{"id":"bc425736-6bb1-4f11-afc0-6ffd2516f475","arxiv_id":"2506.02017","paper_version":1,"verdict":"CONDITIONAL","confidence":"HIGH","novelty_score":4.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":2,"one_line_summary":"A design proposal that lets users correct automatic gender recognition labels, claiming fairness gains, but without empirical validation.","lead":"This paper proposes giving users of automatic gender recognition systems the ability to correct the predicted gender label, inspired by how people correct each other after misgendering. It argues the 'Fairness through Feedback' mechanism increases fairness for trans and non-binary people, at the cost of system autonomy and efficiency.","discovery_kind":"new_application","skeptic_critique":{"model":"deepseek-v4-flash","headline":"The FtF algorithm auto-confirms the system's guess when the user does not respond within 5 seconds, so silence is treated as agreement; this weakens the claim that algorithmic misgendering becomes correctable in the same way as human misgendering.","rationale":"The reader's weakest assumption—that gender is treated as self-identification and that user override is the correctness criterion—is indeed a load-bearing philosophical premise. If one does not accept that premise, the fairness rationale collapses. However, my stress-test found a more concrete and internal problem: even granting self-identification as the criterion, the proposed algorithm does not reliably implement that criterion because it auto-confirms the system's guess on non-response. This is not a disagreement with the reader's overall conclusion: the paper still merits a conditional verdict because it lacks empirical validation and baseline comparisons, and the reader's central critique stands. I select 'partial' agreement because we identify different weak points: the reader focuses on the definition of the correctness criterion, while I focus on the mechanism's failure to apply even its own criterion to non-responsive users. The verdict remains CONDITIONAL, so no adjustment from the reader's verdict is needed; the condition should now explicitly include removing or rethinking the auto-confirm default.","tokens_in":14837,"tokens_out":3913,"duration_ms":49323,"concrete_test":"Implement two variants of Algorithm 1: the published version where timeout returns the predicted label, and an alternative where timeout returns an explicit 'no label' or 'unspecified' outcome. Run a between-subjects user study (or a simulation using realistic response-time distributions from human-computer interaction data) measuring the proportion of final outputs that misgender participants. If the auto-confirm default produces a materially higher misgendering rate than the no-label default, the fairness benefit is attributable to active correction rather than to the FtF mechanism as specified, and the algorithm should be revised.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central fairness claim rests on the idea that a user can correct a wrong AGR prediction, but Algorithm 1 (Section 4.2) contains a default that undermines this: if the user provides no input within time t1, the predicted label is automatically returned as correct (lines 7–8). Thus a user who does not or cannot actively respond within 5 seconds—due to disability, distraction, social pressure, or reluctance—remains definitively misgendered. The paper explicitly frames the mechanism as ensuring that 'if the initial prediction is incorrect, the user can provide the correct label instead of being definitively misgendered by the system,' but this only guarantees the option to correct, not that misgendering is avoided. Human-human correction does not have a timeout after which silence counts as confirmation; the analogy therefore breaks precisely at the point where the system re-asserts its own guess. The paper acknowledges in Section 6 that placing the burden of correction on marginalized individuals may itself be unjust, but it does not revisit the timeout default in light of that concern. The fairness improvement is thus contingent on active user engagement, and the paper provides no empirical or theoretical argument that this contingency is acceptable, nor does it compare against a version where non-response yields no label rather than the predicted one. This is a concrete, internal design issue, not merely a philosophical disagreement about the nature of gender.","agreement_with_reader":"partial"},"referee_report":{"model":"deepseek-v4-flash","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.","tokens_in":15087,"tokens_out":4528,"duration_ms":54919,"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":[{"comment":"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.","section":"4.2, Algorithm 1"},{"comment":"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":"4.2 and Abstract"},{"comment":"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":"Section 2"},{"comment":"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.","section":"Section 6"}],"minor_comments":[{"comment":"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.","section":"4.2, Algorithm 1"},{"comment":"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.","section":"Figure 1"},{"comment":"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.","section":"4.3"},{"comment":"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.","section":"Table 2"}],"recommendation":"major_revision","confidential_remarks":"This is essentially a conceptual and ethical design paper rather than a technical cs.CV contribution, since it contains no experiments or system evaluation. I am not treating that alone as grounds for rejection, but the fit with the journal's scope should be considered by the editor. The more serious issue is that the paper's central fairness guarantee is broken by the timeout default in Algorithm 1, and the fairness claim is unsupported empirically. These problems are fixable within the manuscript's scope, so major revision rather than rejection seems appropriate."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","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.\n\nThe 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.\n\nBottom 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.","headline":"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.","tokens_in":15639,"tokens_out":3298,"would_cite":true,"duration_ms":35431,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"Automatic gender recognition systems should let users confirm or correct the predicted gender label, making algorithmic misgendering correctable in the way human misgendering is.","keywords":["automatic gender recognition","algorithmic misgendering","fairness through feedback","transgender and non-binary","gender self-identification","facial recognition","algorithmic fairness","label incompleteness"],"falsifier":"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.","tokens_in":14601,"feed_emoji":"🏳️‍⚧️","tokens_out":6704,"duration_ms":62019,"temperature":0.7,"pith_summary":"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.","feed_headline":"User feedback can make gender-recognition AI fairer","feed_subtitle":"A proposal to let people confirm or correct the gender a system predicts, making recognition a tool for self-definition.","key_machinery":"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.","core_discovery":"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.","pith_inferences":["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."],"forward_implications":["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."],"supporting_citations":[{"why":"Supplies the conceptual framework of sexual identity as composed of distinct components (sex, gender, gender expression) that the paper builds on.","marker":"Shively and De Cecco [1977]"},{"why":"Supports the view of gender as an ongoing social and self-identification accomplishment rather than a fixed biological category.","marker":"West and Zimmerman [1987]"},{"why":"Provides the empirical evidence that commercial facial analysis services misclassify transgender people and cannot classify non-binary genders, the problem FtF addresses.","marker":"Scheuerman et al. [2019]"},{"why":"Establishes the accuracy disparities in commercial gender classification, motivating the fairness intervention.","marker":"Buolamwini and Gebru [2018]"},{"why":"Frames AGR as misgendering machines and questions their legitimacy, the critique the paper responds to with a design intervention.","marker":"Keyes [2018]"},{"why":"Documents the frequency of misgendering of trans and non-binary people in daily life, justifying the human analogy.","marker":"Jacobsen et al. [2024]"},{"why":"Contributes the notion of label incompleteness and temporal evolution of gender categories, used in the utility analysis and label set extension.","marker":"Quaresmini et al. [2024]"},{"why":"Shows how AGR has been commercially embedded and how race and gender are constructed in image databases, supporting the premise that AGR is already widespread.","marker":"Scheuerman et al. [2020]"}],"fun_headline_variants":["Let users correct gender AI for fairness","Feedback loop makes gender recognition fairer","Self-correction boosts fairness in gender AI","Algorithmic misgendering fix: user input","Fairness via user feedback in gender recognition"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"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.","fun_headline_variants_meta":{"raw":{"variants":["Let users correct gender AI for fairness","Feedback loop makes gender recognition fairer","Self-correction boosts fairness in gender AI","Algorithmic misgendering fix: user input","Fairness via user feedback in gender recognition"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000148,"raw_usage":{"total_tokens":1209,"prompt_tokens":984,"completion_tokens":225,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":600,"completion_tokens_details":{"reasoning_tokens":160}},"tokens_in":600,"tokens_out":225,"duration_ms":2911,"temperature":1.0,"reasoning_tokens":160,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-07T13:15:34.675688+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"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.","supporting_citations":[{"cited_title":"Components of sexual identity","cited_arxiv_id":null,"evidence_quote":"Supplies the conceptual framework of sexual identity as composed of distinct components (sex, gender, gender expression) that the paper builds on."},{"cited_title":"Doing gender","cited_arxiv_id":null,"evidence_quote":"Supports the view of gender as an ongoing social and self-identification accomplishment rather than a fixed biological category."},{"cited_title":"How computers see gender: An evaluation of gender classification in commercial facial analysis services","cited_arxiv_id":null,"evidence_quote":"Provides the empirical evidence that commercial facial analysis services misclassify transgender people and cannot classify non-binary genders, the problem FtF addresses."},{"cited_title":"Gender shades: Intersectional accuracy disparities in commercial gender classification","cited_arxiv_id":null,"evidence_quote":"Establishes the accuracy disparities in commercial gender classification, motivating the fairness intervention."},{"cited_title":"The misgendering machines: Trans/hci implications of automatic gender recognition","cited_arxiv_id":null,"evidence_quote":"Frames AGR as misgendering machines and questions their legitimacy, the critique the paper responds to with a design intervention."},{"cited_title":"Misgendering and the health and wellbeing of nonbinary people in canada","cited_arxiv_id":null,"evidence_quote":"Documents the frequency of misgendering of trans and non-binary people in daily life, justifying the human analogy."},{"cited_title":"Data quality dimensions for fair ai","cited_arxiv_id":null,"evidence_quote":"Contributes the notion of label incompleteness and temporal evolution of gender categories, used in the utility analysis and label set extension."},{"cited_title":"How we've taught algorithms to see identity: Constructing race and gender in image databases for facial analysis","cited_arxiv_id":null,"evidence_quote":"Shows how AGR has been commercially embedded and how race and gender are constructed in image databases, supporting the premise that AGR is already widespread."}],"review_version":1}