. I would not demand experiments from a position paper, but without any comparison or prototype evaluation, the fairness improvement is a claim, not a result.

Bottom line: this deserves a serious referee. It's a worthwhile discussion piece for people working on fairness in face recognition and trans/non-binary inclusion. I'd send it to peer review, with a request that the authors address the timeout default, defend the self-identification premise, and add at least a minimal comparison against the 'just ask' baseline. Those revisions would turn a promising sketch into a solid contribution.

Referee Report

4 major / 4 minor

Summary. The paper argues that Automatic Gender Recognition (AGR) systems conflate sex, gender, and gender expression, and that algorithmic misgendering is especially harmful because, unlike human misgendering, it cannot be corrected. It proposes a 'Fairness through Feedback' (FtF) mechanism in which the system first displays a predicted gender label and then invites the user to confirm or correct it, with the corrected label being returned as final. The proposal is developed conceptually: Section 2 distinguishes sex, gender, and gender expression; Section 3 reviews AGR methods and their biases; Section 4 introduces Algorithm 1 and a qualitative model of AGR utility over time; Sections 5 and 6 discuss objections and future work. The paper contains no experiments, baseline comparisons, or quantitative evaluation; its central fairness claim is presented as a design principle rather than as a demonstrated result.

Significance. If the FtF design were validated, it would offer a concrete, implementable mechanism for reducing algorithmic misgendering and would connect long-standing philosophical critiques of AGR to an interface-level intervention. The paper is conceptually serious and shows good command of the relevant ethics and HCI literature, and it is commendably explicit about several of its own limitations, including the burden that correction places on marginalized users and the fact that the utility model in Section 4.3 is untested. However, the central claim that FtF 'significantly increase[s] fairness levels' is asserted rather than demonstrated, and the proposed algorithm as written contains a timeout default that directly undermines the paper's own stated guarantee. The contribution is therefore best seen as a promising but incomplete design proposal that needs both algorithmic repair and empirical or at least formal support before its fairness benefits can be accepted.

major comments (4)

[4.2, Algorithm 1] Lines 7-8 of Algorithm 1 automatically confirm the predicted label whenever the user provides no input within the timeout, which contradicts the paper's central claim that 'if the initial prediction is incorrect, the user can provide the correct label instead of being definitively misgendered by the system.' A user who does not or cannot respond within t1 - due to disability, distraction, social pressure, or reluctance - is still definitively assigned the system's guess, and the analogy with human-human correction fails because silence is not treated as an opportunity for correction. Additionally, line 4 overwrites the input parameter t1 with the constant 5, so the advertised configurable time limit is not actually used. The algorithm should be revised so that non-response does not silently confirm the prediction; for example, it could return an explicit 'unverified' state or request a second confirmation, and the parameter should be honored.
[4.2 and Abstract] The paper asserts that the FtF mechanism 'represents a way to significantly increase fairness levels in AGR,' but it provides no empirical evidence, no user study, and no comparison against either existing AGR systems or a variant of the proposed algorithm without the timeout default. Footnote 11 mentions a 'preliminary prototype' for internal evaluation, but no results are reported. The fairness benefit is therefore conditional and unmeasured. At minimum, the authors should either provide a small pilot study with representative users, or explicitly reframe the paper as a design proposal whose fairness hypotheses remain to be tested.
[Section 2] The entire FtF mechanism rests on the premise stated in Section 2 that 'if x perceives herself as a woman, then x is a woman,' i.e., that gender is constituted by self-identification. This premise is load-bearing and is asserted rather than argued for. If gender classification is ever asked to track something other than self-identification - sex assigned at birth, legal gender, or context-specific categorical membership - then user override is no longer the correctness criterion and the fairness rationale for FtF collapses. Section 5.1 restricts legitimate AGR use to non-gating contexts, but that restriction does not itself establish that self-identification is the correct ground truth for all remaining uses. The authors should explicitly discuss the conditions under which self-identification should be authoritative.
[Section 6] The paper acknowledges in Section 6 that 'placing the burden of correction on marginalized individuals may itself be unjust,' but it defers this concern to future work. This is a central ethical limitation rather than a peripheral one, and it interacts with the algorithm's design: the 5-second timeout forces users to react quickly or be silently assigned the system's prediction, which is particularly problematic in public, hostile, or surveillance-like settings. The paper should address this by discussing design changes that mitigate the burden (e.g., no required response, or an explicit opt-out) or by arguing why the burden is acceptable in the permitted deployment contexts.

minor comments (4)

[4.2, Algorithm 1] If the user enters an invalid string, Algorithm 1 silently returns the predicted label (lines 11-12). A user who mistypes or uses an unanticipated label will therefore have their prediction confirmed without any feedback. The pseudocode should return an error or prompt the user to retry rather than silently accepting the system's guess.
[Figure 1] The caption and footnote state that Figure 1 is a 'provisional placeholder' and that the final version will include a photograph of one of the authors. This is not appropriate for a submitted manuscript; the figure should either be a real diagram of the mechanism or be omitted.
[4.3] The relationship UAGR(t) ∝ A(t)/L(t) is introduced without formal definitions of A(t) and L(t), and the paper itself states that its 'validity remains to be tested empirically.' Since this is a non-standard and unvalidated model, it should be clearly labeled as a conjecture or hypothesis, and the monotonicity assumptions should be stated more precisely.
[Table 2] The table omits the #agender, #genderqueer, and #nonbinary hashtags without explanation, even though those categories are central to the paper's argument. A short note explaining the omission (e.g., the original study's limitations) would help the reader interpret the table.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: FtF is a design proposal whose fairness benefit follows from an explicit normative premise, not from fitted inputs or a self-citation chain.

full rationale

The paper is a conceptual and design proposal, not an empirical derivation. The Fairness through Feedback (FtF) mechanism in Algorithm 1 takes a predicted label and optionally a user correction; its stated benefit — that a user can correct a wrong AGR output — is a direct property of the algorithm, not a quantity derived from fitted parameters or from a prediction that is forced by construction. No parameter is fit to data and then renamed as a prediction, and no target quantity is defined in terms of the mechanism itself. The only self-citation (Quaresmini et al. 2024) is used as background on label incompleteness and binary mutual exclusivity; it is not load-bearing for the central FtF proposal. The paper's premise that gender is self-identification ('if x perceives herself as a woman, then x is a woman') is an explicit ethical/philosophical assumption, not a result obtained from the mechanism; the fairness claim depends on that premise, but that is a substantive normative commitment rather than a circular derivation. The paper also flags its own limitations: Section 6 acknowledges that 'placing the burden of correction on marginalized individuals may itself be unjust,' and Section 4.3 states that the proposed utility relationship 'remains to be tested empirically.' These admissions further indicate that the authors are not presenting fitted results as predictions. Algorithm 1's five-second timeout, after which silence is treated as automatic confirmation, is a real design weakness that weakens the fairness guarantee for users who cannot or do not respond, but it is a limitation of the proposal, not a circularity: it does not reduce the central claim to its own inputs. Verdict: no significant circularity.

Assumptions & free parameters 2 free parameters · 4 assumptions · 1 invented entities

The paper contributes a normative design proposal rather than a derivation. Its demands on the reader are mostly domain assumptions about gender, inference, and value priorities; there are no fitted constants. The only hand-chosen parameters are the feedback timeout and the label set.

free parameters (2)

t1 timeout for feedback = 5 seconds
Hardcoded in Algorithm 1 ('Set time constraint: t1 = 5 seconds'), overriding the function argument; chosen by hand, no user study supports this value.
valid feedback label set = man, woman, non-binary
The algorithm only accepts these three labels; described as extendable, but the mechanism's behavior, including automatic confirmation for invalid input, depends on this arbitrary partition.

assumptions (4)

domain assumption Gender cannot be inferred from observable facial features
Stated in Section 3 and relied on throughout: if gender were inferable, classifiers could be correct and user override less necessary.
domain assumption Self-identification is the criterion for a person's gender
Section 2: 'if x perceives herself as a woman, then x is a woman.' This normative premise justifies treating user feedback as ground truth.
domain assumption Fairness should be prioritized over efficiency and system autonomy
Section 5.2 explicitly frames the trade-off as a 'clash of ideals' and chooses fairness; the argument does not derive this priority.
domain assumption Human-human misgendering correction is a valid design model for human-machine interaction
Section 4.1 builds the FtF mechanism on the analogy to interpersonal correction, despite noting AGR systems are non-dialogic and not moral agents.

invented entities (1)

Fairness through Feedback (FtF) mechanism
purpose: A user-interaction layer that lets a classified person confirm or correct an AGR system's gender label before final output
Proposed as a new design artifact; no prototype is public and no empirical study tests its effectiveness, so it has no falsifiable handle outside the paper.

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Cite this review

Pith. "Pith review of Fairness through Feedback: Addressing Algorithmic Misgendering in Automatic Gender Recognition." pith.science (2026). https://pith.science/paper/PPUQBVYY

@misc{pith2026250602017,
  author       = {Pith},
  title        = {Pith review of: Fairness through Feedback: Addressing Algorithmic Misgendering in Automatic Gender Recognition},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/PPUQBVYY}},
  note         = {Machine review of arXiv:2506.02017}
}

read the original abstract

Automatic Gender Recognition (AGR) systems are an increasingly widespread application in the Machine Learning (ML) landscape. While these systems are typically understood as detecting gender, they often classify datapoints based on observable features correlated at best with either male or female sex. In addition to questionable binary assumptions, from an epistemological point of view, this is problematic for two reasons. First, there exists a gap between the categories the system is meant to predict (woman versus man) and those onto which their output reasonably maps (female versus male). What is more, gender cannot be inferred on the basis of such observable features. This makes AGR tools often unreliable, especially in the case of non-binary and gender non-conforming people. We suggest a theoretical and practical rethinking of AGR systems. To begin, distinctions are made between sex, gender, and gender expression. Then, we build upon the observation that, unlike algorithmic misgendering, human-human misgendering is open to the possibility of re-evaluation and correction. We suggest that analogous dynamics should be recreated in AGR, giving users the possibility to correct the system's output. While implementing such a feedback mechanism could be regarded as diminishing the system's autonomy, it represents a way to significantly increase fairness levels in AGR. This is consistent with the conceptual change of paradigm that we advocate for AGR systems, which should be understood as tools respecting individuals' rights and capabilities of self-expression and determination.

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