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A subset of machine learning research intersects with societal issues, including fairness, accountability and transparency, as well as the use of machine learning for social good.
Insiders and Outsiders: A Chapter in the Sociology of Knowledge
Merton, R. K. 1972 · 1972
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The Double Bind: The Price of Being a Minority Woman in Science. Report of a Conference of Minority Women Scientists, Arlie House, Warrenton, Virginia
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Boundary-Work and the Demarcation of Science from Non-Science: Strains and Interests in Professional Ideologies of Scientists
Gieryn, T. F. 1983 · 1983
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Black Scientists, White Society, and Colorless Science: A Study of Universalism in American Science
Pearson, Jr., W. 1985 · 1985
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Situated Knowledges: The science Question in Feminism and the Privilege of Partial Perspective
Haraway, D. 1988 · 1988
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Why Are There So Few Female Computer Scientists?
Spertus, E. 1991 · 1991
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The Outer Circle: Women in the Scientific Community
Zuckerman, H.; Cole, J. R.; and Bruer, J. T., eds. 1991 · 1991
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Women in AI
Strok, D. 1992 · 1992
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Undergraduate Women in Computer Science: Experience, Motivation and Culture
Fisher, A.; Margolis, J.; and Miller, F. 1997 · 1997
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Insider/Outsider: Epistemological Privilege and Mothering Work
Griffith, A. I. 1998 · 1998
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Cultural Boundaries of Science: Credibility on the Line
Gieryn, T. F. 1999 · 1999
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Elitism in Mathematics and Inequality
Chang, H.-C. H.; and Fu, F. 2020 · 2002
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Recruitment and Retention of Women Graduate Students in Computer Science and Engineering: Results of a Workshop Organized by the Computing Research Association
Cuny, J.; and Aspray, W. 2002 · 2002
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An ACM-W Literature Review on Women in Computing
Gürer, D.; and Camp, T. 2002 · 2002
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The Risk of ‘Going Observationalist’: Negotiating the Hidden Dilemmas of Being an Insider Participant Observer
Labaree, R. V. 2002 · 2002
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The Study of Boundaries in the Social Sciences
Lamont, M.; and Molnár, V. 2002 · 2002
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Encouraging Women in Computer Science
Roberts, E. S.; Kassianidou, M.; and Irani, L. 2002 · 2002
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The Underrepresentation of Women in Engineering and Related Sciences: Pursuing Two Complementary Paths to Parity
Muller, C. B. 2003 · 2003
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Social Mobility and the Educational Choices of Asian Americans
Xie, Y.; and Goyette, K. 2003 · 2003
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Deterrents to Women Taking Computer Science Courses
Beyer, S.; Rynes, K.; and Haller, S. 2004 · 2004
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The Science Glass Ceiling: Academic Women Scientists and the Struggle to Succeed
Rosser, S. V. 2004 · 2004
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Towards Classification Parity Across Cohorts
Patel, A.; Gupta, R.; Harakere, M.; Krishna, S.; Alok, A.; and Liu, P. 2020 · 2005
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Beyond Small Numbers: Voices of African American PhD Chemists
Pearson, Jr., W. 2005 · 2005
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Women in Computer Science or Management Information Systems Courses: A Comparative Analysis
Beyer, S.; and DeKeuster, M. 2006 · 2006
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Why Students With An Apparent Aptitude for Computer Science Don’t Choose to Major in Computer Science
Carter, L. 2006 · 2006
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Just Get Over It or Just Get On With It: Retaining Women in Undergraduate Computing
Cohoon, J. M. 2006 · 2006
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Science and Social Inequality: Feminist and Postcolonial Issues
Harding, S. 2006 · 2006
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The Challenges of Insider Research in Educational Institutions: Wielding a Double-Edged Sword and Resolving Delicate Dilemmas
Mercer, J. 2007 · 2007
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Women in Interdisciplinary Science: Exploring Preferences and Consequences
Rhoten, D.; and Pfirman, S. 2007 · 2007
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The Space Between: On Being an Insider-Outsider in Qualitative Research
Dwyer, S. C.; and Buckle, J. L. 2009 · 2009
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Computer Science: The Incredible Shrinking Woman
Hayes, C. C. 2010 · 2010
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Why So Few? Women in Science, Technology, Engineering, and Mathematics
Hill, C.; Corbett, C.; and St Rose, A. 2010 · 2010
Cited alongside, same era.
Gender Differences at Critical Transitions in the Careers of Science, Engineering, and Mathematics Faculty
National Research Council of the National Academies. 2010 · 2010
Cited alongside, same era.
Senior technical women: A profile of success
Simard, C.; and Gilmartin, S. 2010 · 2010
Cited alongside, same era.
Race, Ethnicity, and NIH Research Awards
Ginther, D. K.; Schaffer, W. T.; Schnell, J.; Masimore, B.; Liu, F.; Haak, L. L.; and Kington, R. 2011 · 2011
Cited alongside, same era.
Engaging Women in Computer Science and Engineering: Promising Practices for Promoting Gender Equity in Undergraduate Research Experiences
Kim, K. A.; Fann, A. J.; and Misa-Escalante, K. O. 2011 · 2011
Cited alongside, same era.
The Double Bind: The Next Generation
Decolonizing, Indigenizing, and Learning Biskaaybiiyang in the Field: Our Oral History Journey
Srigley, K.; and Sutherland, L. 2018 · 2018
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Earnings of Academic Scientists and Engineers: Intersectionality of Gender and Race/Ethnicity Effects
Tao, Y. 2018 · 2018
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FactSheets: Increasing Trust in AI Services Through Supplier’s Declarations of Conformity
Arnold, M.; Bellamy, R. K. E.; Hind, M.; Houde, S.; Mehta, S.; Mojsilović, A.; Nair, R.; Natesan Ramamurthy, K.; Olteanu, A.; Piorkowski, D.; Tsay, J.; and Varshney, K. R. 2019 · 2019
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Fairness under Unawareness: Assessing Disparity When Protected Class Is Unobserved
Chen, J.; Kallus, N.; Mao, X.; Svacha, G.; and Udell, M. 2019 · 2019
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Techniques for Interpretable Machine Learning
Du, M.; Liu, N.; and Hu, X. 2019 · 2019
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Malcom, L. E.; and Malcom, S. M. 2011 · 2011
Cited alongside, same era.
Inside the Double Bind: A Synthesis of Empirical Research on Undergraduate and Graduate Women of color in Science, Technology, Engineering, and Mathematics
Ong, M.; Wright, C.; Espinosa, L.; and Orfield, G. 2011 · 2011
Cited alongside, same era.
Insider, Outsider, or Somewhere Between: The Impact of Researchers’ Identities on the Community-Based Research Process
Kerstetter, K. 2012 · 2012
Cited alongside, same era.
‘Feminist Theory is Proper Knowledge, But …’: The Status of Feminist Scholarship in the Academy
Pereira, M. d. M. 2012 · 2012
Cited alongside, same era.
Engineering Doctoral Degree Trend of Asian-American Women in the United States, 1994-2013
Tao, Y. 2015 · 2013
Cited alongside, same era.
Women in Academic Science: A Changing Landscape
Ceci, S. J.; Ginther, D. K.; Kahn, S.; and Williams, W. M. 2014 · 2014
Cited alongside, same era.
Cultural Stereotypes as Gatekeepers: Increasing Girls’ Interest in Computer Science and Engineering by Diversifying Stereotypes
Cheryan, S.; Master, A.; and Meltzoff, A. N. 2015 · 2015
Cited alongside, same era.
How Computer Science at CMU is Attracting and Retaining Women
Frieze, C.; and Quesenberry, J. L. 2019 · 2019
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Gebru, T.; Morgenstern, J.; Vecchione, B.; Vaughan, J. W.; Wallach, H.; Daumé, III, H.; and Crawford, K. 2019 · 2019
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Explaining Explainable AI
Hind, M. 2019 · 2019
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The Global Landscape of AI Ethics Guidelines
Jobin, A.; Ienca, M.; and Vayena, E. 2019 · 2019
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An Empirical Study of Rich Subgroup Fairness for Machine Learning
Kearns, M.; Neel, S.; Roth, A.; and Wu, Z. S. 2019 · 2019
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Explanation in Artificial Intelligence: Insights from the Social Sciences
Miller, T. 2019 · 2019
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Model Cards for Model Reporting
Mitchell, M.; Wu, S.; Zaldivar, A.; Barnes, P.; Vasserman, L.; Hutchinson, B.; Spitzer, E.; Raji, I. D.; and Gebru, T. 2019 · 2019
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Boundary-Work That Does Not Work: Social Inequalities and the Non-Performativity of Scientific Boundary-Work
Pereira, M. d. M. 2019 · 2019
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Gender and Race Intersectional Effects in the US Engineering Workforce: Who Stays? Who Leaves?
Tao, Y.; and McNeely, C. L. 2019 · 2019
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Trustworthy Machine Learning and Artificial Intelligence
Varshney, K. R. 2019 · 2019
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Open Platforms for Artificial Intelligence for Social Good: Common Patterns as a Pathway to True Impact
Varshney, K. R.; and Mojsilović, A. 2019 · 2019
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The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions
Whittlestone, J.; Nyrup, R.; Alexandrova, A.; and Cave, S. 2019 · 2019
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Living-Learning Community for Women in Computer Science at Rutgers
Wright, R. N.; Nadler, S. J.; Nguyen, T. D.; Sanchez Gomez, C. N.; and Wright, H. M. 2019 · 2019
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Explainable Machine Learning in Deployment
Bhatt, U.; Xiang, A.; Sharma, S.; Weller, A.; Taly, A.; Jia, Y.; Ghosh, J.; Puri, R.; Moura, J. M.; and Eckersley, P. 2020 · 2020
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Language (Technology) is Power: A Critical Survey of “Bias” in NLP
Blodgett, S. L.; Barocas, S.; Daumé, III, H.; and Wallach, H. 2020 · 2020
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You Shouldn’t Trust Me: Learning Models Which Conceal Unfairness From Multiple Explanation Methods
Dimanov, B.; Bhatt, U.; Jamnik, M.; and Weller, A. 2020 · 2020
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The Diversity–Innovation Paradox in Science
Hofstra, B.; Kulkarni, V. V.; Galvez, S. M.-N.; He, B.; Jurafsky, D.; and McFarland, D. A. 2020 · 2020
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The State of US Science and Engineering 2020
Khan, B.; Robbins, C.; and Okrent, A. 2020 · 2020
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The Term ‘Ethical AI’ is Finally Starting to Mean Something
Kind, C. 2020 · 2020
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Lessons from the PULSE Model and Discussion
Kurenkov, A. 2020 · 2020
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PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models
Menon, S.; Damian, A.; Hu, S.; Ravi, N.; and Rudin, C. 2020 · 2020
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Decolonial AI: Decolonial Theory as Sociotechnical Foresight in Artificial Intelligence
Mohamed, S.; Png, M.-T.; and Isaac, W. 2020 · 2020
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Promising Practices for Addressing the Underrepresentation of Women in Science, Engineering, and Medicine: Opening Doors
National Academies of Sciences, Engineering, and Medicine. 2020 · 2020
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Gender and Race Preferences in Hiring in the Age of Diversity Goals: Evidence from Silicon Valley Tech Firms
Parasurama, P.; Ghose, A.; and Ipeirotis, P. G. 2020 · 2020
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What a Machine Learning Tool that Turns Obama White Can (and Can’t) Tell Us About AI Bias
Vincent, J. 2020 · 2020
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On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? a {}^{{\textpmhg{\char 97\relax}}}
Bender, E.; Gebru, T.; McMillan-Major, A.; and Shmitchell, S. 2021 · 2021
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