Pith. sign in

REVIEW 3 major objections 5 minor 209 references

Algorithmic Gender Prediction Is Illegitimate, But Gender Imputation Can Yield Valid Measurements

T0 review · 3 major / 5 minor · reviewed 2026-08-14 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read Algorithmic gender prediction is always illegitimate, but gender imputation used in the aggregate can still yield valid measurements of traditional sexism—discrimination against women and femininity—even as it remains harmful toward…

desk verdict A genuinely useful conceptual separation of legitimacy from validity for gender imputation, with an empirical premise that remains untested and should be flagged in review. read the letter →

arxiv 2608.13444 v1 pith:XAPHMNNJ submitted 2026-08-13 cs.CY

Evan Dong , Angelina Wang This is my paper · ORCID
classification cs.CY
keywords genderimputationpredictionmeasurementvaliditylegitimacytraditionalsexismoppositionalalgorithmicfairnesstransgenderandnonbinarypeople
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

Gender imputation—predicting gender from names, faces, or other cues to fill in missing demographic data—is a common tool for measuring gender disparities in hiring, film, and image generation. Yet transgender and nonbinary scholars have argued that algorithmically assigning gender is morally wrong. This paper tries to reconcile those positions by separating two questions: whether gender prediction is illegitimate, meaning it denies people the authority to self-determine their gender, and whether it is invalid, meaning it produces unusable measurements. Its central claim is that imputation is always illegitimate but can sometimes be valid—specifically for measuring traditional sexism, discrimination that targets women and femininity, because that form of discrimination operates on perceived gender rather than on gender identity. The paper argues that scoping imputation narrowly, aggregating results, and reserving it for cases with no reasonable alternative can turn an unethical practice into a usable measurement tool without legitimizing gender prediction itself.

What carries the argument

The argument is carried by two conceptual distinctions and one definition. The first distinction separates legitimacy—whether a prediction normatively deserves social authority, judged here by whether it restricts a person's capacity to self-determine gender—from validity, whether a measurement accurately captures the concept it claims to measure. The second, from transfeminist literature, distinguishes traditional sexism (discrimination against women and femininity) from oppositional sexism (discrimination against gender deviance and non-normativity, including transphobia and cissexism). The definition is that of gender imputation as a subset of gender prediction: prediction serves a limited auditing or descriptive end and is interpreted only in the aggregate, at the structural level rather than as claims about individuals. Together these let the paper argue that traditional sexism is carried by perceived gender and social position, so aggregate estimates of perceived gender can validly index it, while the same estimates remain illegitimate for the same reason they are harmful: they assume the very authority over gender that self-determination denies them.

What would settle it

A correspondence audit could settle it: submit matched resumes or applications in which targets share the same gender identity but differ in perceived-gender cues, such as name and photo, and record real decision-makers' responses. If callback or hiring rates track perceived gender, the validity premise holds; if they track identity instead, the premise fails. A second check compares imputed-perceived-gender disparity estimates against self-reported-identity estimates in the same population; systematic divergence in settings where identity-based discrimination is known to operate would falsify the claim that imputation validly measures sexism there.

Watch

Extended reading notes

Core claim

The paper's central claim is that illegitimacy and invalidity are distinct failings, and judging one does not settle the other. Drawing on transfeminist theory, it distinguishes traditional sexism—the privileging of masculinity and targeting of women and femininity—from oppositional sexism, which targets gender deviance and includes transphobia, homophobia, and cissexism. Because traditional sexism is triggered by how a person is perceived and socially positioned rather than by how they identify, an imputation model that estimates perceived gender from names, faces, or appearance can in principle measure the disparities this sexism produces, and can continue to do so even though every such prediction is illegitimate under the paper's self-determination criterion. The paper does not claim all imputation is valid: it is often invalid for the same reasons it is illegitimate, and it can never validly measure oppositional sexism, whose harms it necessarily misses. Valid use requires shifting the target from gender identity to a narrowly scoped concept such as "perceived gender based on a resume," aggregating continuous predictions rather than discretizing them into fixed labels, and reserving imputation for settings where anti-discrimination benefits cannot be obtained through other reasonable means.

Load-bearing premise

The load-bearing premise is that traditional sexism is triggered by perceived gender and social position, and that the cues an imputation model uses—names, faces, appearance—are the same cues that trigger sexist discrimination, so aggregate imputed perception tracks the real disparity.

Editorial extensions

If this is right

  • Researchers may use imputed gender to measure traditional-sexism disparities, such as the share of perceived women in a workplace or film, without contradicting the ethical ban on gender prediction, because the ban concerns legitimacy, not validity.
  • Imputation scores should be kept continuous and aggregated, never discretized into fixed gender labels or used for individual-level decisions, because that preserves convergent validity with self-reported measures.
  • The target concept must be narrowly scoped to the specific gender perception at stake, such as "perceived gender from a written name," and imputation is unjustified whenever self-reported or other legitimate data could be obtained instead.
  • Oppositional sexism—harms targeting transgender and nonbinary people—cannot be measured by gender imputation, so new methods are needed to evaluate it, and its neglect is itself a form of harm.
  • Even valid imputation remains illegitimate, so deployment requires weighing benefits against harms and minimizing the restrictions on gender self-determination.

Reading between the lines

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

  • This analysis suggests a testable empirical program: correspondence audits that pair the same gender identity with different perceived-gender cues, such as names or photos, could verify that decision-makers' discriminatory behavior tracks perceived gender, which would ground the paper's validity premise in data rather than assertion.
  • The legitimacy–validity split plausibly extends to race and age imputation, where similar binds arise; the paper leaves this extension open, and the same scoped-measurement analysis could clarify when surname-and-location imputation is defensible.
  • Read as a measurement agenda, the paper's recommendations set an evaluation standard: any imputation-based disparity claim should report uncertainty under misclassification error and be validated against a self-reported subsample, making "valid" verifiable rather than asserted.
  • If discrimination in a specific domain tracks gender identity rather than perception, such as employment decisions based on disclosed identity, the paper's validity claim would not transfer to that domain, so practitioners need a diagnostic per setting.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 5 minor

Summary. This position paper argues that algorithmic gender prediction is always illegitimate because it restricts individuals' agency to self-determine their gender, but that gender imputation—prediction used for aggregate disparity measurement—can nevertheless yield valid measurements of traditional sexism, since traditional sexism operates on perceived gender and social position rather than on self-identified gender identity. The authors draw on transfeminist literature, especially Serano's distinction between traditional sexism and oppositional sexism, and on measurement theory (Adcock and Collier, Messick) to separate questions of legitimacy from questions of validity. They offer recommendations for when imputation might be justified, including narrowly scoped gender constructs, using appropriate inputs, and aggregating estimates, and they illustrate the framework with three case studies: auditing gender bias in generative image models, measuring gender disparities in film, and imputing gender from names. The paper is conceptual; it provides no empirical validation of the central validity claim.

Significance. If the argument succeeds, the paper makes a valuable contribution by giving researchers a vocabulary to reconcile two positions that are often treated as contradictory: the ethical rejection of gender prediction and the practical need for demographic labels in disparity auditing. The distinction between legitimacy and validity, and the mapping of these concepts onto traditional versus oppositional sexism, productively clarifies a real conflation in the literature. The recommendations—use narrowly scoped perceived-gender constructs, train on directly collected perceived-gender data, aggregate rather than individualize, and use continuous estimators—are concrete and actionable. The paper is careful, well-referenced, and includes thoughtful positionality, ethical, and adverse-impact statements. Its main weakness is that the positive claim "can yield valid measurements" is asserted through illustrative examples and case-study commentary rather than demonstrated through any empirical validity check or fully specified measurement conditions. The contribution is therefore best understood as a taxonomy and research agenda; the title's promise of valid measurements remains conditional.

major comments (3)
  1. [§4.2, footnote 9; §5.2] The positive validity claim is conditional on an unverified alignment between imputation outputs and the perceptions of the discriminators whose behavior constitutes the disparity. The paper asserts that traditional sexism is triggered by perceived gender and social position, and that imputation can capture these constructs (e.g., the catcalling example in §4.2), but it offers no empirical evidence that any imputation model's predicted labels coincide with the perceptions of actual decision-makers in a given setting. The authors' own footnote 9 ("perceived by whom?") and their recommendation in §5.2 to collect perceived-gender data by surveying recruiters concede that this alignment is an open empirical question. Because the title claims imputation "can yield valid measurements," this evidence—or an explicit reformulation of the claim as a conditional feasibility argument—is needed to support the central thesis.
  2. [§4.2] The relation between trans-misogyny and the two-sexism distinction is under-specified. The authors state that imputation can help address "traditional sexism, including instances of trans-misogynistic discrimination," but trans-misogyny, under the Serano account they adopt, is a compound of traditional sexism and oppositional sexism. If imputation is illegitimate and unsuitable for measuring oppositional sexism, the reader needs to know whether the disparity estimate captures only the traditional-sexism component of trans-misogyny and whether such a partial measurement is still valid or is instead liable to mislead. This ambiguity affects the scope of the central claim and should be resolved explicitly.
  3. [§4.1, §4.3] The notion of "validity" used for disparity measurements is not operationalized sufficiently to assess the title's claim. The paper invokes content, convergent, and consequential validity, but it does not state what evidence would establish that an imputed-gender disparity measure is valid, nor under what measurement-error conditions it fails. For example, the paper could specify a formal condition such as the imputation being nondifferential with respect to the disparity construct, or provide a concrete validation procedure such as comparing imputed-perceived-gender disparities to audit-study benchmarks or to self-reported perceived-gender data. Without this, "valid" remains ambiguous between a philosophical claim and a statistical one, and the second half of the paper depends on that distinction.
minor comments (5)
  1. [Abstract] The abstract contains a typo: "predicting gender iswrong" should read "predicting gender is wrong."
  2. [§4.2] The phrase "she coinstraditional sexismto refer" is missing spaces and should read "she coins 'traditional sexism' to refer."
  3. [References and §6.3] The author name "V ogel" appears with an extra space in the text and in the reference list; it should be "Vogel."
  4. [§5.2] The sentence about Spanish versus English-language Chinese name datasets is difficult to parse; consider rewriting to clarify the intended contrast between name-gender associations across linguistic populations.
  5. [Appendix C, Table 3] The table classifies "using self-reported gender identity to measure online recruiter discrimination" as invalid, which is correct but likely surprising to readers; a brief explanation in the main text or table caption would help clarify that the invalidity stems from a construct mismatch, not from the data source.

Circularity Check

1 steps flagged · score 2.0 of 10

No significant circularity: the paper's central validity claim is partly analytic but explicitly conditional, and self-citations are not load-bearing.

  1. self definitional [Sec. 4, opening paragraph (and Sec. 4.2 conclusion)]
    "Moreover, we argue that some forms of discrimination are inherently built on different aspects of gender (e.g., those against the perceived gender of individuals), and can by definition be imputed and inferred."

    The validity half of the thesis is produced by setting the systematized concept to 'perceived gender': once the discrimination being measured is defined as targeting perceived gender, a measurement that infers perceived gender is valid by construction under the paper's content-validity criterion. The conclusion 'disparity measurements using gender imputation ... can validly systematize traditional sexism' thus restates the adopted construct definition plus the empirical premise that traditional sexism tracks perceived gender. The paper mitigates this by acknowledging the open question 'perceived by whom?' and by requiring in Sec. 5.2 that imputation models be trained and validated on directly collected perceived-gender data, so the claim is conditional rather than a forced derivation.

full rationale

The paper is largely self-contained against external frameworks: the legitimacy/validity distinction is built on Adcock and Collier, Messick, Cronbach and Meehl, and the traditional/oppositional sexism distinction is taken from Serano. Self-citations occur (Wallach et al. 2025, co-authored by Wang, for measurement evaluation; Dong et al. 2025 for continuous-output disparity estimators; Wang 2025 for gender-specific harms), but none is load-bearing: the measurement framework is independently sourced from Adcock and Collier and Messick, the continuous-output statistical claim is also supported by Chen et al. 2019, and the harms citation is one of several. There is no imported uniqueness theorem, no ansatz smuggled in by citation, and no fitted parameter renamed as a prediction. The only definitional reduction is the analytic step described above: if the construct of interest is defined as perceived gender, then imputation of perceived gender is valid with respect to that construct by definition. The paper does not hide this conditionality; it explicitly warns that naïve imputation 'will almost necessarily be invalid' and recommends collecting perceived-gender data for training and validation. The skeptical concern that alignment between imputation outputs and actual discriminators' perceptions is unverified is an external-validity limitation, not a circularity, and the paper itself flags the 'perceived by whom?' question. Overall, the derivation does not reduce to its inputs; it offers a conditional reconciliation with independent normative content.

Assumptions & free parameters 0 free parameters · 6 assumptions · 0 invented entities

No free parameters or invented entities. The paper is a conceptual argument, so the load is carried by normative and methodological assumptions: a self-determination-based definition of legitimacy, Serano's two-sexism taxonomy, measurement validity theory, the Belmont beneficence framework, and an empirical premise that traditional sexism tracks perceived gender. These are explicitly sourced and stated, but none are empirically established in the paper.

assumptions (6)
  • domain assumption Serano's distinction between traditional sexism (targeting women/femininity) and oppositional sexism (targeting gender deviance) is the right analysis of sexism.
    Invoked in Sec. 4.2 as the basis for distinguishing what imputation can and cannot measure. If this distinction is wrong or incomplete, the central validity claim loses its foundation.
  • ad hoc to paper Legitimacy is defined by restriction of gender self-determination; all gender prediction restricts it.
    This is the paper's own normative criterion, introduced in Sec. 3.2. The conclusion that every gender prediction is illegitimate follows substantially from this chosen definition, so it is a load-bearing assumption, not an empirical finding.
  • domain assumption Adcock and Collier's and Messick's frameworks of measurement validity (content, convergent, consequential) apply to gender-disparity measurement.
    Adopted in Sec. 4.1; the whole validity half of the argument is built on this methodology being appropriate.
  • domain assumption Traditional sexism operates on perceived gender and social position, which are the same constructs imputation can estimate.
    Stated in Sec. 4.2 through the catcalling example; this is the empirical bridge that makes imputation validity possible but is not empirically demonstrated.
  • domain assumption The Belmont Report's beneficence principle is the appropriate ethical framework for weighing harms and benefits of imputation.
    Used in Sec. 5.1 to ground the recommendation that imputation is justified only when benefits cannot be achieved otherwise. This is a contested normative choice.
  • domain assumption Gender is a social construct with multiple aspects (identity, perceived, expression, social position).
    Adopted in Sec. 3.1 from trans studies and feminist theory; the argument that imputation can validly measure perceived gender requires this pluralist ontology.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Algorithmic Gender Prediction Is Illegitimate, But Gender Imputation Can Yield Valid Measurements." pith.science (2026). https://pith.science/paper/XAPHMNNJ

@misc{pith2026260813444,
  author       = {Pith},
  title        = {Pith review of: Algorithmic Gender Prediction Is Illegitimate, But Gender Imputation Can Yield Valid Measurements},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/XAPHMNNJ}},
  note         = {Machine review of arXiv:2608.13444}
}
read the original abstract

Machine learning ethics researchers and critical HCI scholars have argued that algorithmically predicting gender is wrong. At the same time, other researchers rely on predicted gender labels to study gender disparities and develop algorithmic fairness techniques. How do we reconcile these two seemingly contradictory intuitions? We differentiate two ways gender prediction may be wrong: being illegitimate, thereby contributing to harm; and being invalid, thereby producing unusable measurements. Our analysis translates arguments against gender prediction into these terms of legitimacy and validity and shows how gender imputation applied for fairness purposes can be illegitimate yet still yield valid disparity measurements. We clarify this bind by drawing upon transfeminist literature to distinguish sexism that targets women and femininity from sexism that targets transgender and nonbinary people. While gender imputation can produce valid measurements for the former, it is illegitimate and harmful for the latter. We argue that practitioners should deploy gender imputation only when it would achieve anti-discrimination benefits that cannot be achieved through other reasonable means, while harms are minimized to the extent possible. We examine this tension in three case studies: auditing gender bias in generative image models, measuring gender disparities in film, and imputing gender from personal names. By disentangling legitimacy from validity, and differentiating these two forms of sexism, we show how debates over gender prediction have conflated distinct concerns, obscuring both the settings in which gender imputation can support fairness efforts and the harms towards transgender and nonbinary people that it fundamentally cannot capture. We conclude by recommending the development of more inclusive methods that address all kinds of sexism.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

209 extracted references · 64 canonical work pages

  1. [1]

    PNAS nexus , volume=

    Addressing discretization-induced bias in demographic prediction , author=. PNAS nexus , volume=. 2025 , publisher=

    2025

  2. [2]

    Proceedings of the ACM on human-computer interaction , volume=

    The misgendering machines: Trans/HCI implications of automatic gender recognition , author=. Proceedings of the ACM on human-computer interaction , volume=. 2018 , publisher=

    2018

  3. [3]

    Polity , volume=

    The concept of legitimacy , author=. Polity , volume=. 1974 , publisher=

    1974

  4. [4]

    International Relations , volume=

    On legitimacy , author=. International Relations , volume=. 2006 , publisher=

    2006

  5. [5]

    Canadian Journal of Philosophy , volume=

    The concept of legitimacy , author=. Canadian Journal of Philosophy , volume=. 2022 , publisher=

    2022

  6. [6]

    Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems , pages=

    Exploring a makeup support system for transgender passing based on automatic gender recognition , author=. Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems , pages=

    2021

  7. [7]

    2020 , month=

    Gender Classification and bias mitigation: A post-publication Review , author=. 2020 , month=

    2020

  8. [8]

    Proceedings of the 12th ACM Conference on Web Science , pages=

    Gender classification and bias mitigation in facial images , author=. Proceedings of the 12th ACM Conference on Web Science , pages=

Show all 209 references
  1. [9]

    2019 , month=

    Counting the countless , author=. 2019 , month=

    2019

  2. [10]

    1999 , publisher=

    The social construction of what? , author=. 1999 , publisher=

    1999

  3. [11]

    Sociologia, problemas e pr

    Towards a realist social constructionism , author=. Sociologia, problemas e pr. 2012 , publisher=

    2012

  4. [12]

    Sex in question: French materialist feminism , pages=

    The category of sex , author=. Sex in question: French materialist feminism , pages=. 1996 , publisher=

    1996

  5. [13]

    The psychology of legitimacy: Emerging perspectives on ideology, justice, and intergroup relations , volume=

    Theories of legitimacy , author=. The psychology of legitimacy: Emerging perspectives on ideology, justice, and intergroup relations , volume=

  6. [14]

    Philosophical Studies , pages=

    Does calibration mean what they say it means; or, the reference class problem rises again , author=. Philosophical Studies , pages=. 2025 , publisher=

    2025

  7. [15]

    It’s complicated

    “It’s complicated”: Negotiating accessibility and (mis) representation in image descriptions of race, gender, and disability , author=. Proceedings of the 2021 chi conference on human factors in computing systems , pages=

    2021

  8. [16]

    , author=

    Convergent and discriminant validation by the multitrait-multimethod matrix. , author=. Psychological bulletin , volume=. 1959 , publisher=

    1959

  9. [17]

    Organizational research methods , volume=

    An updated guideline for assessing discriminant validity , author=. Organizational research methods , volume=. 2022 , publisher=

    2022

  10. [18]

    San Diego L

    What is wrongful discrimination , author=. San Diego L. Rev. , volume=. 2006 , publisher=

    2006

  11. [19]

    Conference on fairness, accountability and transparency , pages=

    Gender shades: Intersectional accuracy disparities in commercial gender classification , author=. Conference on fairness, accountability and transparency , pages=. 2018 , organization=

    2018

  12. [20]

    I’m fully who I am

    “I’m fully who I am”: Towards centering transgender and non-binary voices to measure biases in open language generation , author=. Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency , pages=

    2023

  13. [21]

    San Diego L

    Justice for Large Earlobes-A Comment on Richard Arneson's What Is Wrongful Discrimination , author=. San Diego L. Rev. , volume=. 2006 , publisher=

    2006

  14. [22]

    PeerJ Computer Science , volume=

    Comparison and benchmark of name-to-gender inference services , author=. PeerJ Computer Science , volume=. 2018 , publisher=

    2018

  15. [23]

    Data Mining and Knowledge Discovery , volume=

    What’s in a name?--gender classification of names with character based machine learning models , author=. Data Mining and Knowledge Discovery , volume=. 2021 , publisher=

    2021

  16. [24]

    International Conference on Database Systems for Advanced Applications , pages=

    What’s in a name: A study of names, gender inference, and gender behavior in facebook , author=. International Conference on Database Systems for Advanced Applications , pages=. 2011 , organization=

    2011

  17. [25]

    Journal of the Medical Library Association: JMLA , volume=

    Performance of gender detection tools: a comparative study of name-to-gender inference services , author=. Journal of the Medical Library Association: JMLA , volume=

  18. [26]

    Proceedings of the 25th International conference companion on World Wide Web , pages=

    Inferring gender from names on the web: A comparative evaluation of gender detection methods , author=. Proceedings of the 25th International conference companion on World Wide Web , pages=

  19. [27]

    Oxford studies in political philosophy , volume=

    Legitimacy, authority, and democratic , author=. Oxford studies in political philosophy , volume=

  20. [28]

    ACM Journal on Responsible Computing , year=

    Legitimate Power, Illegitimate Automation: The problem of ignoring legitimacy in automated decision systems , author=. ACM Journal on Responsible Computing , year=

  21. [29]

    2024 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML) , pages=

    Estimating and implementing conventional fairness metrics with probabilistic protected features , author=. 2024 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML) , pages=. 2024 , organization=

    2024

  22. [30]

    Ethics , volume=

    Amelioration and inclusion: Gender identity and the concept of woman , author=. Ethics , volume=. 2016 , publisher=

    2016

  23. [31]

    Arguing about science , pages=

    Gender and Race:(What) Are They?(What) Do We Want Them to Be? 1 , author=. Arguing about science , pages=. 2012 , publisher=

    2012

  24. [32]

    Advances in neural information processing systems , volume=

    Robust optimization for fairness with noisy protected groups , author=. Advances in neural information processing systems , volume=

  25. [33]

    2021 , issue_date =

    Yee, Kyra and Tantipongpipat, Uthaipon and Mishra, Shubhanshu , title =. 2021 , issue_date =. doi:10.1145/3479594 , journal =

    2021 doi

  26. [34]

    Proceedings of the ACM on Human-Computer Interaction , volume=

    How computers see gender: An evaluation of gender classification in commercial facial analysis services , author=. Proceedings of the ACM on Human-Computer Interaction , volume=. 2019 , publisher=

    2019

  27. [35]

    Proceedings of the ACM on human-computer interaction , volume=

    You keep using that word: Ways of thinking about gender in computing research , author=. Proceedings of the ACM on human-computer interaction , volume=. 2021 , publisher=

    2021

  28. [36]

    European Journal for Philosophy of Science , volume=

    The predictive reframing of machine learning applications: good predictions and bad measurements , author=. European Journal for Philosophy of Science , volume=. 2022 , publisher=

    2022

  29. [37]

    New media & society , volume=

    Nipples, memes, and algorithmic failure: NSFW critique of Tumblr censorship , author=. New media & society , volume=. 2022 , publisher=

    2022

  30. [38]

    Social Development , volume=

    Relations among gender typicality, peer relations, and mental health during early adolescence , author=. Social Development , volume=. 2014 , publisher=

    2014

  31. [39]

    2020 , publisher=

    Intersex , author=. 2020 , publisher=

    2020

  32. [40]

    XRDS: Crossroads, The ACM Magazine for Students , volume=

    Facial recognition is the plutonium of AI , author=. XRDS: Crossroads, The ACM Magazine for Students , volume=. 2019 , publisher=

    2019

  33. [41]

    Ergo , volume=

    Toward an account of gender identity , author=. Ergo , volume=. 2018 , publisher=

    2018

  34. [42]

    The Belmont Report: Ethical Principles and Guidelines for the Protection of Human Subjects of Research , year =

  35. [43]

    2025 , institution=

    Racial Inequality in France , author=. 2025 , institution=

    2025

  36. [44]

    Social Statistics and Ethnic Diversity: Cross-National Perspectives in Classifications and Identity Politics , pages=

    The choice of ignorance: The debate on ethnic and racial statistics in France , author=. Social Statistics and Ethnic Diversity: Cross-National Perspectives in Classifications and Identity Politics , pages=. 2015 , publisher=

    2015

  37. [45]

    European multiculturalisms: Cultural, religious, and ethnic challenges , pages=

    Ethnic statistics in Europe: The paradox of colorblindness , author=. European multiculturalisms: Cultural, religious, and ethnic challenges , pages=. 2011 , publisher=

    2011

  38. [46]

    , author=

    A minority stress perspective on transgender individuals’ experiences with misgendering. , author=. Stigma and Health , volume=. 2018 , publisher=

    2018

  39. [47]

    The default is just going to be getting misgendered

    “The default is just going to be getting misgendered”: Minority stress experiences among nonbinary adults. , author=. Psychology of Sexual Orientation and Gender Diversity , volume=. 2024 , publisher=

    2024

  40. [48]

    Technology and culture , volume=

    When computers were women , author=. Technology and culture , volume=. 1999 , publisher=

    1999

  41. [49]

    2012 , publisher=

    Recoding gender: Women's changing participation in computing , author=. 2012 , publisher=

    2012

  42. [50]

    , author=

    Gender diagnosticity: A new Bayesian approach to gender-related individual differences. , author=. Journal of Personality and Social Psychology , volume=. 1990 , publisher=

    1990

  43. [51]

    Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society , volume=

    Observing Context Improves Disparity Estimation when Race is Unobserved , author=. Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society , volume=

  44. [52]

    Yale Law Journal , pages=

    Disaggregating gender from sex and sexual orientation: The effeminate man in the law and feminist jurisprudence , author=. Yale Law Journal , pages=. 1995 , publisher=

    1995

  45. [53]

    Synthese , volume=

    Gender and first-person authority , author=. Synthese , volume=. 2023 , publisher=

    2023

  46. [54]

    Communications of the ACM , volume=

    Discrimination in online ad delivery , author=. Communications of the ACM , volume=. 2013 , publisher=

    2013

  47. [55]

    ACM SIGACCESS accessibility and computing , number=

    What is the point of fairness? Disability, AI and the complexity of justice , author=. ACM SIGACCESS accessibility and computing , number=. 2020 , publisher=

    2020

  48. [56]

    You’ve changed: sex reassignment and personal identity , volume=

    Trans identities and first-person authority , author=. You’ve changed: sex reassignment and personal identity , volume=. 2009 , publisher=

    2009

  49. [57]

    Big Data & Society , volume=

    Data for queer lives: How LGBTQ gender and sexuality identities challenge norms of demographics , author=. Big Data & Society , volume=. 2020 , publisher=

    2020

  50. [58]

    Berkeley Technology Law Journal , volume=

    Affinity profiling and discrimination by association in online behavioral advertising , author=. Berkeley Technology Law Journal , volume=. 2020 , publisher=

    2020

  51. [59]

    Proceedings of the 2022 ACM conference on fairness, accountability, and transparency , pages=

    Theories of “gender” in nlp bias research , author=. Proceedings of the 2022 ACM conference on fairness, accountability, and transparency , pages=

    2022

  52. [60]

    The gender panopticon: AI, gender, and design Justice , author=. UCLA L. Rev. , volume=. 2021 , publisher=

    2021

  53. [61]

    2018 , publisher=

    The Turnout Gap: Race, Ethnicity, and Political Inequality in a Diversifying America , author=. 2018 , publisher=

    2018

  54. [62]

    2015 , publisher=

    Hacking the electorate: How campaigns perceive Voters , author=. 2015 , publisher=

    2015

  55. [63]

    Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency , pages=

    Who Audits the Auditors? Recommendations from a Field Scan of the Algorithmic Auditing Ecosystem , author=. Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency , pages=

    2022

  56. [64]

    The Twelfth International Conference on Learning Representations , year=

    Domain Constraints Improve Risk Prediction when Outcome Data is Missing , author=. The Twelfth International Conference on Learning Representations , year=

  57. [65]

    Machine Learning for Healthcare (ML4HC) , year=

    Coarse Race Data Conceals Disparities in Clinical Risk Score Performance , author=. Machine Learning for Healthcare (ML4HC) , year=

  58. [66]

    Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency , pages=

    Towards a Critical Race Methodology in Algorithmic Fairness , author=. Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency , pages=

    2020

  59. [67]

    Proceedings of the National Academy of Sciences , volume=

    Word Embeddings Quantify 100 Years of Gender and Ethnic Stereotypes , author=. Proceedings of the National Academy of Sciences , volume=. 2018 , publisher=

    2018

  60. [68]

    Nature Human Behaviour , volume=

    A Large-Scale Analysis of Racial Disparities in Police Stops across the United States , author=. Nature Human Behaviour , volume=. 2020 , publisher=

    2020

  61. [69]

    Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) , month =

    Birhane, Abeba and Prabhu, Vinay Uday and Whaley, John , title =. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) , month =. 2022 , pages =

    2022

  62. [70]

    arXiv preprint arXiv:2201.11706 , year=

    A Systematic Study of Bias Amplification , author=. arXiv preprint arXiv:2201.11706 , year=

    arXiv

  63. [71]

    Proceedings of the 29th ACM international conference on information & knowledge management , pages=

    Feedback Loop and Bias Amplification in Recommender Systems , author=. Proceedings of the 29th ACM international conference on information & knowledge management , pages=

  64. [72]

    Proceedings of the 40th International Conference on Machine Learning , pages =

    Men also do Laundry: Multi-Attribute Bias Amplification , author =. Proceedings of the 40th International Conference on Machine Learning , pages =. 2023 , editor =

    2023

  65. [73]

    Proceedings of the 3rd innovations in theoretical computer science conference , pages=

    Fairness through awareness , author=. Proceedings of the 3rd innovations in theoretical computer science conference , pages=

  66. [74]

    2024 , publisher=

    Argyle, Lisa P and Barber, Michael , journal=. 2024 , publisher=

    2024

  67. [75]

    Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency , pages=

    The privacy-bias tradeoff: Data minimization and racial disparity assessments in US government , author=. Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency , pages=

    2023

  68. [76]

    Countering Privacy Nihilism , author=

  69. [77]

    American political science review , volume=

    Measurement validity: A shared standard for qualitative and quantitative research , author=. American political science review , volume=. 2001 , publisher=

    2001

  70. [78]

    arXiv preprint arXiv:2502.00561 , year=

    Position: Evaluating generative ai systems is a social science measurement challenge , author=. arXiv preprint arXiv:2502.00561 , year=

    arXiv

  71. [79]

    Science Advances , volume=

    Testing for racial bias using inconsistent perceptions of race , author=. Science Advances , volume=. 2025 , publisher=

    2025

  72. [80]

    Proceedings of the Aristotelian society , volume=

    Essentially contested concepts , author=. Proceedings of the Aristotelian society , volume=. 1955 , organization=

    1955

  73. [81]

    Proceedings of the 2021 ACM conference on fairness, accountability, and transparency , pages=

    Measurement and fairness , author=. Proceedings of the 2021 ACM conference on fairness, accountability, and transparency , pages=

    2021

  74. [82]

    boyd, danah, & Sarathy, Jayshree.(2022)

    Differential perspectives: Epistemic disconnects surrounding the US Census Bureau’s use of differential privacy , author=. boyd, danah, & Sarathy, Jayshree.(2022). Differential Perspectives: Epistemic Disconnects Surrounding the US Census Bureau’s Use of Differential Privacy. ...

    2022 doi

  75. [83]

    Critical Sociology , volume=

    Statistical imaginaries, state legitimacy: grappling with the arrangements underpinning quantification in the US census , author=. Critical Sociology , volume=. 2025 , publisher=

    2025

  76. [84]

    New Media & Society , volume=

    Terms of inclusion: Data, discourse, violence , author=. New Media & Society , volume=. 2021 , publisher=

    2021

  77. [85]

    Proceedings of the 12th ACM Conference on Recommender Systems , pages=

    Calibrated Recommendations , author=. Proceedings of the 12th ACM Conference on Recommender Systems , pages=

  78. [86]

    arXiv preprint arXiv:2005.06251 , year=

    Mitigating Gender Bias Amplification in Distribution by Posterior Regularization , author=. arXiv preprint arXiv:2005.06251 , year=

    2005 arXiv

  79. [87]

    Advances in Neural Information Processing Systems , volume=

    Why is My Classifier Discriminatory? , author=. Advances in Neural Information Processing Systems , volume=

  80. [88]

    Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society , pages=

    Fair Allocation through Selective Information Acquisition , author=. Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society , pages=

  81. [89]

    Proceedings of the 3rd ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization , pages=

    Toward Operationalizing Pipeline-Aware ML Fairness: a Research Agenda for Developing Practical Guidelines and Tools , author=. Proceedings of the 3rd ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization , pages=

  82. [90]

    International Conference on Learning Representations (ICLR) , year=

    Feature-Wise Bias Amplification , author=. International Conference on Learning Representations (ICLR) , year=

  83. [91]

    ACM Computing Surveys , year=

    Fairness in Machine Learning: A survey , author=. ACM Computing Surveys , year=

  84. [92]

    Journal of Machine Learning Research , volume=

    From predictive methods to missing data imputation: an optimization approach , author=. Journal of Machine Learning Research , volume=

  85. [93]

    Ets research report series , volume=

    Foundations of validity: Meaning and consequences in psychological assessment , author=. Ets research report series , volume=. 1993 , publisher=

    1993

  86. [94]

    Statistical methods in medical research , volume=

    A comparison of imputation techniques for handling missing predictor values in a risk model with a binary outcome , author=. Statistical methods in medical research , volume=. 2007 , publisher=

    2007

  87. [95]

    Medical Care Research and Review , volume=

    Race and ethnicity data quality and imputation using US Census data in an integrated health system: the Kaiser Permanente Southern California experience , author=. Medical Care Research and Review , volume=. 2013 , publisher=

    2013

  88. [96]

    Health services research , volume=

    Imputing missing race/ethnicity in pediatric electronic health records: reducing bias with use of US census location and surname data , author=. Health services research , volume=. 2015 , publisher=

    2015

  89. [97]

    , title =

    Seymen, Sinan and Abdollahpouri, Himan and Malthouse, Edward C. , title =. 2021 , isbn =. doi:10.1145/3460231.3478857 , booktitle =

    2021

  90. [98]

    2023 , isbn =

    Abdollahpouri, Himan and Nazari, Zahra and Gain, Alex and Gibson, Clay and Dimakopoulou, Maria and Anderton, Jesse and Carterette, Benjamin and Lalmas, Mounia and Jebara, Tony , title =. 2023 , isbn =. doi:10.1145/3539597.3570402 , booktitle =

    2023

  91. [99]

    Men Also like Shopping: Reducing Gender Bias Amplification Using Corpus-Level Constraints

    Zhao, Jieyu and Wang, Tianlu and Yatskar, Mark and Ordonez, Vicente and Chang, Kai-Wei. Men Also like Shopping: Reducing Gender Bias Amplification Using Corpus-Level Constraints. Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. 2017. doi:...

    2017 doi

  92. [100]

    International Conference on Machine Learning , pages=

    Directional Bias Amplification , author=. International Conference on Machine Learning , pages=. 2021 , organization=

    2021

  93. [101]

    and Zettlemoyer, Luke

    Stanovsky, Gabriel and Smith, Noah A. and Zettlemoyer, Luke. Evaluating Gender Bias in Machine Translation. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 2019. doi:10.18653/v1/P19-1164

    2019 doi

  94. [102]

    Protein Science , volume=

    Randomized Gates Eliminate Bias in Sort-Seq Assays , author=. Protein Science , volume=. 2022 , publisher=

    2022

  95. [103]

    Proceedings of the European conference on computer vision (ECCV) , pages=

    Women Also Snowboard: Overcoming Bias in Captioning Models , author=. Proceedings of the European conference on computer vision (ECCV) , pages=

  96. [104]

    and Garcia-Bulle, Bernardo and Pentland, Alex 'Sandy' , title =

    Noriega-Campero, Alejandro and Bakker, Michiel A. and Garcia-Bulle, Bernardo and Pentland, Alex 'Sandy' , title =. 2019 , isbn =. doi:10.1145/3306618.3314277 , booktitle =

    2019

  97. [105]

    2023 , eprint=

    Classifier Calibration: A Survey on How to Assess and Improve Predicted Class Probabilities , author=. 2023 , eprint=

    2023

  98. [106]

    2020 , eprint=

    The Curious Case of Neural Text Degeneration , author=. 2020 , eprint=

    2020

  99. [107]

    Health Services and Outcomes Research Methodology , volume=

    Using the Census Bureau’s Surname List to Improve Estimates of Race/Ethnicity and Associated Disparities , author=. Health Services and Outcomes Research Methodology , volume=. 2009 , publisher=

    2009

  100. [108]

    Washington, DC: CFPB, Summer , year=

    Using Publicly Available Information to Proxy for Unidentified Race and Ethnicity: A Methodology and Assessment , author=. Washington, DC: CFPB, Summer , year=

  101. [109]

    Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics , pages=

    Language (Technology) is Power: A Critical Survey of “Bias” in NLP , author=. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics , pages=

  102. [110]

    Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency , pages=

    What We Can't Measure, We Can't Understand: Challenges to Demographic Data Procurement in the Pursuit of Fairness , author=. Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency , pages=

    2021

  103. [111]

    arXiv preprint arXiv:2303.02580 , year=

    Estimating Racial Disparities when Race is Not Observed , author=. arXiv preprint arXiv:2303.02580 , year=

  104. [112]

    2020 , publisher=

    Design justice: Community-led practices to build the worlds we need , author=. 2020 , publisher=

    2020

  105. [113]

    Proceedings of the ACM on Human-computer Interaction , volume=

    How we've taught algorithms to see identity: Constructing race and gender in image databases for facial analysis , author=. Proceedings of the ACM on Human-computer Interaction , volume=. 2020 , publisher=

    2020

  106. [114]

    Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency , pages=

    Identities are not Interchangeable: The Problem of Overgeneralization in Fair Machine Learning , author=. Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency , pages=

    2025

  107. [115]

    Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics , pages=

    Gender gap in natural language processing research: Disparities in authorship and citations , author=. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics , pages=

  108. [116]

    Advances in Neural Information Processing Systems , volume=

    Stable bias: Evaluating societal representations in diffusion models , author=. Advances in Neural Information Processing Systems , volume=

  109. [117]

    arXiv preprint arXiv:2404.01030 , year=

    Survey of bias in text-to-image generation: Definition, evaluation, and mitigation , author=. arXiv preprint arXiv:2404.01030 , year=

    arXiv

  110. [118]

    Proceedings of the IEEE/CVF international conference on computer vision , pages=

    Understanding and evaluating racial biases in image captioning , author=. Proceedings of the IEEE/CVF international conference on computer vision , pages=

  111. [119]

    Eddie Murphy and the dangers of counterfactual causal thinking about detecting racial discrimination , author=. Nw. UL Rev. , volume=. 2018 , publisher=

    2018

  112. [120]

    arXiv preprint arXiv:2006.01770 , year=

    What's sex got to do with fair machine learning? , author=. arXiv preprint arXiv:2006.01770 , year=

    2006 arXiv

  113. [121]

    Journal of Moral Philosophy , volume=

    What is “race” in algorithmic discrimination on the basis of race? , author=. Journal of Moral Philosophy , volume=. 2023 , publisher=

    2023

  114. [122]

    Law & Society Review , volume=

    What is perceived when race is perceived and why it matters for causal inference and discrimination studies , author=. Law & Society Review , volume=. 2025 , publisher=

    2025

  115. [123]

    Political Philosophy , volume=

    Sex Discrimination, Normativity, and Begging the Causal Question , author=. Political Philosophy , volume=. 2025 , publisher=

    2025

  116. [124]

    Big Data & Society , volume=

    Auto-essentialization: Gender in automated facial analysis as extended colonial project , author=. Big Data & Society , volume=. 2021 , publisher=

    2021

  117. [125]

    University of Pennsylvania Law Review , volume=

    What makes wrongful discrimination wrong? Biases, preferences, stereotypes, and proxies , author=. University of Pennsylvania Law Review , volume=. 1992 , publisher=

    1992

  118. [126]

    2008 , publisher=

    When is discrimination wrong? , author=. 2008 , publisher=

    2008

  119. [127]

    1990 , publisher=

    The realm of rights , author=. 1990 , publisher=

    1990

  120. [128]

    , author=

    Construct validity in psychological tests. , author=. Psychological bulletin , volume=. 1955 , publisher=

    1955

  121. [129]

    The Stanford Encyclopedia of Philosophy , editor=

    Political Legitimacy , author=. The Stanford Encyclopedia of Philosophy , editor=. 2023 , month=

    2023

  122. [130]

    2007 , publisher=

    Whipping girl: A transsexual woman on sexism and the scapegoating of femininity , author=. 2007 , publisher=

    2007

  123. [131]

    2013 , publisher=

    The legitimation of power , author=. 2013 , publisher=

    2013

  124. [132]

    From max weber: Essays in Sociology , pages=

    Politics as a Vocation , author=. From max weber: Essays in Sociology , pages=. 1946 , publisher=

    1946

  125. [133]

    American economic review , volume=

    Are Emily and Greg more employable than Lakisha and Jamal? A field experiment on labor market discrimination , author=. American economic review , volume=. 2004 , publisher=

    2004

  126. [134]

    evans , author=

    The use of statistical methods in the us census: Utah v. evans , author=. The American Statistician , volume=. 2004 , publisher=

    2004

  127. [135]

    Proceedings of the IEEE/CVF international conference on computer vision , pages=

    Facet: Fairness in computer vision evaluation benchmark , author=. Proceedings of the IEEE/CVF international conference on computer vision , pages=

  128. [136]

    Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society , pages=

    A step toward more inclusive people annotations for fairness , author=. Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society , pages=

    2021

  129. [137]

    Social theory re-wired , pages=

    The presentation of self in everyday life , author=. Social theory re-wired , pages=. 2023 , publisher=

    2023

  130. [138]

    The transgender studies reader , pages=

    (De) subjugated knowledges: An introduction to transgender studies , author=. The transgender studies reader , pages=. 2013 , publisher=

    2013

  131. [139]

    Gender & society , volume=

    Doing gender , author=. Gender & society , volume=. 1987 , publisher=

    1987

  132. [140]

    2015 , publisher=

    Normal life: Administrative violence, critical trans politics, and the limits of law , author=. 2015 , publisher=

    2015

  133. [141]

    Ergo an Open Access Journal of Philosophy , volume=

    Telling gender: The pragmatics and ethics of gender ascriptions , author=. Ergo an Open Access Journal of Philosophy , volume=. 2023 , publisher=

    2023

  134. [142]

    1967 , publisher=

    Strategy for labor: A radical proposal , author=. 1967 , publisher=

    1967

  135. [143]

    2nd international conference of the European association for digital humanities (EADH 2021) , year=

    Exploring computer vision for film analysis: A case study for five canonical movies , author=. 2nd international conference of the European association for digital humanities (EADH 2021) , year=

    2021

  136. [144]

    Adjudicating Identity , author=. Tex. A&M L. Rev. , volume=. 2021 , publisher=

    2021

  137. [145]

    Central mistake of sex discrimination law: The disaggregation of sex from gender , author=. U. Pa. L. Rev. , volume=. 1995 , publisher=

    1995

  138. [146]

    Proceedings of the ACM on Human-Computer Interaction , volume=

    Quantification of gender representation bias in commercial films based on image analysis , author=. Proceedings of the ACM on Human-Computer Interaction , volume=. 2019 , publisher=

    2019

  139. [147]

    Hypatia , volume=

    Misgendering and its moral contestability , author=. Hypatia , volume=. 2016 , publisher=

    2016

  140. [148]

    Social Media Users Think So, Too

    Social Media Profiling Continues to Partake in the Development of Formalistic Self-Concepts. Social Media Users Think So, Too. , author=. Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society , pages=

    2022

  141. [149]

    2023 , school=

    Understanding the Legitimacy of Digital Socio-Technical Classification Systems , author=. 2023 , school=

    2023

  142. [150]

    Academy of management review , volume=

    Managing legitimacy: Strategic and institutional approaches , author=. Academy of management review , volume=. 1995 , publisher=

    1995

  143. [151]

    Genderfluid

    " Genderfluid" or" Attack Helicopter" Responsible HCI Research Practice with Non-binary Gender Variation in Online Communities , author=. Proceedings of the 2018 CHI conference on human factors in computing systems , pages=

    2018

  144. [152]

    Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency , pages=

    What people think AI should infer from faces , author=. Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency , pages=

    2022

  145. [153]

    Psychology & Sexuality , volume=

    Him, her, them, or none: Misgendering and degendering of transgender individuals , author=. Psychology & Sexuality , volume=. 2022 , publisher=

    2022

  146. [154]

    New Media & Society , volume=

    The gender binary will not be deprogrammed: Ten years of coding gender on Facebook , author=. New Media & Society , volume=. 2017 , publisher=

    2017

  147. [155]

    Hypatia , volume=

    Are lesbians women? , author=. Hypatia , volume=. 1996 , publisher=

    1996

  148. [156]

    Proceedings of the ACL-2012 Special Workshop on Rediscovering 50 Years of Discoveries , pages=

    He said, she said: Gender in the ACL anthology , author=. Proceedings of the ACL-2012 Special Workshop on Rediscovering 50 Years of Discoveries , pages=

    2012

  149. [157]

    2000 , publisher=

    Sorting things out: Classification and its consequences , author=. 2000 , publisher=

    2000

  150. [158]

    2012 , publisher=

    Resisting reality: Social construction and social critique , author=. 2012 , publisher=

    2012

  151. [159]

    Ethics and Information Technology , volume=

    Enacting silence: Residual categories as a challenge for ethics, information systems, and communication , author=. Ethics and Information Technology , volume=. 2007 , publisher=

    2007

  152. [160]

    London: Arnold , year=

    Measurement theory and practice , author=. London: Arnold , year=

  153. [161]

    Gender pay gap in U.S

    Fry, Richard , year=. Gender pay gap in U.S. has narrowed slightly over 2 decades , url=. Pew Research Center , publisher=

  154. [162]

    Proceedings of the 2020 conference on fairness, accountability, and transparency , pages=

    Awareness in practice: tensions in access to sensitive attribute data for antidiscrimination , author=. Proceedings of the 2020 conference on fairness, accountability, and transparency , pages=

    2020

  155. [163]

    Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society , pages=

    Fairness for unobserved characteristics: Insights from technological impacts on queer communities , author=. Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society , pages=

    2021

  156. [164]

    , author=

    Approaches to studying racism and sexism in Black women's lives. , author=. Journal of Social issues , volume=

  157. [165]

    Eliminating racism: Profiles in controversy , pages=

    Racism and sexism: Comparisons and conflicts , author=. Eliminating racism: Profiles in controversy , pages=. 1988 , publisher=

    1988

  158. [166]

    Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society , pages=

    Social biases through the text-to-image generation lens , author=. Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society , pages=

    2023

  159. [167]

    Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society , pages=

    Equalizing credit opportunity in algorithms: Aligning algorithmic fairness research with us fair lending regulation , author=. Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society , pages=

    2022

  160. [168]

    2002 , publisher=

    Gender trouble , author=. 2002 , publisher=

    2002

  161. [169]

    Statistics and Public Policy , volume=

    Using First Name Information to Improve Race and Ethnicity Classification , author=. Statistics and Public Policy , volume=. 2018 , publisher=

    2018

  162. [170]

    2019 , isbn =

    Chen, Jiahao and Kallus, Nathan and Mao, Xiaojie and Svacha, Geoffry and Udell, Madeleine , title =. 2019 , isbn =. doi:10.1145/3287560.3287594 , booktitle =

    2019

  163. [171]

    Health Services Research , pages=

    Evaluating Methods for Imputing Race and Ethnicity in Electronic Health Record Data , author=. Health Services Research , pages=. 2025 , publisher=

    2025

  164. [172]

    Science Advances , volume=

    Addressing Census Data Problems in Race Imputation via Fully Bayesian Improved Surname Geocoding and Name Supplements , author=. Science Advances , volume=. 2022 , publisher=

    2022

  165. [173]

    Health Services Research , volume=

    Use of Geocoding and Surname Analysis to Estimate Race and Ethnicity , author=. Health Services Research , volume=. 2006 , publisher=

    2006

  166. [174]

    Epidemiologic Reviews , pages=

    Methods for Retrospectively Improving Race/Ethnicity Data Quality: A Scoping Review , author=. Epidemiologic Reviews , pages=. 2023 , publisher=

    2023

  167. [175]

    Proceedings of the 44th international ACM SIGIR conference on Research and development in information retrieval , pages=

    When Fair Ranking Meets Uncertain Inference , author=. Proceedings of the 44th international ACM SIGIR conference on Research and development in information retrieval , pages=

  168. [176]

    2022 , pages =

    Validating the Applicability of Bayesian Inference with Surname and Geocoding to Congressional Redistricting , journal =. 2022 , pages =

    2022

  169. [177]

    American Economic Review , volume=

    The Effects of Rent Control Expansion on Tenants, Landlords, and Inequality: Evidence from San Francisco , author=. American Economic Review , volume=. 2019 , publisher=

    2019

  170. [178]

    2023 , eprint=

    Predicting Race and Ethnicity from the Sequence of Characters in a Name , author=. 2023 , eprint=

    2023

  171. [179]

    Management Science , volume=

    Assessing Fair Lending Risks using Race/Ethnicity Proxies , author=. Management Science , volume=. 2018 , publisher=

    2018

  172. [180]

    Health Services Research , volume=

    Using the Bayesian Improved Surname Geocoding Method (BISG) to Create a Working Classification of Race and Ethnicity in a Diverse Managed Care Population: A Validation Study , author=. Health Services Research , volume=. 2014 , publisher=

    2014

  173. [181]

    Public Opinion Quarterly , volume=

    The True Electorate: A Cross-Validation of Voter Registration Files and Election Survey Demographics , author=. Public Opinion Quarterly , volume=. 2007 , publisher=

    2007

  174. [182]

    Political Analysis , volume=

    Voter Registration Databases and MRP: Toward the Use of Large-Scale Databases in Public Opinion Research , author=. Political Analysis , volume=. 2020 , publisher=

    2020

  175. [183]

    Political Analysis , volume=

    Validation: What Big Data Reveal about Survey Misreporting and the Real Electorate , author=. Political Analysis , volume=. 2012 , publisher=

    2012

  176. [184]

    American Journal of Political Science , volume=

    Candidates or Districts? Reevaluating the Role of Race in Voter Turnout , author=. American Journal of Political Science , volume=. 2016 , publisher=

    2016

  177. [185]

    American Political Science Review , volume=

    Race and Representation in Campaign Finance , author=. American Political Science Review , volume=. 2020 , publisher=

    2020

  178. [186]

    The Journal of Politics , volume=

    Redistricting and the Causal Impact of Race on Voter turnout , author=. The Journal of Politics , volume=. 2016 , publisher=

    2016

  179. [187]

    American Journal of Political Science , volume=

    The Primacy of Race in the Geography of Income-based Voting: New Evidence from Public Voting Records , author=. American Journal of Political Science , volume=. 2016 , publisher=

    2016

  180. [188]

    APSA 2011 Annual Meeting Paper , year=

    Gender, Race, Age, and Voting: A Research Note , author=. APSA 2011 Annual Meeting Paper , year=

    2011

  181. [189]

    PLOS ONE , volume=

    400 Million Voting Records Show Profound Racial and Geographic Disparities in Voter Turnout in the United States , author=. PLOS ONE , volume=. 2022 , publisher=

    2022

  182. [190]

    Population Research and Policy Review , volume=

    Disaggregating Heterogeneity Among Non-Hispanic Whites: Evidence and Implications for US Racial/Ethnic Health Disparities , author=. Population Research and Policy Review , volume=. 2021 , publisher=

    2021

  183. [191]

    Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency , pages=

    Towards Intersectionality in Machine Learning: Including More Identities, Handling Underrepresentation, and Performing Evaluation , author=. Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency , pages=

    2022

  184. [192]

    Population Research and Policy Review , volume=

    The Critical Role of Racial/Ethnic Data Disaggregation for Health Equity , author=. Population Research and Policy Review , volume=. 2021 , publisher=

    2021

  185. [193]

    2021 , url =

    Arvind Narayanan , title =. 2021 , url =

    2021

  186. [194]

    2024 , eprint=

    An Improved BISG for Inferring Race from Surname and Geolocation , author=. 2024 , eprint=

    2024

  187. [195]

    2023 , version =

    Greengard, Philip and Gelman, Andrew , publisher =. 2023 , version =. doi:10.7910/DVN/QIM4UF , url =

    2023 doi

  188. [196]

    Journal of Machine Learning Research , year =

    Steven Diamond and Stephen Boyd , title =. Journal of Machine Learning Research , year =

  189. [197]

    Proceedings of the National Academy of Sciences , volume=

    Risk of Being Killed by Police Use of Force in the United States by Age, Race--Ethnicity, and Sex , author=. Proceedings of the National Academy of Sciences , volume=. 2019 , publisher=

    2019

  190. [198]

    arXiv preprint arXiv:2405.17159 , year=

    Stop! in the name of flaws: Disentangling personal names and sociodemographic attributes in NLP , author=. arXiv preprint arXiv:2405.17159 , year=

    arXiv

  191. [199]

    Law & Society Review , volume=

    Stop, Frisk, and Assault? Racial Disparities in Police Use of Force during Investigatory Stops , author=. Law & Society Review , volume=. 2018 , publisher=

    2018

  192. [200]

    Sociological Methods & Research , pages=

    Comparing Methods for estimating Demographics in Racially Polarized Voting Analyses , author=. Sociological Methods & Research , pages=. 2022 , publisher=

    2022

  193. [201]

    Management Science , volume=

    Assessing Algorithmic Fairness with Unobserved Protected Class Using Data Combination , author=. Management Science , volume=. 2022 , publisher=

    2022

  194. [202]

    arXiv preprint arXiv:2206.14583 , year=

    How Should We Proxy for Race/Ethnicity? Comparing Bayesian Improved Surname Geocoding to Machine Learning Methods , author=. arXiv preprint arXiv:2206.14583 , year=

    arXiv

  195. [203]

    2022 , publisher=

    The Importance of Being Ernest, Ekundayo, or Eswari: an Interpretable Machine Learning Approach to Name-Based Ethnicity Classification , author=. 2022 , publisher=

    2022

  196. [204]

    Fair Lending: Implications for the Indirect Auto Finance Market , url=

    Baines, Arthur P and Courchane, Marsha J , year=. Fair Lending: Implications for the Indirect Auto Finance Market , url=

  197. [205]

    Philosophy of Sex: Contemporary Readings , editor=

    Trans women and the meaning of `woman' , author=. Philosophy of Sex: Contemporary Readings , editor=. 2013 , publisher=

    2013

  198. [206]

    Signs: Journal of Women in Culture and Society , volume=

    Trapped in the wrong theory: Rethinking trans oppression and resistance , author=. Signs: Journal of Women in Culture and Society , volume=. 2014 , publisher=

    2014

  199. [207]

    Proceedings of the IEEE/CVF International Conference on Computer Vision , pages=

    Gender artifacts in visual datasets , author=. Proceedings of the IEEE/CVF International Conference on Computer Vision , pages=

  200. [208]

    Health Services Research , volume=

    Imputation of Race/Ethnicity to Enable Measurement of HEDIS Performance by Race/Ethnicity , author=. Health Services Research , volume=. 2019 , publisher=

    2019

  201. [209]

    IJCAI , volume=

    Name Nationality Classification with Recurrent Neural Networks , author=. IJCAI , volume=

Pith tools

Reviewed August 14, 2026 · model on record in the stance chip above.