Movatterモバイル変換


[0]ホーム

URL:


Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Advertisement

Nature
  • Perspective
  • Published:

Intersectional analysis for science and technology

Naturevolume 640pages329–337 (2025)Cite this article

Subjects

Abstract

Intersectionality describes interdependent systems of inequality related to sex, gender, race, age, class and other socio-political dimensions. By focusing on the compounded effects of social categories, intersectional analysis can enhance the accuracy and experimental efficiency of science. Here we extend intersectional approaches that were predominantly developed in the humanities, social sciences and public health to the fields of natural science and technology, where this type of analysis is less established. Informed by diverse global and disciplinary examples—from enhancing facial recognition for diverse user bases to mitigating the disproportionate impact of climate change on marginalized populations—we extract methods to demonstrate how quantitative intersectional analysis functions throughout the research process, from strategic considerations for establishing research priorities to formulating research questions, collecting and analysing data and interpreting results. Our goal is to offer a set of guidelines for researchers, peer-reviewed journals and funding agencies that facilitate systematic integration of intersectional analysis into relevant domains of science and technology. Precision in research best guides effective social and environmental policy aimed at achieving global equity and sustainability.

This is a preview of subscription content,access via your institution

Access options

Access through your institution

Access Nature and 54 other Nature Portfolio journals

Get Nature+, our best-value online-access subscription

9,800 Yen / 30 days

cancel any time

Subscription info for Japanese customers

We have a dedicated website for our Japanese customers. Please go tonatureasia.com to subscribe to this journal.

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF

Prices may be subject to local taxes which are calculated during checkout

Similar content being viewed by others

References

  1. Truth, S.Ain’t I a Woman? (1851);https://tag.rutgers.edu/wp-content/uploads/2014/05/Aint-I-woman.pdf.

  2. Combahee River Collective. inHome Girls: A Black Feminist Anthology (ed. Smith, B.) 307–318 (Rutgers Univ. Press, 2023).

  3. Crenshaw, K. Demarginalizing the intersection of race and sex: a Black feminist critique of antidiscrimination doctrine, feminist theory, and antiracist politics.Univ. Chic. Leg. Forum1, 138–167 (1989).This report develops the term‘intersectionality’and derives its meaning from key legal cases.

    Google Scholar 

  4. Crenshaw, K. Mapping the margins: intersectionality, identity, and violence against women of color.Stan. L. Rev.43, 1241–1300 (1991).

    Google Scholar 

  5. Collins, P. H. & Bilge, S.Intersectionality (John Wiley & Sons, 2020).This book includes detailed discussions of qualitative approaches to intersectionality.

  6. McCall, L. The complexity of intersectionality.Signs30, 1771–1800 (2005).

    Google Scholar 

  7. Choo, H. Y. & Ferree, M. M. Practicing intersectionality in sociological research: a critical analysis of inclusions, interactions, and institutions in the study of inequalities.Theory Soc.28, 129–149 (2010).

    Google Scholar 

  8. Grzanka, P. R., Brian, J. D. & Bhatia, R. Intersectionality and science and technology studies.Sci. Technol. Hum. Valueshttps://doi.org/10.1177/016224392312017 (2023).

  9. Cole, E. R. Intersectionality and research in psychology.Am. Psychol.64, 170–180 (2009).

    PubMed  Google Scholar 

  10. Hancock, A. M.Intersectionality: An Intellectual History (Oxford Univ. Press, 2016).

  11. Browne, I. & Misra, J. The intersection of gender and race in the labor market.Annu. Rev. Sociol29, 487–513 (2003).

    Google Scholar 

  12. Bauer, G. R. et al. Intersectionality in quantitative research: a systematic review of its emergence and applications of theory and methods.SSM Popul. Health14, 100798 (2021).This review providesfoundational quantitative approaches to intersectionality in public health sciences.

    PubMed PubMed Central  Google Scholar 

  13. Zou, J. & Schiebinger, L. AI can be sexist and racist—it’s time to make it fair.Nature559, 324–326 (2018).

    ADS CAS PubMed  Google Scholar 

  14. O’Neil, C.Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy (Crown, 2017).

  15. Noble, S. U.Algorithms of Oppression: How Search Engines Reinforce Racism (New York Univ. Press, 2018).

  16. Benjamin, R.Race after Technology: Abolitionist Tools for the New Jim Code (John Wiley & Sons, 2019).

  17. Buolamwini, J. & Gebru, T. Gender shades: intersectional accuracy disparities in commercial gender classification. InProc. 1st Conference on Fairness, Accountability and Transparency 77–91 (PMLR, 2018).

  18. Raji, I. D. et al. Saving face: investigating the ethical concerns of facial recognition auditing. InProc. AAAI/ACM Conference on AI, Ethics, and Society (eds Markham, A. et al.) 145–151 (Association for Computing Machinery, 2020).

  19. Tao, Y., Viberg, O., Baker, R. S. & Kizilcec, R. F. Cultural bias and cultural alignment of large language models.PNAS Nexus3, pgae346 (2024).

  20. Lawrence, H. M. inYour Computer is on Fire (eds Mullaney, T. S. et al.) 179–198 (MIT Press, 2021).

  21. Lett, E. & La Cava, W. G. Translating intersectionality to fair machine learning in health sciences.Nat. Mach. Intell.5, 476–479 (2023).This comment reframes fairness to contextualize individuals in interacting systems of power and oppression.

    PubMed PubMed Central  Google Scholar 

  22. Ranjan, R., Gupta, S. & Singh, S. N. A comprehensive survey of bias in LLMs: current landscape and future directions. Preprint athttps://doi.org/10.48550/arXiv.2409.16430 (2024).

  23. Horton, R. et al. From public to planetary health: a manifesto.Lancet383, 847 (2014).This statement describes the need to consider health and well-being at the personal, community, national, regional, global and planetary levels.

    PubMed  Google Scholar 

  24. Friel, S., Arthur, M. & Frank, N. Power and the planetary health equity crisis.Lancet400, 1085–1087 (2022).

    PubMed  Google Scholar 

  25. Amorim-Maia, A. T., Anguelovski, I., Chu, E. & Connolly, J. Intersectional climate justice: a conceptual pathway for bridging adaptation planning, transformative action, and social equity.Urban Clim.41, 101053 (2022).

    Google Scholar 

  26. Assaduzzaman, M., Filatova, T., Lovett, J. C. & Coenen, F. H. Gender-ethnicity intersectionality in climate change adaptation in the coastal areas of Bangladesh.Sustainability17, 3744 (2023).

    Google Scholar 

  27. Gabrys, J. Planetary health in practice: sensing air pollution and transforming urban environments.Humanit. Soc. Sci. Commun.7, 35 (2020).

    Google Scholar 

  28. Stein, P. J. et al. Advancing disability-inclusive climate research and action, climate justice, and climate-resilient development.Lancet Planet. Health8, e242–e255 (2024).

    PubMed  Google Scholar 

  29. Ngarava, S., Zhou, L., Ningi, T., Chari, M. M. & Mdiya, L. Gender and ethnic disparities in energy poverty: the case of South Africa.Energy Policy161, 112755 (2022).

    Google Scholar 

  30. Bail, C. A. Can generative AI improve social science?Proc. Natl Acad. Sci. USA121, e2314021121 (2024).

    CAS PubMed PubMed Central  Google Scholar 

  31. Lazer, D. M. et al. Computational social science.Science323, 721–723 (2009).

    CAS PubMed PubMed Central  Google Scholar 

  32. Lazer, D. M. et al. Computational social science: obstacles and opportunities.Science369, 1060–1062 (2020).

    ADS CAS PubMed  Google Scholar 

  33. Collins, P. H. Intersectionality’s definitional dilemmas.Annu. Rev. Sociol.41, 1–20 (2015).

    Google Scholar 

  34. Davis, A. Y.Women, Race, & Class (Vintage, 2011).

  35. Haq, R. Intersectionality of gender and other forms of identity: dilemmas and challenges facing women in India.Gend. Manag.28, 171–184 (2013).

    Google Scholar 

  36. Lui, L. Gender, rural-urban inequality, and intermarriage in China.Soc. Forces95, 639–662 (2016).

    Google Scholar 

  37. Raday, F. Culture, religion, and gender.Int. J. Const. Law1, 663–715 (2003).

    Google Scholar 

  38. Ritz, S. A. & Greaves, L. Transcending the male–female binary in biomedical research: constellations, heterogeneity, and mechanism when considering sex and gender.Int. J. Environ. Res. Public Health19, 4083 (2022).This article provides a key resource for understanding sex and gender.

    PubMed PubMed Central  Google Scholar 

  39. Gissi, E., Schiebinger, L., Santoleri, R. & Micheli, F. Sex analysis in marine biological systems: insights and opportunities.Front. Ecol. Environ.21, 324–332 (2023).This review provides foundational quantitative approaches to considering sex analysis in research design for biological studies.

    Google Scholar 

  40. Peters, M. R. & Slade, T. Enhancing sex-and gender-based analysis by using appropriate sub-variables.rehabINKrehabinkmag.com/2023/07/24/enhancing-sex-and-gender-based-analysis-by-using-appropriate-sub-variables/ (2023).

  41. Tannenbaum, C., Ellis, R. P., Eyssel, F., Zou, J. & Schiebinger, L. Sex and gender analysis improves science and engineering.Nature575, 137–146 (2019).This Perspective provides methods for analysing sex and gender in science and technology.

    ADS CAS PubMed  Google Scholar 

  42. Nielsen, M. W. et al. Gender-related variables for health research.Biol. Sex Diff.12, 23 (2021).

    Google Scholar 

  43. Special section on sex and gender.Cell187, 1513–1357 (2024).

  44. Why it’s essential to study sex and gender, even as tensions rise.Nature629, 7–8 (2024).

  45. Krieger, N., Williams, D. R. & Moss, N. E. Measuring social class in US public health research: concepts, methodologies, and guidelines.Annu. Rev. Public Health18, 341–378 (1997).

    CAS PubMed  Google Scholar 

  46. Arcaya, M. C., Arcaya, A. L. & Subramanian, S. V. Inequalities in health: definitions, concepts, and theories.Glob. Health Action8, 27106 (2015).

    PubMed  Google Scholar 

  47. Roth, W. D. The multiple dimensions of race.Ethn. Racial Stud.39, 1310–1338 (2016).

    Google Scholar 

  48. Flanagin, A., Frey, T., Christiansen, S. L. & AMA Manual of Style Committee. Updated guidance on the reporting of race and ethnicity in medical and science journals.JAMA326, 621–627 (2021).This editorial provides a basic resource for reporting race and ethnicity in research articles.

    PubMed  Google Scholar 

  49. Saperstein, A., Penner, A. M. & Light, R. Racial formation in perspective: connecting individuals, institutions, and power relations.Annu. Rev. Sociol39, 359–378 (2013).

    Google Scholar 

  50. Baker, R. S. The historical racial regime and racial inequality in poverty in the American South.Am. J. Sociol.127, 1721–1781 (2022).

    Google Scholar 

  51. Farkas, L.Analysis and Comparative Review of Equality Data Collection Practices in the European Union. Data Collection in the Field of Ethnicity (European Commission Directorate-General for Justice and Consumers, 2017).

  52. Yudell, M., Roberts, D., DeSalle, R. & Tishkoff, S. Taking race out of human genetics: engaging a century-long debate about the role of race in science.Science351, 564–565 (2016).

    ADS CAS PubMed  Google Scholar 

  53. Yudell, M. et al. NIH must confront the use of race in science.Science369, 1313–1314 (2020).

    ADS PubMed PubMed Central  Google Scholar 

  54. Khalfani, A. K. & Zuberi, T. Racial classification and the modern census in South Africa, 1911–1996.Race Soc.4, 161–176 (2001).

    Google Scholar 

  55. Movva, R. et al. Coarse race data conceals disparities in clinical risk score performance. InProc. 8th Machine Learning for Healthcare Conference 443–472 (PMLR, 2023).

  56. Zou, J., Gichoya, J. W., Ho, D. E. & Obermeyer, Z. Implications of predicting race variables from medical images.Science381, 149–150 (2023).

    ADS CAS PubMed  Google Scholar 

  57. Krieger, N.Ecosocial Theory, Embodied Truths, and the People’s Health (Oxford Univ. Press, 2021).

  58. Krieger, N., Smith, K., Naishadham, D., Hartman, C. & Barbeau, E. M. Experiences of discrimination: validity and reliability of a self-report measure for population health research on racism and health.Soc. Sci. Med.61, 1576–1596 (2005).

    PubMed  Google Scholar 

  59. Douds, K. W. & Hout, M. Microaggressions in the United States.Soc. Sci.7, 528–543 (2020).

    Google Scholar 

  60. Puckett, J. A. et al. Internalized homophobia and perceived stigma: a validation study of stigma measures in a sample of young men who have sex with men.Sex. Res. Soc. Policy14, 1–16 (2017).

    Google Scholar 

  61. Jackson, J. W. & VanderWeele, T. J. Intersectional decomposition analysis with differential exposure, effects, and construct.Soc. Sci. Med.226, 254–259 (2019).

    PubMed PubMed Central  Google Scholar 

  62. Bauer, G. R. & Scheim, A. I. Methods for analytic intercategorical intersectionality in quantitative research: Discrimination as a mediator of health inequalities.Soc. Sci. Med.226, 236–245 (2019).

    PubMed  Google Scholar 

  63. Pager, D., Bonikowski, B. & Western, B. Discrimination in a low-wage labor market: a field experiment.Am. Soc. Rev.74, 777–799 (2009).

    Google Scholar 

  64. Milkman, K. L., Akinola, M. & Chugh, D. What happens before? A field experiment exploring how pay and representation differentially shape bias on the pathway into organizations.J. Appl. Psychol.100, 1678 (2015).

    PubMed  Google Scholar 

  65. Gaddis, S. M. & Ghoshal, R. Arab American housing discrimination, ethnic competition, and the contact hypothesis.Ann. Am. Acad. Pol. Soc. Sci.660, 282–299 (2015).

    Google Scholar 

  66. Bowleg, L. The problem with the phrase women and minorities: intersectionality—an important theoretical framework for public health.Am. J. Public Health102, 1267–1273 (2012).

    PubMed PubMed Central  Google Scholar 

  67. Everett, B. G., Limburg, A., McKetta, S. & Hatzenbuehler, M. L. State-level regulations regarding the protection of sexual minorities and birth outcomes: results from a population-based cohort study.Psychosom. Med.84, 658–668 (2022).

    PubMed PubMed Central  Google Scholar 

  68. Krieger, N. et al. Structural racism, historical redlining, and risk of preterm birth in New York City, 2013–2017.Am. J. Public Health110, 1046–1053 (2020).

    PubMed  Google Scholar 

  69. Krieger, N. et al. Cancer stage at diagnosis, historical redlining, and current neighborhood characteristics: breast, cervical, lung, and colorectal cancers, Massachusetts, 2001–2015.Am. J. Epidemiol.189, 1065–1075 (2020).

    PubMed PubMed Central  Google Scholar 

  70. Wilson, B. Urban heat management and the legacy of redlining.J. Am. Plan. Assoc.86, 443–457 (2020).

    Google Scholar 

  71. Davis, S. N. & Greenstein, T. N. Gender ideology: components, predictors, and consequences.Annu. Rev. Sociol.35, 87–105 (2009).

    Google Scholar 

  72. Bareket, O. & Fiske, S. A systematic review of the ambivalent sexism literature: hostile sexism protects men’s power; benevolent sexism guards traditional gender roles.Psychol. Bull.149, 637–698 (2023).

    Google Scholar 

  73. Glick, P. & Fiske, S. T. The ambivalent sexism inventory: differentiating hostile and benevolent sexism.J. Pers. Soc. Psychol.70, 491–512 (1996).

    Google Scholar 

  74. McConahay, J. B. inPrejudice, Discrimination, and Racism (eds Dovidio, J. F. & Gaertner, S. L.) 91–125 (Academic Press, 1986).

  75. Tarman, C. & Sears, D. O. The conceptualization and measurement of symbolic racism.J. Politics67, 731–761 (2005).

    Google Scholar 

  76. Hill, D. B. & Willoughby, B. L. The development and validation of the genderism and transphobia scale.Sex Roles53, 531–544 (2005).

    Google Scholar 

  77. Chae, D. H. et al. Association between an internet-based measure of area racism and black mortality.PLoS ONE10, e0122963 (2015).

    PubMed PubMed Central  Google Scholar 

  78. Breda, T., Jouini, E., Napp, C. & Thebault, G. Gender stereotypes can explain the gender-equality paradox.Proc. Natl Acad. Sci. USA117, 31063–31069 (2020).

    ADS CAS PubMed PubMed Central  Google Scholar 

  79. Agyeman, J., Schlosberg, D., Craven, L. & Matthews, C. Trends and directions in environmental justice: from inequity to everyday life, community, and just sustainabilities.Annu. Rev. Environ. Resour.41, 321–340 (2016).This review illustrates advances in theory and practice of environmental justice towards climate justice, indigenous justice, food justice and energy justice.

    Google Scholar 

  80. Taylor, D. E. (2014).Toxic Communities: environmental Racism, Industrial Pollution, and Residential Mobility (New York Univ. Press, 2014).

  81. Auyero, J. & Swistun, D. A.Flammable: Environmental suffering in an Argentine Shantytown (Oxford Univ. Press, 2009).

  82. Haram, L. E., Carlton, J. T., Ruiz, G. M. & Maximenko, N. A. A plasticene lexicon.Mar. Pollut. Bull.150, 110714 (2020).

    CAS PubMed  Google Scholar 

  83. Jung, Y. S. et al. Characterization and regulation of microplastic pollution for protecting planetary and human health.Environ. Pollut.315, 120442 (2022).This study provides an overview of microplastics and their impacts on the environment and human health in efforts to support the management and regulation of plastic wastes.

    CAS PubMed  Google Scholar 

  84. Landrigan, P. J. et al. The Minderoo–Monaco Commission on plastics and human health.Ann. Glob. Health89, 23 (2023).This report examines the intersection between plastic, social inequity and environmental injustice.

    PubMed PubMed Central  Google Scholar 

  85. Albers, P. N., Wright, C. Y., Voyi, K. V. & Mathee, A. Household fuel use and child respiratory ill health in two towns in Mpumalanga, South Africa.S. Afr. Med. J.105, 573–577 (2015).

    PubMed  Google Scholar 

  86. Cortes-Ramirez, J., Naish, S., Sly, P. D. & Jagals, P. Mortality and morbidity in populations in the vicinity of coal mining: a systematic review.BMC Public Health18, 721 (2018).

    PubMed PubMed Central  Google Scholar 

  87. Perera, F. & Nadeau, K. Climate change, fossil-fuel pollution, and children’s health.New Engl. J. Med.386, 2303–2314 (2022).

    CAS PubMed  Google Scholar 

  88. Symeonides, C. et al. Buy-now-pay-later: hazards to human and planetary health from plastics production, use and waste.J. Paediatr. Child Health57, 1795–1804 (2021).

    PubMed PubMed Central  Google Scholar 

  89. Jones, R., Macmillan, A. & Reid, P. Climate change mitigation policies and co-impacts on indigenous health: a scoping review.Int. J. Environ. Res. Public Health17, 9063 (2020).

    PubMed PubMed Central  Google Scholar 

  90. Deivanayagam, T. A. et al. Envisioning environmental equity: climate change, health, and racial justice.Lancet402, 64–78 (2023).This article discusses structural discriminations that drive unequal impacts of climate change.

    PubMed PubMed Central  Google Scholar 

  91. Cundill, G. et al. Toward a climate mobilities research agenda: intersectionality, immobility, and policy responses.Glob. Environ. Change69, 102315 (2021).This synthesis discusses the implications of mobility in the context of climate change migration.

    Google Scholar 

  92. Rastogi, D. et al. Exploring the spatial patterning of sociodemographic disparities in extreme heat exposure at multiple scales across the conterminous United States.Geohealth7, e2023GH000864 (2023).

    PubMed PubMed Central  Google Scholar 

  93. Alvarez, C. H. & Evans, C. R. Intersectional environmental justice and population health inequalities: a novel approach.Soc. Sci. Med.269, 113559 (2021).

    PubMed  Google Scholar 

  94. McDonald, R. I. et al. The tree cover and temperature disparity in US urbanized areas: quantifying the association with income across 5,723 communities.PLoS ONE16, e0249715 (2021).

    CAS PubMed PubMed Central  Google Scholar 

  95. Chakraborty, J., Collins, T. W. & Grineski, S. E. Exploring the environmental justice implications of Hurricane Harvey flooding in Greater Houston, Texas.Am. J. Public Health109, 244–250 (2019).

    PubMed PubMed Central  Google Scholar 

  96. Arunachalam, M., Saravanavel, J. & Joseph Kochuparampil, A. PCA-based approach for mapping social vulnerability to hazards in the Chennai metropolitan area, east coast of India.Ann. GIS29, 529–552 (2023).

    Google Scholar 

  97. Vieira, R. M. S. P. et al. Characterizing spatio-temporal patterns of social vulnerability to droughts, degradation and desertification in the Brazilian northeast.Environ. Sustain. Indic.5, 100016 (2020).

    Google Scholar 

  98. Domingue, S. J. & Emrich, C. T. Social vulnerability and procedural equity: exploring the distribution of disaster aid across counties in the United States.Am. Rev. Public Admin.49, 897–913 (2019).

    Google Scholar 

  99. Arcaya, M., Raker, E. J. & Waters, M. C. The social consequences of disasters: individual and community change.Annu. Rev. Sociol46, 671–691 (2020).This review highlights intersectional factors of social vulnerability to disasters.

    Google Scholar 

  100. Park, L. S. H. & Pellow, D. inThe Slums of Aspen (New York Univ. Press, 2011).

  101. Mosley, T. J. et al. Intersectionality and diversity, equity, and inclusion in the healthcare and scientific workforces.Lancet41, 100973 (2025).

    Google Scholar 

  102. Nielsen, M. W., Bloch, C. W. & Schiebinger, L. Making gender diversity work for scientific discovery and innovation.Nat. Hum. Behav.2, 726–734 (2018).

    PubMed  Google Scholar 

  103. Macari, D., Fratzl, A., Keplinger, K. & Keplinger, C. Accelerating the pace of innovation in robotics by fostering diversity and inclusive leadership.Sci. Robot.9, eadt1958 (2024).

    PubMed  Google Scholar 

  104. Abrams, J. A., Tabaac, A., Jung, S. & Else-Quest, N. M. Considerations for employing intersectionality in qualitative health research.Soc. Sci. Med.258, 113138 (2020).

    PubMed PubMed Central  Google Scholar 

  105. Norström, A. V. et al. Principles for knowledge co-production in sustainability research.Nat. Sustain.3, 182–190 (2020).

    Google Scholar 

  106. Frank, L. et al. Conceptual and practical foundations of patient engagement in research at the Patient-Centered Outcomes Research Institute.Qual. Life Res.24, 1033–1041 (2015).

    ADS PubMed PubMed Central  Google Scholar 

  107. Forsythe, L. P. et al. Patient engagement in research: early findings from the Patient-Centered Outcomes Research Institute.Health Aff.38, 359–367 (2019).

    Google Scholar 

  108. Sloane, M., Moss, E., Awomolo, O. & Forlano, L. Participation is not a design fix for machine learning. InProc. 2nd ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization 1–6 (Association for Computing Machinery, 2022).

  109. Pratt, B. Inclusion of marginalized groups and communities in global health research priority-setting.J. Empir. Res. Hum. Res. Ethics14, 169–181 (2019).

    PubMed  Google Scholar 

  110. Egid, B. R. et al. ‘You want to deal with power while riding on power’: global perspectives on power in participatory health research and co-production approaches.BMJ Glob. Health6, e006978 (2021).

    PubMed PubMed Central  Google Scholar 

  111. Hankivsky, O.Intersectionality 101 (Simon Fraser Univ., 2014).

  112. Mahendran, M., Lizotte, D. & Bauer, G. R. Quantitative methods for descriptive intersectional analysis with binary health outcomes.SSM Popul. Health17, 101032 (2022).

    PubMed PubMed Central  Google Scholar 

  113. Miller, A. C., Gatys, L. A., Futoma, J. & Fox, E. Model-based metrics: sample-efficient estimates of predictive model subpopulation performance. In Proc. 6th Machine Learning for Healthcare Conference 308–336 (PMLR, 2021).

  114. Herlihy, C., Truong, K., Chouldechova, A. & Dudík, M. A structured regression approach for evaluating model performance across intersectional subgroups. In2024 ACM Conference on Fairness, Accountability, and Transparency 313–325 (Association for Computing Machinery, 2024).

  115. D’Ignazio, C. & Klein, L. F.Data Feminism (MIT Press, 2023).

  116. Progovac, A. M. et al. Identifying gender minority patients’ health and health care needs in administrative claims data.Health Aff.37, 413–420 (2018).

    Google Scholar 

  117. Westbrook, L. & Saperstein, A. New categories are not enough: Rethinking the measurement of sex and gender in social surveys.Gender Soc.29, 534–560 (2015).

    Google Scholar 

  118. Magliozzi, D., Saperstein, A. & Westbrook, L. Scaling up: representing gender diversity in survey research.Sociushttps://doi.org/10.1177/2378023116664352 (2016).

  119. Bowleg, L. & Bauer, G. R. Invited reflection: quantifying intersectionality.Psychol. Women Q.40, 337–341 (2016).

    Google Scholar 

  120. Macapagal, K., Coventry, R., Arbeit, M. R., Fisher, C. B. & Mustanski, B. “I won’t out myself just to do a survey”: sexual and gender minority adolescents’ perspectives on the risks and benefits of sex research.Arch. Sex. Behav.46, 1393–1409 (2017).

    PubMed  Google Scholar 

  121. Li, D., Lin, C. T., Sulam, J. & Yi, P. H. Deep learning prediction of sex on chest radiographs: a potential contributor to biased algorithms.Emerg. Radiol.29, 365–370 (2022).

    PubMed  Google Scholar 

  122. Larrazabal, A. J., Nieto, N., Peterson, V., Milone, D. H. & Ferrante, E. Gender imbalance in medical imaging datasets produces biased classifiers for computer-aided diagnosis.Proc. Natl Acad. Sci.USA117, 12592–12594 (2020).

    ADS CAS PubMed PubMed Central  Google Scholar 

  123. Lemarchand, P., Hassoun, D. & Kuntz, P. Questioning the relevance of sex categories implemented in medical decision support systems: the example of pulmonary function.ESSACHESS17, 205–229 (2024).

    Google Scholar 

  124. Weber, A. M. et al. Gender norms and health: insights from global survey data.Lancet393, 2455–2468 (2019).This paper is one of a five-part series examining gender equality, norms and health that make recommendations for optimizing research on the health impacts of gender norms.

    PubMed  Google Scholar 

  125. Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. BERT: pre-training of deep bidirectional transformers for language understanding. InProc. 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Vol. 1 4171–4186 (Association for Computational Linguistics, 2019).

  126. Bender, E. M., Gebru, T., McMillan-Major, A. & Shmitchell, S. On the danger of stochastic parrots: can language models be too big? InProc. 2021 ACM Conference on Fairness, Accountability, and Transparency 610–623 (Association for Computing Machinery, 2021).

  127. Zack, T. et al. Assessing the potential of GPT-4 to perpetuate racial and gender biases in health care: A model evaluation study.Lancet Digit. Health6, e12–e22 (2024).

    CAS PubMed  Google Scholar 

  128. Ali, R. et al. Demographic representation in 3 leading artificial intelligence text-to-image generators.JAMA Surg.159, 87–95 (2024).

    PubMed  Google Scholar 

  129. Jacobi, T. & Sag, M. We are the AI problem.Emory L. J.74, 1 (2024).

    Google Scholar 

  130. Hancock, A. M. When multiplication doesn’t equal quick addition: examining intersectionality as a research paradigm.Perspect. Politics5, 63–79 (2007).

    Google Scholar 

  131. Schulman, K. A. et al. The effect of race and sex on physicians’ recommendations for cardiac catheterization.New Engl. J. Med.340, 618–626 (1999).

    CAS PubMed  Google Scholar 

  132. Schwartz, L. M., Woloshin, S. & Welch, H. G. Misunderstandings about the effects of race and sex on physicians’ referrals for cardiac catheterization.New Engl. J. Med.341, 279–283 (1999).

    CAS PubMed  Google Scholar 

  133. McCabe, C. J. et al. Estimating substance use disparities across intersectional social positions using machine learning: an application of group-lasso interaction network.Psychol. Addict. Behav.39, 113–126 (2025).

    PubMed  Google Scholar 

  134. Evans, C. R., Williams, D. R., Onnela, J. P. & Subramanian, S. V. A multilevel approach to modeling health inequalities at the intersection of multiple social identities.Soc. Sci. Med.203, 64–73 (2018).

    PubMed  Google Scholar 

  135. Merlo, J. Multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA) within an intersectional framework.Soc. Sci. Med.203, 74–80 (2018).

    PubMed  Google Scholar 

  136. Evans, C. R. et al. Clarifications on the intersectional MAIHDA approach: a conceptual guide and response to Wilkes and Karimi (2024).Soc. Sci. Med.350, 116898 (2024).

    PubMed  Google Scholar 

  137. Sinha, A. et al. Race-and sex-specific population attributable fractions of incident heart failure: a population-based cohort study from the lifetime risk pooling project.Circ. Heart Failure14, e008113 (2021).

    PubMed  Google Scholar 

  138. Versey, H. S. Missing pieces in the discussion on climate change and risk: intersectionality and compounded vulnerability.Policy Insights Behav. Brain Sci.8, 67–75 (2021).

    Google Scholar 

  139. DeFur, P. L. et al. Vulnerability as a function of individual and group resources in cumulative risk assessment.Environ. Health Perspect.115, 817–824 (2007).This article discusses how many discriminatory factors compound vulnerabilities to climate change.

    CAS PubMed PubMed Central  Google Scholar 

  140. Green, A. E., Price, M. N. & Dorison, S. H. Cumulative minority stress and suicide risk among LGBTQ youth.Am. J. Commun. Psychol.69, 157–168 (2022).

    Google Scholar 

  141. Shen, K. & Zeng, Y. Direct and indirect effects of childhood conditions on survival and health among male and female elderly in China.Soc. Sci. Med.119, 207–214 (2014).

    PubMed PubMed Central  Google Scholar 

  142. Hermstad, A. K., Swan, D. W., Kegler, M. C., Barnette, J. K. & Glanz, K. Individual and environmental correlates of dietary fat intake in rural communities: a structural equation model analysis.Soc. Sci. Med.71, 93–101 (2010).

    PubMed  Google Scholar 

  143. Jang, S. & Kim, J. Remedying food policy invisibility with spatial intersectionality: a case study in the Detroit Metropolitan Area.J. Public Pol. Mark.37, 167–187 (2018).

    Google Scholar 

  144. Bauer, G. R., Mahendran, M., Walwyn, C. & Shokoohi, M. Latent variable and clustering methods in intersectionality research: systematic review of methods applications.Soc. Psychiatry Psychiatr. Epidemiol.57, 221–237 (2022).

    PubMed  Google Scholar 

  145. Tadesse, G. A. et al. Bridging the gap: leveraging data science to equip domain experts with the tools to address challenges in maternal, newborn, and child health.npj Womens Health2, 13 (2024).

    Google Scholar 

  146. Frees, E. W.Longitudinal and Panel Data: Analysis and Applications in the Social Sciences (Cambridge Univ. Press, 2004).

  147. Snijders, T. & Bosker, R.Multilevel Analysis: An Introduction to Basic and Advanced Multilevel Modeling (Sage, 2012).

  148. Barker, K. M. et al. Cross-classified multilevel models (CCMM) in health research: a systematic review of published empirical studies and recommendations for best practices.SSM Popul. Health12, 100661 (2020).

    PubMed PubMed Central  Google Scholar 

  149. Leavy, P.Oral History: Understanding Qualitative Research (Oxford Univ. Press, 2011).

  150. Katerere, D. R., Applequist, W., Aboyade, O. M. & Togo, C. eds.Traditional and Indigenous Knowledge for the Modern Era: A Natural and Applied Science Perspective (CRC Press, 2019).

  151. Kearns, M., Neel, S., Roth, A. & Wu, Z. S. Preventing fairness gerrymandering: auditing and learning for subgroup fairness. InProc. 35th International Conference on Machine Learning 2564–2572 (PMLR, 2018).This paper proposes solutions to the problem of fairness where a classifier is fair on each individual group but violates the fairness constraint on structured subgroups.

  152. Speakman, S. et al. Detecting systematic deviations in data and models.Computer56, 82–92 (2023).

    Google Scholar 

  153. Liang, W. et al. Advances, challenges and opportunities in creating data for trustworthy AI.Nat. Mach. Intell.4, 669–677 (2022).This Perspective discusses key considerations for each stage of the data-for-AI pipeline to help make it more scalable and rigorous.

    Google Scholar 

  154. Caton, S. & Haas, C. Fairness in machine learning: a survey.ACM Comput. Surv.56, 1–38 (2024).

    Google Scholar 

  155. Hébert-Johnson, U., Kim, M., Reingold, O. & Rothblum, G. Multicalibration: calibration for the (computationally-identifiable) masses. InProc. 35th International Conference on Machine Learning 1939–1948 (PMLR, 2018).

  156. Foulds, J. R., Islam, R., Keya, K. N. & Pan, S. An intersectional definition of fairness. InIEEE 36th International Conference on Data Engineering 1918–1921 (IEEE, 2020).

  157. Hutchinson, B. & Mitchell, M. 50 years of test (un)fairness: lessons for machine learning. InProc. Conference on Fairness, Accountability, and Transparency 49–58 (Association for Computing Machinery, 2019).

  158. National Academies of Sciences, Engineering, and Medicine.Fostering Responsible Computing Research: Foundations and Practices (The National Academies Press, 2022).

  159. Kraemer-Mbula, E., Tijssen, R., Wallace, M. L. & McClean, R.Transforming Research Excellence: New Ideas from the Global South (African Minds, 2019).

  160. Debusscher, P. & Maes, E. L. The European Union–intersectionality framework: unpacking intersectionality in the ‘Union of Equality’ agenda.Political Stud. Rev.https://doi.org/10.1177/14789299241242 (2024).

  161. Abrahams, L. & Burke, M.Working Paper on Digital Research Infrastructure (National Research Foundation of South Africa, 2023).

  162. Lynch, I. et al.Intersectionality in Research, Grant-Making and Human Capital Development: Considerations for Public Funding Agencies in Advancing Equality, Diversity and Inclusion. Integrated Report for Sub-Saharan Africa (Human Science Research Council, 2022);https://sgciafrica.org/wp-content/uploads/2022/11/Integrated-report_Intersectionality-in-research-grant-making-and-human-capital-development.pdf.

  163. Okune, A., Hillyer, R., Albornoz, D. Posada, A. & Chan, L. Whose infrastructure? Towards inclusive and collaborative knowledge infrastructures in open science. InProc. ElPub Conferencehttps://doi.org/10.4000/proceedings.elpub.2018.31 (Electronic PUBlishing, 2018).

  164. Sex, gender, and/or intersectional analysis policies of major granting agencies.Gendered Innovationshttp://genderedinnovations.stanford.edu/sex-and-gender-analysis-policies-major-granting-agenciesAug621.html (2024).

  165. Sex, gender, and/or intersectional analysis policies of peer-reviewed journals.Gendered Innovationshttp://genderedinnovations.stanford.edu/sex-and-gender-analysis-policies-peer-reviewed-journals.html (2024).

  166. Heidari, S. et al. Sex and gender equity in research: rationale for the SAGER guidelines and recommended use.Res. Integr. Peer Rev.https://doi.org/10.1186/s41073-016-0007-6 (2016).These guidelines provide a basic resource for reporting sex and gender in research articles.

  167. Hartig, C. et al. A deeper consideration of sex/gender in quantitative health research: a checklist for incorporating multidimensionality, variety, embodiment, and intersectionality throughout the whole research process.BMC Med. Res. Method.24, 180 (2024).

    Google Scholar 

  168. Editorial policies, research ethics.Nature Portfoliohttps://www.nature.com/nature-portfolio/editorial-policies/ethics-and-biosecurity#research-on-human-populations (2025).This is a basic resource for reporting race and ethnicity in research articles.

  169. Chew, M., Samuel, D., Mullan, Z. & Kleinert, S. The Lancet group’s new guidance to authors on reporting race and ethnicity.Lancet403, 2360–2361 (2024).

    PubMed  Google Scholar 

  170. Schiebinger, L. & Klinge, I.Gendered Innovations 2: How Inclusive Analysis Contributes to Research and Innovation, A Policy Review (European Commission, 2020).

  171. Incorporating Intersectional Gender Analysis into Research on Infectious Diseases of Poverty—A Toolkit for Health Researchers (World Health Organization, 2020);https://www.who.int/publications/i/item/9789240008458.

  172. Kabir, A., Thomson, T. & Abukito, A.Intersectionality Resource Guide and Toolkit: An Intersectional Approach to Leave No One Behind (United Nations Women, 2021);https://www.unwomen.org/sites/default/files/2022-01/Intersectionality-resource-guide-and-toolkit-en.pdf.

  173. Mbah, O. et al.Advancing Equity by Incorporating Intersectionality in Research and Analysis (US Office of Human Services Policy, 2022).

  174. Mohai, P., Pellow, D. & Roberts, J. T. Environmental justice.Annu. Rev. Environ. Resour.34, 405–430 (2009).

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Sociology, University of Copenhagen, Copenhagen, Denmark

    Mathias Wullum Nielsen

  2. National Research Council, Institute of Marine Sciences, Venice, Italy

    Elena Gissi

  3. National Biodiversity Future Center, Palermo, Italy

    Elena Gissi

  4. GENDRO, Gender Centre, Geneva Graduate Institute, Geneva, Switzerland

    Shirin Heidari

  5. The Lancet, London, UK

    Richard Horton

  6. Department of Environmental Health, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA

    Kari C. Nadeau

  7. Center for Climate, Health and the Global Environment, Harvard University, Boston, MA, USA

    Kari C. Nadeau

  8. Knowledge and Institutional Networks, National Research Foundation of South Africa, Pretoria, South Africa

    Dorothy Ngila

  9. Division of Social Sciences, University of California, Los Angeles, Los Angeles, CA, USA

    Safiya Umoja Noble

  10. Minderoo Initiative on Tech and Power, Center on Race and Digital Justice, University of California, Los Angeles, Los Angeles, CA, USA

    Safiya Umoja Noble

  11. Department of Gender Studies, University of California, Los Angeles, Los Angeles, CA, USA

    Safiya Umoja Noble

  12. Department of African American Studies, University of California, Los Angeles, Los Angeles, CA, USA

    Safiya Umoja Noble

  13. Department of Information Studies, University of California, Los Angeles, Los Angeles, CA, USA

    Safiya Umoja Noble

  14. Department of Foods and Nutrition, Seoul National University, Seoul, Republic of Korea

    Hee Young Paik

  15. Microsoft AI for Good Lab, Nairobi, Kenya

    Girmaw Abebe Tadesse

  16. School of Environment and Energy, South China University of Technology, Environmental Pollution, Guangzhou, China

    Eddy Y. Zeng

  17. Department of Biomedical Data Science, Stanford University, Stanford, CA, USA

    James Zou

  18. Department of Computer Science, Stanford University, Stanford, CA, USA

    James Zou

  19. Department of Electrical Engineering, Stanford University, Stanford, CA, USA

    James Zou

  20. History of Science, Stanford University, Stanford, CA, USA

    Londa Schiebinger

  21. Gendered Innovations in Science, Health and Medicine, Engineering and Environment, Stanford University, Stanford, CA, USA

    Londa Schiebinger

Authors
  1. Mathias Wullum Nielsen

    You can also search for this author inPubMed Google Scholar

  2. Elena Gissi

    You can also search for this author inPubMed Google Scholar

  3. Shirin Heidari

    You can also search for this author inPubMed Google Scholar

  4. Richard Horton

    You can also search for this author inPubMed Google Scholar

  5. Kari C. Nadeau

    You can also search for this author inPubMed Google Scholar

  6. Dorothy Ngila

    You can also search for this author inPubMed Google Scholar

  7. Safiya Umoja Noble

    You can also search for this author inPubMed Google Scholar

  8. Hee Young Paik

    You can also search for this author inPubMed Google Scholar

  9. Girmaw Abebe Tadesse

    You can also search for this author inPubMed Google Scholar

  10. Eddy Y. Zeng

    You can also search for this author inPubMed Google Scholar

  11. James Zou

    You can also search for this author inPubMed Google Scholar

  12. Londa Schiebinger

    You can also search for this author inPubMed Google Scholar

Contributions

M.W.N. and L.S. conceptualized, wrote and edited the article with input from all authors. E.G. contributed sections on environmental analysis. G.A.T. and J.Z. contributed sections on AI and machine learning. R.H., S.H. and D.N. reviewed and edited proposed peer-reviewed journal and funding agency guidelines. K.C.N., H.Y.P., E.Y.Z., S.U.N. and G.A.T. contributed globally relevant perspectives and materials. All authors reviewed multiple versions of the manuscript.

Corresponding author

Correspondence toLonda Schiebinger.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature thanks Joseph Feinglass, Sabra Klein, Sharuna Verghis and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Guidelines for reporting intersectional analysis in science and technology (GRIST).

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nielsen, M.W., Gissi, E., Heidari, S.et al. Intersectional analysis for science and technology.Nature640, 329–337 (2025). https://doi.org/10.1038/s41586-025-08774-w

Download citation

Access through your institution
Buy or subscribe

Advertisement

Search

Advanced search

Quick links

Nature Briefing

Sign up for theNature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox.Sign up for Nature Briefing

[8]ページ先頭

©2009-2025 Movatter.jp