Upcoming EVENTSCommittee on Women in Science, Engineering, and Medicine Celebrating Women in Science and Recognizing L’ORÉAL USA for Women in Science Fellows
October 24, 2018
Sexual Harassment of Women: Climate, Culture, and Consequences in Academic Sciences, Engineering, and Medicine
June 26, 2018
More past events...
Committee on Women in Science, Engineering, and Medicine
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Washington, DC 20001
Resources on Implicit Bias
These books represent comprehensive reviews of research related to inherent bias and related topics. All are well written and have extensive notes and references.
Banaji, Mahzarin R. and Greenwald, Anthony G. Blindspot: Hidden Biases of Good People. Delecorte Press, New York, 2013
Sandberg, Sheryl. Lean In: Women, Work, and the Will to Lead. Alfred A. Knopf, New York, 2013
Steele, Claude M. Whistling Vivaldi: How Stereotypes Affect Us and What We Can Do. WW Norton & Company, New York, 2010 (Issues of Our Times, series editor Henry Louis Gates, Jr.)
Kahneman, Daniel. Thinking, Fast and Slow. Farrar, Strauss and Giroux, New York, 2011
Kaplan, Mark. The Inclusion Dividend: Why Investing in Diversity & Inclusion Pays Off. Bibliomotion; First edition, 2013
Ross, Howard. Everyday Bias: Identifying and Navigating Unconscious Judgements in Our Daily Lives. Rowman & Littlefield Publishers, 2014
Project Implicit - From the web site: “Project Implicit investigates thoughts and feelings that exist outside of conscious awareness or conscious control.” A rich source of reviews, primary literature, and links to research sites and tests.
Implicit Association Test by Project Implicit - This site has 14 different implicit association tests (IAT) including Gender-Science IAT, Age IAT, Race IAT, and Gender-Career IAT.
Women in Science & Engineering Leadership Institute, University of Wisconsin-Madison - Web site of the Women in Science & Engineering Leadership Institute (WISELI) at the University of Wisconsin-Madison. Founded in 2002 with an NSF ADVANCE grant; now funded by contributions from 8 UW-Madison schools, colleges, and units as well as funding agencies, gifts, and income generated by WISELI activities.
Kirwan Institute – This site provides an annual report on the state of implicit bias in the United States.
Games Learning Society - Another rich source of information including the prototype of a game under development.
Committee on Maximizing the Potential of Women in Academic Science and Engineering. 2006. Beyond Bias and Barriers: Fulfilling the Potential of Women in Academic Science and Engineering. The National Academies Press: Washington, DC. This report can be downloaded at http://www.nap.edu/catalog/11741/beyond-bias-and-barriers-fulfilling-the-potential-of-women-in
Committee on Advancing Institutional Transformation for Minority Women in Academia. 2013. Seeking Solutions: Maximizing American Talent by Advancing Women of Color in Academia. The National Academies Press: Washington, DC. Conference Summary, Workshop Summary, and slides can be downloaded at http://sites.nationalacademies.org/PGA/cwsem/minoritywomen/PGA_087695
National Academy of Engineering. 2014. Advancing Diversity in the US Industrial Science and Engineering Workforce: Summary of a Workshop. Washington, DC: The National Academies Press. This report can be downloaded at http://www.nap.edu/catalog/13512/advancing-diversity-in-the-us-industrial-science-and-engineering-workforce
Special Report: State of the World’s Science 2014: How diversity powers innovation. Sci American 311:12, 38-57. A collection of 7 articles (some very short), 2 editorials and some terrific graphics.
Diversity, a Nature Special Feature in News and Comment. 2014. Nature 513:297-307.
The Massachusetts Institute of Technology. 1999. A study on the status of women faculty in science at MIT. Available at http://web.mit.edu/fnl/women/women.html#.
Byars-Winston, A. (2014). Toward a framework for multicultural STEM-focused career interventions. Career Development Quarterly, 62(4), 340-357. doi: 10.1002/j.2161-0045.2014.00087.x
Carnes, M., Devine, P. G., Baier Manwell, L., Byars-Winston, A., Fine, E., Ford, C. E., . . . Sheridan, J. (2015). The effect of an intervention to break the gender bias habit for faculty at one institution: A cluster randomized, controlled trial. Academic Medicine, 90(2), 221-230. doi: 10.1097/ACM.0000000000000552
Carnes M, Devine PG, Isaac C, Manwell LB, Ford CE, Byars-Winston A, Fine E, Sheridan JT (2012) Promoting Institutional Change through Bias Literacy J Divers High Educ 5: 63–77. doi: 10.1037/a0028128 Description of development and implementation of a workshop for faculty
Carnes M, Geller S, Fine E, Sheridan J, Handelsman J (2005) NIH Director's Pioneer Awards: Could the Selection Process Be Biased against Women? J Women's Health 14:684-691. doi:10.1089/jwh.14.684.
Chapman EN, Kaatz A, Carnes M (2013) Physicians and Implicit Bias: How Doctors May Unwittingly Perpetuate Health Care Disparities. J Gen Intern Med. 28(11): 1504–1510.
doi: 10.1007/s11606-013-2441-1. PMCID: PMC3797360
Devine PG, Forscher PS, Austin AJ, Cox WTL (2012) Long-term reduction in implicit race bias: A prejudice habit-breaking intervention. J Exp Soc Psychol. 48(6): 1267–1278. doi:
Eddy, S. L., Brownell, S. E., & Wenderoth, M. P. (2014). Gender gaps in achievement and participation inmultiple introductory biology classrooms. CBE Life Sciences Education, 13(3), 478-492. doi: 10.1187/cbe.13-10-0204
Fine, E., Sheridan, J., Carnes, M., Handelsman, J., Pribbenow, C., Savoy, J., & Wendt, A. (2014) Minimizing the influence of gender bias on the faculty search process. Vol. 19. Advances in Gender Research (pp. 267-289).
Greenwald AG, Krieger LH(2006) Implicit Bias: Scientific Foundations. California Law Review 94:945-968. Available at http://scholarship.law.berkeley.edu/californialawreview/vol94/iss4/1
Handelsman J, Cantor N, Carnes M, Denton D, Fine E, Grosz B, Hinshaw V, Marrett C, Rosser R, Shalala D, Sheridan J (2005) "More Women in Science." Science 309:1190-1191. doi: 10.1126/science.1113252
Jost JT, Rudman LA, Blair IV, Carney DR, Dasgupta N, Glaser J, Hardin CD (2009) The existence of implicit bias is beyond reasonable doubt: A refutation of ideological and methodological objections and executive summary of ten studies that no manager should ignore. Research in Organizational Behavior 29:39–69.
Larivière, V., Ni, C., Gingras, Y., Cronin, B., & Sugimoto, C. R. (2013). Global gender disparities in science. Nature, 504(7479), 211-213. doi: 10.1038/504211a
Moss-Racusin, C. A., Van Der Toorn, J., Dovidio, J. F., Brescoll, V. L., Graham, M. J., & Handelsman, J. (2014). Scientific diversity interventions. Science, 343(6171), 615-616. doi: 10.1126/science.1245936
Moss-Racusin CA, Dovidio JF, Brescoll VL, Graham MJ, Handelsman J (2012) Science faculty’s subtle gender biases favor male students. Proc. Natl Acad Sci 109:16474–16479. doi: 10.1073/pnas.1211286109
Nosek BA, Smyth FL (2011). Implicit social cognitions predict sex differences in math engagement and achievement. American Educational Research Journal, 48, 1124-1154.
Reuben, E., Sapienza, P., & Zingales, L. (2014). How stereotypes impair women's careers in science. Proceedings of the National Academy of Sciences of the United States of America, 111(12), 4403-4408. doi: 10.1073/pnas.1314788111
Shen, H. (2013). Inequality quantified: Mind the gender gap. Nature, 494(7439), 22-24. doi: 10.1038/495022a
Stanley, D. A., Sokol-Hessner, P., Banaji, M. R., & Phelps, E. A. (2011). Implicit race attitudes predict trustworthiness judgments and economic trust decisions. Proceedings of the National Academy of Sciences of the United States of America, 108(19), 7710-7715. doi: 10.1073/pnas.1014345108
Stephens, N. M., Markus, H. R., & Phillips, L. T. (2014) Social class culture cycles: How three gateway contexts shape selves and fuel inequality. Vol. 65. Annual Review of Psychology (pp. 611-634).
Tsay CJ, Banaji MR (2011) Naturals and Strivers: Preferences and Beliefs about Sources of Achievement. Journal of Experimental Social Psychology, 47, 460-465.
To download this list click here (PDF).
For further readings topics related to CWSEM interests, including gender inequality, bias, stereotype threat: click here (PDF).