REDUCINGTHEIMPACTOFBIASINTHESTEMWORKFORCE: 

STRENGTHENINGEXCELLENCEANDINNOVATION

A REPORT OF THE

Interagency Policy Group on Increasing Diversity in the 

STEM Workforce by Reducing the Impact of Bias 

 

November 2016
OFFICE OF SCIENCE AND TECHNOLOGY POLICY

EXECUTIVE OFFICE OF THE PRESIDENT

AND

OFFICE OF PERSONNEL MANAGEMENT

WASHINGTON, D.C. 20502

November 29, 2016

Dear Colleagues:

We are pleased to transmit to you the report, “Reducing the Impact of Bias in the STEM 

Workforce: Strengthening Excellence and Innovation.” The corresponding study was initiated in 

response to President Obama’s goal of broadening participation in science, technology, 

engineering, and mathematics (STEM) to meet workforce needs, draw on the best talent at all 

levels, and diversify the workforce. 

It was conducted by the Interagency Policy Group on Increasing Diversity in the STEM 

Workforce by Reducing the Impact of Bias, which is led by the Office of Personnel Management 

(OPM) and the White Office of Science and Technology Policy. 

The Interagency Policy Group comprises leaders from the Executive Office of the President 

(EOP) and Federal agencies, and is charged with addressing bias in the Federal workforce and in 

institutions of higher education that receive Federal funding for STEM. In the new report, the 

Group provides an inventory of the agencies’ current policies and practices to reduce the 

impact of bias; identifies the best policies and practices; offers recommendations for additional 

policies and practices to address bias in the agencies and at Federally funded institutions of 

higher education; and suggests actions to implement change. 

This report comes at a time when other Federal agencies, such as those in the national-security 

domain, are increasing their own exploration of best practices and creating new bodies of 

knowledge to draw upon in the collective quest to engage all Americans in solving the most 

pressing challenges of our time. Further, the private sector, particularly companies with large 

technical workforces, is also taking more deliberate and regular action around these issues. As 

knowledge of best practices around implementing solutions to mitigate bias expands, it is 

critical that those striving to craft a workforce drawn from all of America continue to share and 

improve on lessons from diversity and inclusion policies and practices.

Building on the many commendable efforts by the Federal Government, institutions of higher 

education, industry, professional societies, and others to broaden participation among groups 

historically underrepresented in STEM, the Nation must continue to increase its efforts to strive 

toward a STEM workforce that draws from the full diversity of the United States, including 

groups historically underrepresented—women and girls, many ethnic and racial groups, and 

people with disabilities. As noted in the October 2016 Presidential Memorandum on Promoting 

Diversity and Inclusion in the National Security Workforce, “research has shown that diverse 

groups are more effective at problem solving than homogeneous groups, and policies that 

promote diversity and inclusion will enhance our ability to draw from the broadest possible 
pool of talent, solve our toughest challenges, maximize employee engagement and 

innovation, and lead by example by setting a high standard for providing access to 

opportunity to all segments of our society.” To achieve fuller participation, the STEM 

enterprise must remain vigilant in mitigating explicit and implicit bias in order to provide 

opportunity to all.

The recommendations in the new report provide guidance for educating, enlarging, advancing, 

and diversifying a world-class STEM workforce in the Federal Government and Federally funded 

institutions of higher education, and for sharing lessons learned in the process. 

Sincerely,

John P. Holdren Beth F. Cobert

Assistant to the President for Science and Technology Acting Director

Director, Office of Science and Technology Policy Office of Personnel Management
i

ABOUT THE OFFICE OF SCIENCE AND TECHNOLOGY POLICY

The Office of Science and Technology Policy (OSTP) was established by the National Science and 

Technology Policy, Organization, and Priorities Act of 1976. OSTP’s responsibilities include advising 

the President in policy formulation and budget development on questions in which science and 

technology (S&T) are important elements; articulating the President’s S&T policy and programs; 

and fostering strong partnerships among Federal, state, and local governments, and the scientific 

communities in industry and academia. The Director of OSTP also serves as Assistant to the 

President for Science and Technology and manages the National Science and Technology Council 

(NSTC). More information is available at: www.whitehouse.gov/ostp.

ABOUT THE OFFICE OF PERSONNEL MANAGEMENT

The Office of Personnel Management (OPM) was established in 1978 as one of three organizations 

of the former Civil Service Commission. OPM oversees all policy created to support Federal human 

resources departments – from classification and qualifications systems to hiring authorities and 

from performance management to pay, leave, and benefits. Along with making those policies, 

OPM is responsible for ensuring they are properly implemented and continue to be adequately 

carried out. 

ABOUT THE INTERAGENCY POLICY GROUP ON INCREASING DIVERSITY IN THE STEM 

WORKFORCE BY REDUCING THE IMPACT OF BIAS 

In October 2015, OSTP and OPM established the Interagency Policy Group on Increasing Diversity 

in the STEM Workforce by Reducing the Impact of Bias and charged the Group to identify existing 

and propose new policies aimed at reducing the impact of explicit and implicit biases in the 

Federal and Federally funded STEM workforce, produce a report recommending Governmentwide policy options, and submit the report and recommendations for leadership approval. 

ABOUT THIS DOCUMENT

This document was developed by the Interagency Policy Group on Increasing Diversity in the STEM 

Workforce by Reducing the Impact of Bias. The document was published by OSTP.

ACKNOWLEDGEMENTS

We would like to thank the interagency group that worked over the past several months to 

produce this report. Key to the group’s work was the identification of agency efforts to reduce 

the impact of bias on the science, technology, engineering, and technology (STEM) workforce;

identification of evidence-based best and promising practices; generation of government-wide 

recommendations to mitigate the impact of bias; and development of a government-wide tool 

on bias mitigation. The report was developed under the direction of Wanda E. Ward, Assistant 

Director for Broadening Participation, OSTP. Contributors that provided significant support to 

the activities of the Interagency Policy Group include those who served on the report writing 

team, the best practices development sub-group, and the government-wide recommendations 

sub-group, and the dashboard development sub-group. 
ii

COPYRIGHT INFORMATION

This document is a work of the United States Government and is in the public domain (see 17 

U.S.C. §105). Subject to the stipulations below, it may be distributed and copied with 

acknowledgement to OSTP and OPM. Copyrights to graphics included in this document are 

reserved by the original copyright holders or their assignees and are used here under the 

Government’s license and by permission. Requests to use any images must be made to the 

provider identified in the image credits or to OSTP if no provider is identified. Printed in the 

United States of America, 2016. 
Increasing Diversity in the STEM Workforce by Reducing the Impact of Bias

iii

REPORT PREPARED BY

Office of Science and Technology Policy Office of Personnel Management

John P. Holdren Beth F. Cobert

Assistant to the President for Science and Acting Director

Technology and Director, Office of Science Office of Personnel Management

and Technology Policy

Jo Handelsman

Associate Director for Science

Office of Science and Technology Policy

Megan Smith

U.S. Chief Technology Officer

Office of Science and Technology Policy

INTERAGENCY POLICY GROUP ON INCREASI0NG DIVERSITY IN THE STEM 

WORKFORCE BY REDUCING THE IMPACT OF BIAS

Co-Chair Co-Chair

Wanda E. Ward Bruce Stewart

Assistant Director for Broadening Participation Deputy Director for Strategic Initiatives, 

Office of Science and Technology Policy Training, and Compliance,

Office of Diversity and Inclusion

Office of Personnel Management 

Members

Department of Agriculture

Sonny Ramaswamy

Department of Defense 

Kevin Kelly 

Diann McCants

Department of Education

Joiselle Cunningham

Christina Chhin

Department of Energy

David Klaus

La Doris Harris 

Department of Health and Human Services

Hannah A. Valantine 

H. Anna Han

Department of Homeland Security

Alitza Vega

Nimesh M. Patel

Department of the Interior

Suzette Kimball 

Department of Labor

Pronita Gupta 

Department of Transportation 

Stephanie Jones

Environmental Protection Agency

Donna Vizian

Lynnann Hitchens
Increasing Diversity in the STEM Workforce by Reducing the Impact of Bias

iv

National Aeronautics and Space 

Administration

Brenda Manuel 

National Science Foundation

Suzanne C. Iacono

National Oceanic and Atmospheric 

Administration, a Bureau of the Department of 

Commerce

Marlene Kaplan

Patent and Trademark Office, a Bureau of the 

Department of Commerce

Bismarck Myrick

Jamie Simpson

Smithsonian Institution

James D. Douglas

Office of Personnel Management

Sharon Wong

Domestic Policy Council

Roy Austin 

Irene Hsu 

Equal Employment Opportunity Commission

Dexter Brooks
Increasing Diversity in the STEM Workforce by Reducing the Impact of Bias

v

Best Practices Development Sub-group 

Bernice Anderson, National Science Foundation

Christina Chhin, Department of Education

Joiselle Cunningham, Department of Education

Amber Haggins, Department of Energy

Anna Han, National Institutes of Health, Department of Health and Human Services

Suzanne C. Iacono, National Science Foundation (Sub-group Lead)

Marlene Kaplan, National Oceanic and Atmospheric Administration, a Bureau of the Department of 

 Commerce

Bismarck Myrick, United States Patent and Trademark Office, a Bureau of the Department of Commerce

F. Michael Sena, Department of Defense

Government-Wide Recommendations Sub-Group

Ray Campbell, United States Geological Survey, Department of the Interior

David Chambers and Sharon Wagner, National Aeronautics and Space Administration

Brenda R. Manuel, National Aeronautics and Space Administration (Sub-Group Lead)

Nimesh M. Patel, Department of Homeland Security

Dashboard Development Sub-Group

Ray Campbell, United States Geological Survey, Department of the Interior

Anna Han, National Institutes of Health, Department of Health and Human Services

Bruce Stewart, Office of Personnel Management (Sub-Group Lead)

Report Writing Team

Bernice Anderson, National Science Foundation

David Chambers, National Aeronautics and Space Administration

Andrew Hanus, OSTP Fellow

Suzanne E. Iacono, National Science Foundation

Brenda R. Manuel, National Aeronautics and Space Administration

Osaremen Okolo, OSTP Intern

Sana Surani, OSTP Intern

Sharon Wagner, National Aeronautics and Space Administration

Wanda E. Ward, OSTP (Writing Team Lead)
Increasing Diversity in the STEM Workforce by Reducing the Impact of Bias

vi

TABLE OF CONTENTS

Executive Summary 1

Chapter I. The Benefits of Diversifying the STEM Workforce 5

A National Imperative 5

The National Demographic Landscape 8

The Federal Workforce Demographic Landscape 9

Recent Congressional Concerns 10

Chapter II. Bias: Impacts, Metrics, and Mitigation 12

 Impacts of Bias 12

 Metrics for Assessing Bias 14

 Practices that Mitigate the Effects of Bias 14

The Corporate Experience 18

 The Academic Experience 19

Chapter III. Federal Leadership Role 21

Federal Accomplishments to Date in Addressing Bias 21

Future Federal Priorities 21

OSTP - OPM Charge and Interagency Policy Group Response 22

Vision, Goal, and Objectives 23

Guiding Principles 23 

Chapter IV. Best, Promising, and Emerging Practices 24

 Practices for Reducing the Impact of Bias in the Federal STEM Workforce 25

Federal - Academic Interface: Research, Development, and Interventions on Bias 33

Chapter V. Recommendations and Implementation 39

Recommendations 39

Implementation and Next Steps 44

Appendices 45 

A. Summaries of Agency Reports on Efforts to Mitigate the Impact of Bias 45

 B. Resources for Reducing the Impact of Bias 47

Abbreviations 51
Increasing Diversity in the STEM Workforce by Reducing the Impact of Bias

1

EXECUTIVE SUMMARY

There is enormous opportunity to enrich the science, engineering, technology, and mathematics 

(STEM) enterprise—and the innovations and solutions it produces—by increasing diversity of the 

human resources on which STEM relies at all levels of participation and leadership. Recognizing 

the potential contributions of people from all backgrounds in STEM is imperative on scientific, 

workforce, economic, and moral grounds.

America’s role as a global leader in innovation and equity will be fortified by tapping into the 

unparalleled diversity of the American people. Efforts to draw on a diverse community in STEM, 

however, have been undermined, at least in part, by systemic barriers. Prominent among these 

are both implicit and explicit biases1 generated by stereotypes and expectations of how a STEM 

professional should look and act, which can lead to lack of opportunities and access to a better 

education and careers. The United States must continue to address the biases that act to

constrain the diversity of the STEM workforce; there is a “fierce urgency of now”2 to do so in 

order to maintain the Nation’s competitiveness in science, technology, and innovation.

Evidence shows that it is very difficult to change implicit or unconscious biases, but it is possible 

to change behaviors that are shaped by biases. Most people want to be fair, and, if they are 

made aware of their biases, many will use cognitive processes to prevent biases from shaping 

their decisions and behaviors.3, 4 Over the last decade, nationwide efforts to enhance diversity 

have been predicated on the hypothesis that implicit biases are ubiquitous and unlikely to 

change, but training and awareness can and has reduced the impact of these biases. Accordingly, 

this report focuses on actions to raise awareness about bias and reduce its impact in Federal 

agencies and the institutions of higher education that they fund. Similarly, the metrics used to 

assess progress will focus on outcomes—increased diversity in hiring, retention, and promotion 

in the STEM workforce—not on reduction of biases themselves.

In addressing the national imperative for diversity in the STEM workforce, in October 2015, the 

White House Office of Science and Technology Policy (OSTP) and the Office of Personnel 

Management (OPM) established an Interagency Policy Group to identify policies and practices to 

 

1 Use of the terms “bias” or “mitigating or reducing the impact of bias” should be understood to mean “where such biasexists” 

throughout this report.

2 Martin Luther King, Jr., Why We Can’t Wait, (New York: Harper & Row, 1963); Martin Luther King Jr., “I Have a Dream,” 

 (Washington DC, 1963).

3 Lai, C.K., Marini, M., Lehr, S.A., Cerruti, C., Shin, E.L., Joy-Gaba, J., Ho, A.K., Teachman, B.A., Wojcik, S.P., Koleva, S., Frazier, 

R.S., Heiphetz, L., Chen, E., Turner, R.N. Haidt, J., Kesebir, S., Hawkins, C.B., Sartori, G., Schaefer, H.S., Rubichi, S., Dial, 

C.M.,Sriram, N., Banaji, M.R., Nosek, B.A. (2014) Reducing implicit racial preferences: I. A comparative investigation of 17 

interventions. Journal of Experimental Psychology 143: 1765-1785. 4 Carnes, M., Devine, P. G., Manwell, L. B., 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 90:221-230. 
Increasing Diversity in the STEM Workforce by Reducing the Impact of Bias

2

increase diversity in the STEM workforce by reducing the impact of bias, both in the Federal 

Government and in Federally funded institutions of higher education. The interagency group 

developed a vision, a goal, objectives, and basic principles to guide its efforts to: inventory

current policies and practices; identify best and promising practices; develop recommendations

for Government-wide policies and practices; and suggest strategies and next steps. The

recommendations and implementation strategy follow here. The rest of the findings are 

presented in the main report. 

In the Federal STEM Workforce

Recommendation 1. Each Federal agency should exercise leadership at all levels, including senior 

officials, STEM-program and administrative managers, human-capital officials, and diversity and 

inclusion officials (or their equivalent), to reduce the impact of bias in their internal operations 

through:

• Incorporation of the agency’s diversity and inclusion objectives in its strategic plan; 

• Visible participation, deep engagement, and demonstrated accountability by agency and 

department leaders in the dialogue and activities to increase diversity; 

• Implementation of an organizational cycle of recruiting, hiring, and promotion practices 

that encourages diversity and inclusion, in part by reducing the impact of bias; 

• Engagement and empowerment of employees through policies, practices, and 

programmatic activities across all groups, including managers; 

• Expanded research-based education and training on implicit- and explicit-bias mitigation; 

and

• Establishment of bias-mitigation goals, techniques, and accountability mechanisms.

In Federally Funded Institutions of Higher Education

Recommendation 2. Each Federal agency should incorporate bias-mitigation strategies into its 

proposal-review process and offer technical assistance to grantee institutions to implement biasmitigation strategies. Such strategies and activities should include:

• Emphasizing to the academic community the importance of using bias-mitigation 

strategies to achieve fairness and quality in the STEM endeavor;

• Ensuring diversity in membership of grant-review panels to include representation of all, 

including women, underrepresented racial and ethnic groups, and people with 

disabilities; 

• Establishing a systematic means of collecting and analyzing data on the entire cycle of the 

grant-making process to analyze success rates in getting grants across groups; 
Increasing Diversity in the STEM Workforce by Reducing the Impact of Bias

3

• Providing grantees with information about methods to reduce the impact of bias and 

enhance diversity and inclusion in their research groups and institutions; and

• Collecting best practices from grantee institutions and sharing them among agencies 

and other grantees, including by supporting communities of practice.

Cross-cutting Government Leadership—STEM Workforce and Federally Funded 

Institutions of Higher Education

Recommendation 3. OSTP, OPM, and the Department of Justice (DOJ), as appropriate, should 

exercise leadership to reduce the impact of bias in the Federal STEM workforce and Federally

funded institutions of higher education by:

• Serving as focal points, clearinghouses, and distribution points for bias-reduction 

strategies and best practices for both Federal agencies and Federally funded institutions 

of higher education to reduce the impact of bias; 

• Coordinating civil-rights-compliance efforts; 

• Providing guidance to agencies related to performance and accountability in efforts to 

mitigate the impact of explicit and implicit bias (where it exists) by investigation of 

potential measurement tools;

• Promoting greater strategic coordination, sharing, and collaborating on successful 

programs aimed at reducing the impact of bias and increasing diversity in Federally

funded institutions of higher education; and

• Strengthening university-community partnerships to mitigate the impact of bias and to 

increase access to Federal STEM employment.

Implementation and Next Steps 

To implement the recommendations, the Interagency Policy Group proposes the following

actions. 

• An interagency body, acting as a community of practice and comprising staff drawn from 

OPM and Federal agencies, should coordinate and review the Government-wide 

implementation and scaling of best and promising practices; develop principles for 

appropriate local adaptation of practices; identify gaps; develop a tool, including a set of 

quantitative metrics and strategies, to track increased diversity in the Federal STEM 

workforce by the reduction of the impact of bias where it may exist; catalyze use of bestpractices such as peer-to-peer learning and innovation methods through regular 

engagement with the community of practice; and develop a comprehensive, living 

inventory of policies and practices by the Federal Government and Federally funded
Increasing Diversity in the STEM Workforce by Reducing the Impact of Bias

4

institutions of higher education that reduce the impact of bias in the STEM workforce. 

This body should develop targeted timelines for implementation and completion for each 

of these proposed actions, include those mentioned below.

• OPM and Federal agencies should implement a public-engagement campaign, 

strategically targeted to key stakeholders, to highlight the existence, challenges, and 

impacts of bias and ways to reduce it in the STEM workforce.

• Federal agencies should develop plans to implement and institutionalize policies and 

practices for reducing the impact of bias on the education, employment, and 

advancement of members of groups historically underrepresented or underserved in 

STEM fields. Such policies and practices should be designed to make careers in STEM 

more attractive and conducive to the success of all people, including women, members 

of underrepresented ethnic and racial groups, and people with disabilities. Agency plans 

should be best-practice driven, include measurable goals, and be published in easy-tounderstand forms to be reviewed by OPM on an annual basis. 

• Federal agencies, institutions of higher education, and the proposed interagency

implementation body should develop accountability measures that can be used to assess 

progress and should review ongoing research to accelerate progress in the emerging 

fields of bias mitigation, diversity, and inclusion policies and practices; best practices 

emerging from research should also be brought back into agencies to accelerate 

implementation of new research-based tools and strategies. 
Increasing Diversity in the STEM Workforce by Reducing the Impact of Bias

5

CHAPTER I. THE BENEFITS OF DIVERSIFYING THE STEM WORKFORCE

A National Imperative

To confront the global challenges of the 21st century, the Nation requires a STEM workforce that 

is sufficiently large and innovative to meet the current and future needs of academia, 

government, and the private sector. The rapid pace of science and technology (S&T) discovery 

and innovation has created a sense of possibility—indeed, an expectation—within the research

community and the general public that world-changing discoveries are almost in hand on many 

S&T fronts. At the same time, without further intentional action, the lack of human diversity in 

many S&T fields will likely constrain America’s role as a preeminent leader in the global economy. 

As noted in the October 2016 Presidential Memorandum on Promoting Diversity and Inclusion in 

the National Security Workforce, “research has shown that diverse groups are more effective at 

problem solving than homogeneous groups, and policies that promote diversity and inclusion will 

enhance our ability to draw from the broadest possible pool of talent, solve our toughest 

challenges, maximize employee engagement and innovation, and lead by example by setting a 

high standard for providing access to opportunity to all segments of our society.” The enormity 

of the opportunity for America to lead a new burst of discovery and invention is matched by a 

singular opportunity to achieve STEM workforce diversity. 

In this report, diversity refers to representation of people who are members of gender and ethnic 

groups that historically have been underrepresented in STEM—including women, African 

Americans, Native Americans, Hispanics, Pacific Islanders, and Asian Americans—and people with 

disabilities. The intent, however, is to implement practices that benefit all groups, including

groups who are underrepresented and may experience bias because of their sexual orientation, 

educational attainment, socioeconomic status, gender identity, and geographic location within 

the United States. Inclusion refers to the fostering of a work culture where uniqueness of beliefs, 

backgrounds, talents and capabilities, and ways of living is valued and leveraged for learning and 

informing decision making.

Explicit bias entails intentional and consciously held attitudes and beliefs that influence people’s 

evaluation of and behavior toward a particular group. This kind of bias is overt, deliberate, and 

can be self-reported. Implicit bias, on the other hand, is applied without consciousness or 

control, and is thus more challenging to detect and mitigate. Critically, implicit bias can occur 

both at an individual level, for example, reduced interpersonal warmth when interviewing a 

minority job candidate,

5 as well as at an institutional level, for example, holding meetings at times 

that conflict with childcare pick-up times. There is substantial evidence that these types of biases 

 5 Carl O. Word, Mark P. Zanna, and Joel Cooper, “The Nonverbal Mediation of Self-Fulfilling Prophecies in Interracial 

 Interaction,” Journal of Experimental Social Psychology 10 (1974): 109–12.
Increasing Diversity in the STEM Workforce by Reducing the Impact of Bias

6

often operate without conscious intention and yet create sizeable hurdles for members of 

negatively stereotyped groups.

America must accelerate efforts to engage, inspire, develop, and advance diverse talent in STEM 

fields, especially by reducing the impact of bias where it exists. Several imperatives in particular 

converge to create a national imperative to make the STEM workforce diverse:

• Science and Innovation Imperative. A robust body of scientific studies, demonstrating 

both correlation and causation,shows that cognitive diversity within groups enables them 

to be more creative and generate higher quality or more defensible decisions, particularly 

in uncertain and novel situations, such as the pursuit of science and innovation. 6,7

 The 

wider range of expertise, ideas, experiences, and perspectives provided by diverse groups 

is particularly beneficial in innovation, problem-solving, and prediction. This is made 

evident through studies in various settings. For example, a recent analysis of 2.5 million 

papers from U.S. research groups found that publications generated by more ethnically 

diverse research teams were more likely to be published in higher-impact journals and 

cited 5 to 10 percent more than those produced by more homogeneous teams.8 

• Workforce Imperative. The U.S. workforce needs the best STEM talent and more STEM 

college graduates. The demand for STEM workers is expected to outpace STEM college 

degree production by about 1 million by 2022.9

 Several national studies and reports10

have argued that students need to be attracted to and retained in STEM pathways and 

should be drawn from all demographic groups to ensure utilization of the best talent. As 

the proportion of women and underrepresented ethnic and racial groups in the U.S. 

college population increases—currently approaching 70 percent—there is an increasing 

cost to not recruiting more members of these communities to the STEM workforce. The 

United States cannot afford to “leave much of the best talent on the bench.”11

• Economic Imperative. Research shows that diverse teams outperform homogeneous 

teams when it comes to financial returns.12 While neither government nor nonprofit 

institutions of higher education are pursuing profits, the same contributing factors to 

 6 Laurel Smith-Doerr, Sharla Alegria, and Timothy Sacco describe “cognitive diversity” as involving “training, ideas, and skills 

 relevant to completing the task” in Laurel Smith–Doerr, Sharia Alegria, and Timothy Sacco, “How Diversity Matters in the U.S. 

 Science and Engineering Workforce: A Critical Review Considering Teams, Fields, and Organizational Contexts.” Under review

 for publication by Engaging Science, Technology, and Society, (2016). 7 Aparna Joshi and Hyuntak Roh, “The Role of Context in Work Team Diversity Research: A Meta-Analytic Review.” Academy 

 of Management Journal 52 (2009): 599–627; Scott E. Page, Diversity and Complexity, (Princeton, NJ: Princeton University 

 Press, 2010). 8 Richard B. Freeman and Wei Huang, “Collaboration: Strength in Diversity,” Nature 513 (2014): 305. 9 Executive Office of the President, President’s Council of Advisors on Science and Technology, “Report to the President,

 Engage to Excel: Producing One Million Additional College Graduates with Degrees in Science, Technology, Engineering, and 

 Mathematics.” (2012); 10 Ibid; National Academies of Science, “Expanding Underrepresented Minority Participation: America’s Science and 

 Technology Talent at the Crossroads,” (2011). 11 Remarks by the President at the White House Science Fair, 2016.

12 Vivian Hunt, Dennis Layton, and Sara Prince, “Why Diversity Matters,” Our Insights, McKinsey & Company, (2015), 

http://www.mckinsey.com/business-functions/organization/our-insights/why-diversity-matters
Increasing Diversity in the STEM Workforce by Reducing the Impact of Bias

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these success metrics, such as the ability to attract and retain top talent, and improved 

customer or constituent orientation, suggest that many private-sector benefits of 

diversity would accrue to these stakeholders as well. 

• First Principles Imperative. The Federal-merit-system principles, which are a foundation 

for the civil service, provide that agencies should recruit, for their workforce, qualified 

individuals drawn from all segments of society. 

• Moral Imperative. Drawing the workforce from a diverse cross-section of Americans is 

the right thing to do. To advance the most fundamental American value of fairness, it is 

essential to provide equal opportunities for all members of society, including women,

ethnic and racial groups, and people with disabilities to enter STEM fields, realize their 

potential, and experience the personal and financial rewards that accompany STEM 

careers. 

In sum, the evidence demonstrates that diversity strengthens group and organization 

performance,

13 and is itself a force for innovation.14 Furthermore, research also shows that 

diversity and inclusion increase people’s performance in achieving organizational goals, retention 

rates, and emotional commitment to colleagues. Diversity and inclusion also provide multiple 

perspectives that improve problem-solving and creativity, reduce conformity, and raise team 

intelligence by increasing social intelligence.15

There is also a statistically significant positive relationship among gender, racial, and ethnic 

diversity and company financial performance.

16 This finding extended to company leadership as 

well. Based on financial data and leadership demographics from hundreds of organizations and 

thousands of executives in Canada, Latin America, the United Kingdom, and the United States, 

researchers found that companies in the top quartile of gender diversity were 15 percent more 

likely to have financial returns above the national industry mean, and those that were in the top 

quartile of racial and ethnic diversity were 35 percent more likely.

17 Comparison across countries 

revealed that in the United States efforts to increase racial and ethnic diversity have a stronger 

impact on financial performance than those addressing gender diversity, possibly, because of the 

already accumulated positive results for gender. No industry or company studied was found to 

be in the top quartile for both race and ethnic diversity and gender diversity. Researchers also 

assert that achieving organizational transformation along diversity considerations is difficult

because of barriers like unconscious bias, making it all the more important that companies 

 13 Scott E. Page, The Difference: How the Power of Diversity Creates Better Groups, Firms, Schools, and Societies, (Princeton, NJ: 

 Princeton University Press, 2007). 14 Scott E. Page, “Diversity Powers Innovation,” Center for American Progress (2007),

 http://www.americanprogressorg/issues/economy/news/2007/01/26/2523/diversity-powers-innovations/ 15 David Rock and Heidi Grant Halvorson, “Breaking Workplace Bias at Its Source,” NeuroLeadership Institute, (2015), 

 https://neuroleadership.com/wp-content/uploads/2015/03/Breaking-Workplace-Bias-at-its-Source_Rock_17Mar15.pdf 16 Company financial performance measured as average earnings before interest and taxes.

17 Vivian Hunt, Dennis Layton, and Sara Prince, “Diversity Matters,” McKinsey & Company (2015).
Increasing Diversity in the STEM Workforce by Reducing the Impact of Bias

8

implement programs that explicitly address unconscious bias with visible commitment from 

leadership. 

Additional research delineates further the conditions under which diversity promotes innovation, 

creative problem-solving, and greater productivity. New research has summarized studies that

indicate the mechanisms and boundary conditions by which diversity affects the performance of 

teams. These studies found that when conditions are right for diversity to be beneficial, that is, 

in equitable and integrated work environments, it leads to improved creativity, innovation, 

productivity, and reputation. 18

The need for greater diversity, fuller inclusion, and bias mitigation in STEM extends to the 

international level.

19 Many countries expect the United States to be a model from which they can 

learn how to promote and manage diversity. At the 2016 G7 Summit in Japan, President Obama 

and other G-7 leaders committed to advancing women’s empowerment and gender equality.20 At 

the meeting of the G7 Science Ministers, Japan initiated discussion—and looked to the United 

States as a source of experience and knowledge—about policies that eliminate gender disparities 

and reduce stereotypes, biases, and other barriers to education and careers for women in STEM 

fields.21 These barriers will be discussed further in Chapter II.

The National Demographic Landscape

Profiles of the U.S. population and the STEM workforce reveal a disconnect: the general U.S. 

population is becoming more diverse far more rapidly than the STEM workforce. Reversing this 

trend is a national imperative. In 2012, a significant proportion of the U.S. population between 

the ages of 18 and 64 comprises groups that are not well-represented in STEM fields. This 

population includes women (approximately 51 percent) and a number of ethnic and racial 

groups, including Hispanics (approximately 16 percent), African Americans (approximately 13

percent), resident Asians (approximately 6 percent), and other non-white racial and ethnic 

groups combined (approximately 2 percent).22 Collectively, these ethnic and racial groups, 

currently 37 percent of the U.S. population, are projected to reach 57 percent of the U.S. 

population by 2060.23

 18 Laurel Smith–Doerr, Sharia Alegria, and Timothy Sacco, “How Diversity Matters in the U.S. Science and Engineering 

 Workforce: A Critical Review Considering Teams, Fields, and Organizational Contexts.” Under review for publication by 

 Engaging Science, Technology, and Society, (2016). 19 “Diversity Fueling Excellence in Research and Innovation: A Roadmap for Action for North America,” (Gender Summit 3: 

 North America, 2013). 20 FACT SHEET: The G-7 Summit at Ise-Shima Japan,

 https://www.whitehouse.gov/the-press-office/2016/05/27/fact-sheet-g-7-summit-ise-shima-japan. 21 “Tsukuba Communique: G7 Science and Technology Ministers’ Meeting in Tsukuba, Ibaraki,” May 2016, 

 http://www8.cao.go.jp/cstp/english/others/20160517communique.pdf. 22 National Science Foundation, “Women, Minorities and Persons with Disabilities in Science and Engineering,” National Center

 for Science and Engineering Statistics, www.nsf.gov/statistics/2015/nsf15311/. 23 U.S. Department of Commerce Census Bureau 2014 National Projections.
Increasing Diversity in the STEM Workforce by Reducing the Impact of Bias

9

Yet participation and advancement of groups historically underrepresented in STEM continue to 

lag behind their representation in the overall population. Consider the following statistics about 

the collegiate, STEM degree, and STEM workforce populations: 24

• Underrepresented groups, especially Hispanics/Latinos, are an increasing fraction of

undergraduate students while whites are a decreasing fraction, and more women than 

men across all groups are enrolling in college.

• Although women earn 57 percent of all bachelor’s degrees and approximately 

50 percent of all science and engineering (S&E) degrees, their participation varies across 

S&E fields. Women’s participation is lowest in engineering, at approximately 22 percent 

at the doctor-of-philosophy ( Ph.D.) level and 19 percent at the bachelor-of-science (BS) 

level in 2012, and computer science, at approximately 21 percent at the Ph.D. level and 

18 percent at the B.S. level in 2012, with their participation in computer-science majors 

decreasing in recent years.

• The gap in educational attainment that separates underrepresented ethnic minorities 

from whites and Asians remains wide, despite considerable progress in closing this gap 

over the past two decades, with underrepresented groups generally being less likely than 

whites and Asians to graduate from high school, enroll in college, and earn college STEM 

degrees.

• Women and other historically underrepresented groups are a smaller proportion of the 

overall S&E workforce than of degree recipients who recently joined the workforce

(because older cohorts of S&E workers are disproportionately male and white or Asian); 

and people with disabilities are also underrepresented in this workforce compared with 

the overall college-educated population.

• Women’s participation in the academic doctoral workforce has increased considerably 

over the past 20 years, while advancement for underrepresented minorities has been 

slower, with the rates for both women and underrepresented minorities in the academic 

workforce continuing to differ from those of their male and white or Asian counterparts 

in rank, tenure, salary, and Federal support.

The Federal Workforce Demographic Landscape

While certain groups continue to be underrepresented in the U.S. Federal workforce, it is more 

diverse than at any time in U.S. history. Hispanics, women, and persons with disabilities were

underrepresented across the Federal workforce compared to the Civilian Labor Force (CLF) in 

 24 National Science Foundation, “Women, Minorities and Persons with Disabilities in Science and Engineering, ” National Center

 for Science and Engineering Statistics, www.nsf.gov/statistics/2015/nsf15311/.
Increasing Diversity in the STEM Workforce by Reducing the Impact of Bias

10

fiscal year (FY) 2015—8 percent versus 15 percent of the CLF for Hispanics25, 43 percent versus 

46 percent for women, and people with disabilities remaining underrepresented overall.26

The Federal STEM permanent workforce has seen some gains in racial and ethnicity diversity over 

the last 5 years. From FY 2011 to FY 2015, African Americans increased from 9.0 percent to 9.5

percent, Asian Americans from 8.7 percent to 9.2 percent, Native Hawaiians or Pacific Islanders 

from 0.2 percent to 0.3 percent, Hispanics from 5.0 percent to 5.5 percent, and those with more 

than one race increased from 0.8 percent to 1.3 percent. American Indians or Alaska Natives had 

a slight decrease from 1.0 percent to 0.9 percent with women also showing a slight decrease 

from 25.8 percent of the Federal STEM occupations to 25.6 percent.

27

Recent Congressional Concerns

A compelling demonstration of the urgent need to address barriers such as bias in STEM fields is 

revealed in a recent report on women in STEM research positions by the Government

Accountability Office (GAO), a fact-finding arm of Congress.

28 The December 2015 GAO report

examined differences in Federal funding received by women and men in STEM fields, and the 

extent to which Federal agencies enforce Title IX at the educational institutions at which they 

fund STEM research.

29 

GAO reported the following: 

• Among the six Federal agencies examined for research awards made between FY 2009 

and FY 2013, no disparities in success rates in receiving awards between women and men

were found at the National Institutes of Health (NIH), Department of Health and Human 

Services (HHS); National Science Foundation (NSF); or National Institute of Food and 

Agriculture (NIFA), United States Department of Agriculture (USDA). 

• Limitations of available data narrowed insights into success rates at the remaining three 

agencies that the GAO considered, including: the Department of Defense (DOD), 

Department of Energy (DOE), and National Aeronautics and Space Administration (NASA). 

 25 Although Hispanics, taken as a whole, make up approximately 14 percent of the CLF, that number drops to approximately 11 

 percent of the CLF when only U.S. citizens (including those in Puerto Rico) are counted. Citizenship is a requirement for 

 most Federal positions. Citizenship-based CLF calculations are based on the Equal Employment Opportunity (EEO) 

 Tabulation of 5-year ACS data. 26 United States Office of Personnel Management Office of Diversity and Inclusion, “The Current State of Diversity and Inclusion 

in the Federal Workforce,” (2014).

27 United States Office of Personnel Management’s Enterprise Human Resources Integration Statistical Data Mart (EHRI-SDM).

28 Government Accountability Office, “WOMEN IN STEM RESEARCH: Better Data and Information Sharing Could Improve 

 Oversight of Federal Grant-making and Title IX Compliance,” GAO-16-14, December 2015,

 http://www.gao.gov/products/GAO-16-14. 29 Title IX of the Education Amendments Act of 1972 states: “No person in the United States shall, on the basis of sex, be 

 excluded from participation in, be denied the benefits of, or be subjected to discrimination under any education program or 

 activity receiving Federal financial assistance.”
Increasing Diversity in the STEM Workforce by Reducing the Impact of Bias

11

• Inadequate data limited the evaluation of STEM program performance against stated 

goals of funding the best scientists independent of gender. Such limitations obscure bias,

where it may exist, in the grant-making system.

• Of the six departments and agencies reviewed, DOD and HHS were found to be 

noncompliant in conducting Federally required Title IX reviews. Officials in many DOD 

grant-making components reported that they were unaware of the requirement to 

conduct compliance reviews, lacked departmental guidance on this responsibility, and 

were not aware of any DOD-wide directive to conduct such periodic reviews. The second 

agency found non-compliant, HHS, reported that it focused its civil-rights enforcement on 

its primary or sole jurisdiction of civil-rights protections in healthcare settings rather than 

Title IX compliance reviews of NIH grantees. 

• Five of the six departments and agencies suggested that the Department of Justice (DOJ) 

should facilitate interagency sharing of best practices for Title IX compliance to increase 

coordination, improve compliance reviews across agencies, and reduce the impact of any 

bias in the STEM enterprise. 
Increasing Diversity in the STEM Workforce by Reducing the Impact of Bias

12

CHAPTER II. BIAS: IMPACTS, METRICS, AND MITIGATION 

Impacts of Bias

Biases, whether explicit or implicit, are prejudices that can create barriers for members of 

negatively stereotyped groups. Explicit biases are conscious and acknowledged by the person 

applying them, for example, “We shouldn’t hire her because she’s a woman, and women just go 

off and have babies,” or “We shouldn’t make him an offer because he’s from the South, and he’ll 

never want to live in a cold environment like ours.” Implicit biases are usually unintended and 

unconscious, but nonetheless are powerful influences on human behavior. In situations such as 

hiring decisions, these biases are frequently accompanied by a pretextual justification for the

choice, which may seem legitimate but, in fact, masks the preference for a member of a particular 

demographic group. For example, in one study in which evaluators were asked to choose 

between candidates, evaluators more often chose the male candidate and justified their choice 

by pointing to a strong trait reflected in his resume. When the resumes were reversed, however, 

evaluators were still more likely to choose the male candidate and identified a different criterion 

as the basis of their decision.30 Across a range of different occupations, research shows that the 

racial, ethnic, or gender group that traditionally has been affiliated with that profession will be 

favored—e.g., men in STEM fields or women in nursing. In academic settings, randomized 

controlled experiments show that STEM faculty are less likely to hire, mentor, or even respond 

to emails from women and minorities.31

Implicit biases are even held and applied by well-meaning people who are consciously supportive 

of the targets of their bias. For example, studies find that parents underestimate their daughters’ 

math abilities compared to that of sons with the same performance scores;

32 and teachers often 

assume that white boys are better at computer science than girls or students of color.33 In 

addition to distorting evaluations by strangers, family members, teachers, and employers, 

implicit biases are also powerful influences on children’s and adults’ evaluations of themselves. 

Many studies show that people are much less likely to aspire to a professional role that they have 

never seen filled by someone of their own demographic group.34 An individual’s estimation of 

 30 Eric Luis Uhlmann and Geoffrey L. Cohen, “Constructed Criteria: Redefining Merit to Justify Discrimination,” Psychological

 Science 16 (2005): 474-480. 31 Corinne A. Moss-Racusin, John F. Dovidio, Victoria L. Brescoll, Mark J. Graham, and Jo Handelsman, "Science Faculty’s Subtle

 Gender Biases Favor Male Students," Proceedings of the National Academy of Sciences 109, no. 41 (2012): 16474-16479;

 Katherine L. Milkman, Modupe Akinola, and Dolly Chugh, “What Happens Before? A Field Experiment Exploring How Pay 

 and Representation Differentially Shape Bias on the Pathway into Organizations,” Journal of Applied Psychology 23 (2014): 

 710-717. 32 Doris K. Yee and Jacquelynne S. Eccles, “Parent Perceptions and Attributions for Children's Math Achievement,” Sex Roles

 19 (1988): 317–333. 33 Victor Lavy and Edith Sand, “On The Origins of Gender Human Capital Gaps: Short and Long Term Consequences of Teachers' 

 Stereotypical Biases,” National Bureau of Economic Research, http://www.nber.org/papers/w20909. 34 Albert Bandura, Social Learning Theory, (Englewood Cliffs, N.J.: Prentice Hall, 2007). 
Increasing Diversity in the STEM Workforce by Reducing the Impact of Bias

13

his or her own abilities, moreover, is shaped by feedback from others—particularly teachers, 

mentors, and supervisors—and by the people he or she sees designated as experts in that pursuit,

both in life and via entertainment media. 

Institutional or structural bias is usually a manifestation of either explicit or implicit biases held 

in the past. Both tradition and lack of awareness have often led to perpetuation of these biases, 

such as denying admission to a particular group, scheduling that is inconvenient for one group 

within the community, images of leaders that do not include women or minorities, and facilities 

that do not accommodate wheelchairs. Even where inadvertent, these vestigial institutional 

behaviors can continue to exert powerful pressures against members of certain groups in the 

present. 

Explicit, implicit, and structural biases do not operate independently, and all need to be 

addressed. Often these biases interact in an iterative manner with implicit biases determining 

an initial, spontaneous reaction that either may be altered with deliberate effort or reinforced 

by explicit or structural biases.

35

Individuals can be made aware of their implicit biases and guard against their influence, but doing 

so requires effort, time, and motivation.36 Studies show that individuals are particularly 

vulnerable to the influence of their own biases when they are tired or cognitively stressed.37 Yet 

the effort required to address biases is a worthwhile endeavor because of the positive impact 

diversity has on increasing economic outcomes for individuals and organizations alike. 

Experimental evidence from STEM disciplines shows a causal relationship between bias and 

diminished opportunities for some groups, and shows that addressing bias can increase hiring of

and improve the environment for underrepresented groups.

38 If the effects of bias are not 

reduced, they will act as counterweights to other diversity efforts. Actions to reduce bias will 

amplify the impact of investments in STEM teachers, schools, and communities. Without these 

actions, countervailing forces will include students’ lack of confidence and aspirations in STEM; 

unfair biases in hiring, mentoring, and promotion; and lower persistence of diverse people in 

STEM education and careers.39

 35 Russell H. Fazio, and Tamara Towles-Schwen, “The MODE Model of Attitude-Behavior Processes,” in Dual Process Theories in 

 Social Psychology, ed. Shelley Chaiken and Yaacov Trop, (New York: Guilford, 1999), 97-116. 36 Adam Hahn, Charles M. Judd, Holen K. Hirsh, and Irene V. Blair, “Awareness of Implicit Attitudes,” Journal of Experimental

 Psychology: General 143, no. 3 (2014): 1369. 37 Galen V. Bodenhausen, “Stereotypes as Judgmental Heuristics: Evidence of Circadian Variations in Discrimination,”

 Psychological Science 1, no. 5 (1990): 319-322. 38Eve Fine, Jennifer Sheridan, Molly Carnes, Jo Handelsman, Christine Pribbenow, Julia Savoy, and Amy Wendt, “Minimizing the

 Influence of Gender Bias on the Faculty Search Process,” in Gender Transformation in the Academy, (2014): 267-289; Molly

 Carnes, Patricia G. Devine, Linda Baier Manwell, Angela Byars-Winston, Eve Fine, Cecilia E. Ford, Patrick Forscher et al., 

 "Effect of an Intervention to Break the Gender Bias Habit for Faculty at One Institution: A Cluster Randomized, Controlled 

 Trial," Academic Medicine: Journal of the Association of American Medical Colleges 90, no. 2 (2015): 221. 39 Mark J. Graham, Jennifer Frederick, Angela Byars-Winston, Anne-Barrie Hunter, and Jo Handelsman, “Increasing Persistence 

 of College Students in STEM,” Science 341 (2013): 1455-6.
Increasing Diversity in the STEM Workforce by Reducing the Impact of Bias

14

Metrics for Assessing Bias

Bias is traditionally assessed using three methods: self-reports, implicit measures such as the 

Implicit Association Test (IAT), and randomized experiments on populations of people. Selfreporting is an ineffective measure of implicit bias since the bias itself is unconsciously held. The 

IAT is used to assess the degree to which two words (e.g., black and white) are associated in 

memory with positive and negative evaluations and to measure associations that people may be 

unable or unwilling to report. For example, the rate at which people can link concepts like “black” 

people or “white” people with characteristics like “good” or “bad” reveals implicit bias. In 

randomized experiments on populations of people, study participants are asked to evaluate an 

item such as a resume or job-performance description, one aspect of which is varied (i.e., male 

vs. female names; stereotypically black vs. white names). Findings from such randomized 

controlled experiments indicate that people are more likely to hire a male candidate for a science 

position, rate an African-American candidate as having higher athletic ability than a white 

candidate, and rate the verbal skills of the person higher if they think that writer is a woman 

rather a man. Research also indicates that biases can be detrimental to employers as well as to 

those against whom biases are held. For example, some science faculty are proven to be less 

likely to hire a female candidate even if she has equal or superior qualifications than her male 

counterpart, which may result in not hiring a better candidate.

40

Practices that Mitigate the Effects of Implicit Bias

There is no single solution to eliminate implicit bias, and despite many organizations’ 

endorsements of equal opportunity principles, the struggle continues to increase workforce 

diversity and minimize the effects of bias in employment decisions.41 Yet the impact of explicit 

and implicit biases can be reduced by making people aware of their own biases and providing 

strategies for bias mitigation,42 applying objective evaluation criteria,43 and exposing people to 

counter-stereotypic images.44 Institutional biases can be replaced with inclusive policies, such as 

 40 Corinne A. Moss-Racusin, John F. Dovidio, Victoria L. Brescoll, Mark J. Graham, and Jo Handelsman, "Science Faculty’s Subtle

 Gender Biases Favor Male Students," Proceedings of the National Academy of Sciences 109, no. 41 (2012): 16474-16479. 41 Donald D. Tomaskovic-Devey, Written Testimony, Meeting of July 1, 2015 – EEOC at 50: Progress and Continuing 

 Challenges in Eradicating Employment Discrimination, U.S. Equal Employment Opportunity Commission (2015),

 http://www.1.eeoc.gov//eeoc/meetings/7-1-15/devey.cfm?renderforprint=1. 42 Sarah M. Jackson, Amy L. Hillard, and Tamera R. Schneider, “Using Implicit Bias Training to Improve Attitudes Toward Women

 in STEM,” Social Psychology of Education 17, no. 3 (2014): 419-438; Matthew J. Zawadzki, Cinnamon L. Danube, and 

 Stephanie A. Shields, “How to Talk About Gender Inequity in the Workplace: Using WAGES as an Experiential Learning Tool 

 to Reduce Reactance and Promote Self-Efficacy,” Sex Roles 67, no. 11-12(2012): 605-616; Eve Fine, Jennifer Sheridan, Molly 

 Carnes, Jo Handelsman, Christine Pribbenow, Julia Savoy, and Amy Wendt, “Minimizing the Influence of Gender Bias on the 

 Faculty Search Process,” in Gender Transformation in the Academy, (2014): 267-289; Molly Carnes, Patricia G. Devine, Linda 

 Baier Manwell, Angela Byars-Winston, Eve Fine, Cecilia E. Ford, Patrick Forscher et al., "Effect of an Intervention to Break the 

 Gender Bias Habit for Faculty at One Institution: A Cluster Randomized, Controlled Trial," Academic Medicine: Journal of the 

 Association of American Medical Colleges 90, no. 2 (2015): 221. 43 Eric Luis Uhlmann and Geoffrey L. Cohen, “Constructed Criteria: Redefining Merit to Justify Discrimination,” Psychological

 Science 16 (2005): 474-480. 44 Nilanjana Dasgupta and Shaki Asgari, "Seeing is Believing: Exposure to Counterstereotypic Women leaders and Its Effect On
Increasing Diversity in the STEM Workforce by Reducing the Impact of Bias

15

access to childcare, dependent care, flexible length of academic tenure clocks, hiring and 

promotion practices, and evaluation criteria.45

Efforts to control managers tend to lead to resistance, whereas human resource practices that 

engage managers tend to be more effective.

46 In attempts to prevent managers from acting on 

biases, equal-opportunity experts have long espoused managerial discretion-control reforms, 

such as diversity training, diversity-performance evaluations, and discrimination-grievance 

mechanisms. There is no universal place to introduce these interventions, but “responsible 

conduct of research” (RCR) courses for junior scientists provide one venue in which it would be 

practical to implement proven practices.47

A review of research in the area of unconscious biases reveals few documented strategies that 

have been shown to effectively organize and simplify the over 150 documented unconscious 

biases. Subsequently, efforts by a small number of researchers have been undertaken to 

organize the most frequent unconscious biases into a taxonomy of biases to more easily identify, 

discuss, and develop strategies to minimize their impact. 

After a review of the literature by the Interagency Policy Group, two unconscious bias taxonomies 

appear to provide a valid foundation to effectively address the organization and simplification of 

unconscious biases. 

The first taxonomy, a proprietary model developed by the NeuroLeadership Institute, is known 

as the SEEDS ModelTM of unconscious bias. According to the NeuroLeadership Institute, the SEEDS 

ModelTM simplifies the roughly 150 identified cognitive biases and recognizes five categories of 

bias, each category responsive to a different set of actions that will help mitigate them.48 It

identifies processes that are claimed to potentially interrupt and redirect unconsciously biased 

thinking. 

 

 the Malleability of Automatic Gender Stereotyping," Journal of Experimental Social Psychology 40, no. 5 (2004): 642-658; 

 Irene V. Blair, Jennifer E. Ma, and Alison P. Lenton, "Imagining Stereotypes Away: The Moderation of Implicit Stereotypes 

 Through Mental Imagery," Journal of Personality and Social Psychology 81, no. 5 (2001): 828. 45 Committee on Maximizing the Potential of Women in Academic Science and Engineering (US), Beyond Bias and Barriers:

 Fulfilling the Potential of Women in Academic Science and Engineering, National Academies Press (2007); Karin Matchett,

 ed., Seeking Solutions: Maximizing American Talent by Advancing Women of Color in Academia: Summary of a Conference,

 National Academies Press (2013). 46 Donald D. Tomaskovic-Devey, Written Testimony, Meeting of July 1, 2015 – EEOC at 50: Progress and Continuing 

 Challenges in Eradicating Employment Discrimination, U.S. Equal Employment Opportunity Commission (2015),

 http://www.eeoc.gov/eeoc/meetings/7-1-15/devey.cfm ; Alexandra Kalev, Frank Dobbin, and Erin Kelly. "Best Practices or 

Best Guesses? Assessing the Efficacy of Corporate Affirmative Action and Diversity Policies." American Sociological Review 

(2006), 71: 589-617. 47 Scholars Offer Scientific Solution to ‘Persistent Bias’ In Academia,” (2014), 

 http://www.sciencedaily.com/releases/2014/02/140206164500.html, in Corinne A. Moss-Racusin, Jojanneke van der 

 Toorn, John F. Dovidio, Victoria L. Brescoll, Mark J. Graham, and Jo Handelsman, "Scientific Diversity Interventions," Science

 343, no. 6171 (2014): 615.

48 David Rock and Heidi Grant Halvorson, “Breaking Workplace Bias at Its Source,” NeuroLeadership Institute, (2015),

 https://neuroleadership.com/wp-content/uploads/2015/03/Breaking-Workplace-Bias-at-its-Source_Rock_17Mar15.pdf
Increasing Diversity in the STEM Workforce by Reducing the Impact of Bias

16

The SEEDS ModelTM is organized around the following dimensions: 

• Similarity: biases that favor those who are similar to us;

• Expediency: biases that favor speed and familiarity;

• Experience: biases that favor past experiences or knowledge;

• Distance: biases that favor things that are close (e.g., time, space, ownership); and

• Safety: biases that favor perceived safety (e.g., status, loss aversion).

One training program from the NeuroLeadership Institute uses the SEEDS ModelTM to simplify 

categories that address implicit bias proposes different mitigation strategies for each of these 

(See Table 1).

49

The second taxonomy is one built upon the research of Lovallo, et al.50 These authors identified 

a similar set of biases to that of the SEEDS ModelTM. They also organize biases into a family of 

five dimensions or clusters that form the acronym “PASSS”. The PASSS model highlights the view 

that unconscious biases “pass” under the awareness of the conscious mind and impact processes, 

policies, and systems in ineffective ways. This model also posits that certain biases interacting 

together can create unique types of organizational cultures that normalize everyday 

organizational decisions, actions, and ways of thinking. 

 49 David Rock and Heidi Grant Halvorson, “Breaking Workplace Bias at Its Source,” NeuroLeadership Institute, (2015),

 https://neuroleadership.com/wp-content/uploads/2015/03/Breaking-Workplace-Bias-at-its-Source_Rock_17Mar15.pdf

50 Dan Lovallo and Olivier Sibony, “The Case for Behavioral Strategy”, McKinsey Quarterly, McKinsey&Company, (2010)

 http://www.mckinsey.com/business-functions/strategy-and-corporate-finance/our-insights/the-case-for-behavioral-

 strategy.

Table 1. Elements of the SEEDS model of unconscious bias

Element Associated Belief Mitigation Strategy

similarity “people like me are 

better than others” focusing on shared goals

expedience “if it feels familiar and 

easy, it must be true”

creating processes to 

consider all information

experience “my perceptions are 

accurate”

by providing multiple 

independent opinions

distance “closer is better than 

distant”

acting as if people are 

closer in space and time

safety “bad is stronger than 

good”

acting as if you are 

deciding for others
Increasing Diversity in the STEM Workforce by Reducing the Impact of Bias

17

The following are the five dimensions of the PASSS model:

• Pattern-recognition: biases that lead people to recognize patterns even where there are 

none;

• Action-oriented: biases that drive people to take action less thoughtfully than they

should;

• Self-interest: biases that arise in the presence of conflicting incentives, including 

nonmonetary and even purely emotional ones;

• Stability: biases that create a tendency toward inertia in the presence of uncertainty; and

• Social: biases that arise from the preference for harmony over conflict.

The PASSS model seems to be a good first step in organizing unconscious biases in a way that can 

mitigate unconscious biases through various strategies and serve as a common language to 

discuss biases at various levels of the organization or institution. But, even more important, the 

research by Lovallo and collaborators has identified two of what they consider to be the most 

important unconscious biases: optimism bias and confirmation bias. The focus on these two 

unconscious biases in particular provides organizations and institutions the ability to provide a 

more simplistic model for bias mitigation.

51

Implicit-bias training for employees and managers should be based on empirical evidence and 

theories, utilize active learning and engagement techniques, and highlight the assertion that 

everyone has biases in order to avoid the appearance of assigning blame.52 It should also contain 

the following components to achieve behavioral change:

• Raising awareness and motivation by acknowledging the need to change and desire to 

take action;

• Providing individuals with strategies and tools to change behavior and reduce the impact 

of bias;

• Giving a sense of empowerment and expectation of positive outcome (that it is similar to 

habit breaking and is worth it); and

• Increasing commitment to reducing bias and practice. 53

Trainings should also include an evaluation of training effectiveness by measuring changes in 

awareness, biases, and self-reported behavior before and after the training. 

 51 Ibid.

52 Corinne A. Moss-Racusin, Jojanneke van der Toorn, John F. Dovidio, Victoria L. Brescoll, Mark J. Graham, and Jo Handelsman,

 "Scientific Diversity Interventions," Science 343, no. 6171 (2014): 615. 53 Molly Carnes, Patricia G. Devine, Carol Isaac, Linda Baier Manwell, Cecelia E. Ford, Angela Byars-Winston, Eve Fine, and

 Jennifer Sheridan, "Promoting Institutional Change Through Bias Literacy," Journal of Diversity in Higher Education 5, no. 2

 (2012): 63.
Increasing Diversity in the STEM Workforce by Reducing the Impact of Bias

18

The Corporate Experience

Government and academic efforts to develop bias interventions can be informed by previous and 

ongoing work in industry, which faces many of the same challenges. Some industry groups are 

rigorously assessing the impact of implicit bias interventions and their studies should provide 

data useful to all sectors. Although these studies do not reflect a consensus view among 

researchers and are derived entirely from corporate environments, some general findings are 

nonetheless important indicators of the areas for consideration in addressing bias in other 

venues. 54 These findings suggest that: 

• There is no evidence that corporate equal-opportunity statements by themselves, though 

important, are associated with increased employee diversity; 

• In corporate settings, some approaches in diversity training often are not effective and 

even can reduce racial minorities’ employment representation, and training that contains 

legal content focused on compliance and legal requirements can reduce representation 

of women and minorities in the company’s workforce; 

• Increased representation of protected groups in influential managerial roles typically 

leads to better implementation of equal-opportunity policies and higher rates of 

employment of minority groups; 

• Accountability and transparency in managers’ employment decisions often leads to 

increased representation and more equal pay for women and minorities; 

• Identity-based networking groups are not on their own effective in advancing careers of 

minority groups in corporate settings, but combined with mentoring programs with 

senior managers often increase representation of protected groups; formalized hiring 

procedures, job postings, and job descriptions tend to increase hiring of women and 

minorities; 

• Often formal evaluation systems are not on their own associated with improved 

outcomes for women and minorities in corporate settings;

• Human-resource practices can exacerbate inequalities, such as certain pay-forperformance systems or performance-based merit pay tend to increase disparities 

between majority and minority employees; and 

 54 Donald D. Tomaskovic-Devey, Written testimony, Meeting of July 1, 2015 – EEOC at 50: Progress and Continuing 

 Challenges in Eradicating Employment Discrimination, U.S. Equal Employment Opportunity Commission (2015),

 http://www.eeoc.gov/eeoc/meetings/7-1-15/devey.cfm ; Alexandra Kalev, Frank Dobbin, and Erin Kelly. "Best Practices or

 Best Guesses? Assessing the Efficacy of Corporate Affirmative Action and Diversity Policies." American Sociological Review 

 (2006), 71: 589-617.
Increasing Diversity in the STEM Workforce by Reducing the Impact of Bias

19

• Using workers’ tenure or position as formal layoff criteria when downsizing an 

organization often disproportionally harms black and female managers unless the 

consequences are analyzed before decisions are finalized. 55

Some of the outcomes of these corporate studies contradict conventional wisdom and some 

standard practices. They serve as a reminder that interventions to reduce the impact of bias 

must be experimentally evaluated to ensure that they achieve the desired outcomes, rather than 

designed and implemented based on intuition alone. 

Businesses and industries are increasingly taking new approaches to reduce implicit bias in their 

organizations. For example, Microsoft CEO Satya Nadella identified diversity as a major focus for 

the company, and all Microsoft employees are now required to take a mandatory course 

evidence-based about overcoming unconscious bias.56 In 2015, Google launched a website that 

provides tools and resources to help other companies learn from their practices, including a 

section on bias.57

 

The Academic Experience

Academic institutions also provide evidence-based practices for reducing bias. Examples of these

practices include a diversity-training session that reduced men’s implicit bias;58 a board game 

that generated discussion about bias;59 a bias workshop during a search-and-hiring process that 

improved hiring outcomes;60 and a bias workshop that improved department climate and 

attitudes of faculty toward women within 3 months of completion.

61 Some lessons learned from 

research on reducing bias in academic settings are: 

• Organizational leadership can create greater value for equitable behaviors;62

• A multicultural approach to race reduces bias, whereas attempts at “color blindness” can 

increase expressions of bias;63

 55 Ibid.

56“Satya Nadella Made Every Microsoft Employee Take a Class on 'Unconscious Bias',” last modified December 2, 2015, 

 http://finance.yahoo.com/news/satya-nadella-made-every-microsoft-171801352.html. 57 See www.rework.withgoogle.com. 58 Sarah M. Jackson, Amy L. Hillard, and Tamera R. Schneider, “Using Implicit Bias Training to Improve Attitudes Toward Women

 in STEM,” Social Psychology of Education 17, no. 3 (2014): 419-438. 59 Matthew J. Zawadzki, Cinnamon L. Danube, and Stephanie A. Shields, “How to Talk About Gender Inequity in the Workplace:

 Using WAGES as an Experiential Learning Tool to Reduce Reactance and Promote Self-Efficacy,” Sex Roles 67, no. 11-

12(2012): 605-616. 60 Eve Fine, Jennifer Sheridan, Molly Carnes, Jo Handelsman, Christine Pribbenow, Julia Savoy, and Amy Wendt, “Minimizing the

 Influence of Gender Bias on the Faculty Search Process,” in Gender Transformation in the Academy, (2014): 267-289. 61 Molly Carnes, Patricia G. Devine, Linda Baier Manwell, Angela Byars-Winston, Eve Fine, Cecilia E. Ford, Patrick Forscher et al.,

 "Effect of an Intervention to Break the Gender Bias Habit for Faculty at One Institution: A Cluster Randomized, Controlled 

 Trial," Academic Medicine: Journal of the Association of American Medical Colleges 90, no. 2 (2015): 221. 62 Virginia Valian, Why So Slow? The Advancement of Women, (Cambridge, Mass.: MIT Press, 2000). 63 Jennifer A. Richeson and Richard J. Nussbaum, "The Impact of Multiculturalism Versus Color-Blindness on Racial Bias,” Journal 

 of Experimental Social Psychology 40, no. 3 (2004): 417-423.
Increasing Diversity in the STEM Workforce by Reducing the Impact of Bias

20

• Multifaceted, repeated training is more effective than unidimensional training;64

• Bias in selection processes can be reduced by developing objective criteria before 

evaluating candidates, ensuring that reviewers adhere to the criteria, and discussing 

alignment of criteria with selections;

65 and 

• Reviewers of application materials rely less on implicit biases when they focus their full 

attention on reviewing candidates than when they multitask or have cognitive 

distractions.66

 64 Patricia G. Devine, Patrick S. Forscher, Anthony J. Austin, and William TL Cox, "Long-term Reduction in Implicit Race Bias: A

 Prejudice Habit-Breaking Intervention," Journal of Experimental Social Psychology 48, no. 6 (2012): 1267-1278. 65 Eric Luis Uhlmann and Geoffrey L. Cohen, “Constructed Criteria: Redefining Merit to Justify Discrimination,” Psychological

 Science 16 (2005): 474-480. 66 Richard F. Martell, "Sex Bias at Work: The Effects of Attentional and Memory Demands on Performance Ratings of Men and 

 Women," Journal of Applied Social Psychology 21, no. 23 (1991): 1939-1960.
Increasing Diversity in the STEM Workforce by Reducing the Impact of Bias

21

CHAPTER III. FEDERAL LEADERSHIP ROLE

Federal Accomplishments to Date in Addressing Bias

Reducing the impact of bias (where it exists, especially unconscious bias, in the Federal STEM 

workforce is an important focus of OPM’s efforts to increase the opportunity for diversity. Based 

on research that shows that recruiting with an emphasis on cultural, experiential, and cognitive 

diversity will improve agencies’ workforce efficiency in addressing increasingly complex 

challenges, OPM has taken actions over the past few years to mitigate the negative impact of 

explicit and implicit bias (where it exists). These actions include: developing a course called

“Micro-behaviors: Understanding the Power of the Unconscious Mind” and using it to train more 

than 10,000 Federal leaders, managers, and employees on the impact of implicit bias; developing 

a course around inclusion called the “New Intelligence Quotient” that has been completed by 

over 15,000 Federal employees and managers; providing implicit bias training to approximately 

3,000 Senior Executive Service members; and using OPM’s updated Inclusive Diversity Strategic 

Plan as a framework for bias mitigation by establishing new norms for OPM activities (such as 

managing meetings, gathering data, and discussing analogies) and stimulating debate that can 

diminish the impact of implicit biases on critical decisions. 

Future Federal Priorities

Addressing bias is one of the priorities in the Administration’s STEM for All Initiative to improve 

equity in and quality of STEM education.67 Existing efforts include working with the media and 

entertainment industry to help raise awareness of opportunities to ensure that representation 

of people working in STEM more accurately represents the diversity of Americans in STEM fields, 

while offering the public a more realistic image of the broad range of STEM jobs to help inspire 

students to consider STEM degrees and careers.

68 

The national-security community in the Federal Government also has identified workforce 

diversity within the intelligence community as a key priority directly impacting mission success.69

The national-security community is working together to develop further and share research-

 67 The White House, “STEM for ALL: Ensuring High-Quality STEM Education Opportunities For All Students,”

 https://www.whitehouse.gov/sites/default/files/microsites/ostp/stem_fact_sheet_2017_budget_final.pdf 68 The White House, Office of Science and Technology Policy, “STEM Depiction Opportunities,”

 https://www.whitehouse.gov/sites/default/files/microsites/ostp/imageofstemdepictiondoc_02102016_clean.pdf. 69 Presidential Memorandum, Promoting Diversity and Inclusion in the National Security Workforce, (October 2016), 

 https://www.whitehouse.gov/the-press-office/2016/10/05/presidential-memorandum-promoting-diversity-and-inclusion-

 national
Increasing Diversity in the STEM Workforce by Reducing the Impact of Bias

22

based best practices that will support its goals around recruitment, retention, and advancement 

in both STEM and non-STEM jobs.

Additionally, a number of agencies are working with their private-sector constituents on inclusion 

in workforce and entrepreneurship across sectors; for example, the private sector has 

spearheaded a Tech Inclusion Pledge that encourages companies to draw from a broader 

segment of the workforce.

70

OSTP-OPM Charge and Interagency Policy Group Response

In October 2015, OSTP and OPM established an Interagency Policy Group (IPG) to engage 

Executive Office of the President offices and Federal agencies (including Federal laboratories)71

in designing efforts to broaden participation in STEM fields through reducing the impact of bias 

in the Federal workplace and in Federally funded institutions of higher education. The IPG 

focused on actions to mitigate the effects of bias, rather than attempt to eliminate it because 

research shows that whereas biases are recalcitrant to interventions, actions and behaviors are 

more readily modified.72, 73

Recognizing the importance of this issue, the OSTP Director and OPM Acting Director charged the 

Interagency Policy Group to: 

• Identify existing practices and develop proposed policies in their organizations aimed at 

reducing the impact of explicit and implicit biases; 

• Produce a report recommending government-wide policy options; and 

• Submit the report and recommendations for Agency approval.

In responding to the charge, the IPG developed a vision, goal, objectives, and guiding principles; 

inventoried agencies’ current policies and practices; identified best and promising practices; 

developed recommendations for Government-wide policies; and suggested implementations

and next steps. The remainder of this document reports the IPG’s findings, recommendations, 

and suggested next steps.

 70 https://www.whitehouse.gov/sites/whitehouse.gov/files/images/Blog/Tech-Inclusion-Pledge-Letter-6-22-16.pdf or 

 http://www.tech-inclusion.org 71 Participants include: United States Departments of Agriculture, Commerce (including National Oceanic and Atmospheric 

Administration and United States Patent and Trademark Office), Defense, Education, Energy, Health and Human Services, 

Homeland Security, Interior, Labor, and Transportation; Environmental Protection Agency; National Aeronautics and Space 

Administration; National Science Foundation; and the Smithsonian Institution. 72 Lai, C.K., Marini, M., Lehr, S.A., Cerruti, C., Shin, E.L., Joy-Gaba, J., Ho, A.K., Teachman, B.A., Wojcik, S.P., Koleva, S., Frazier, 

 R.S., Heiphetz, L., Chen, E., Turner, R.N. Haidt, J., Kesebir, S., Hawkins, C.B., Sartori, G., Schaefer, H.S., Rubichi, S., Dial, 

 C.M.,Sriram, N., Banaji, M.R., Nosek, B.A. (2014) Reducing implicit racial preferences: I. A comparative investigation of 17 

 interventions. Journal of Experimental Psychology 143: 1765-1785.

73 Carnes, M., Devine, P. G., Manwell, L. B., 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 90:221-230. 
Increasing Diversity in the STEM Workforce by Reducing the Impact of Bias

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In presenting its vision, goals, and guiding principles, the IPG set out to capture the need to 

address barriers to inclusive participation and advancement in the STEM enterprise. The IPG 

drew upon the context described in this section, including the national demographic landscape, 

Congressional concerns, corporate environments, and the evidence supporting bias mitigation 

strategies in remediating diversity- and inclusion-related challenges in the STEM workforce. The 

vision, goals, objectives, and guiding principles are designed to provide a foundation for the 

promising practices and government-wide recommendations identified.

Vision, Goal, and Objectives

Vision: A robust and inclusive world-class U.S. STEM enterprise characterized by institutions of 

higher learning and Federal-workplace environments free from bias and other barriers that can 

impede creativity, productivity, entrepreneurial vitality, and quality of life.

Goal: To enable the progress of science, innovation, and society through greater participation 

and advancement of all Americans in STEM, including those from historically underrepresented 

and underserved racial and ethnic groups, women and girls, and people with disabilities.

Objectives: 

• To enhance existing and to establish new policies and practices across the Federal 

Government that mitigate the impact of explicit and implicit bias (where it exists) in STEM 

workplaces.

• To spur collaboration among Federally funded institutions of higher learning in the 

exchange of best policies and practices and other resources to mitigate bias.

Guiding Principles

• Intellectual diversity strengthens the STEM enterprise;

• Preparation and advancement of all available U.S. talent is essential to the Nation’s STEM

leadership globally;

• Diversity and inclusion are central to meeting agencies’ missions and the business case 

for success; and, 

• Groups traditionally underrepresented and underserved in STEM workplaces provide a 

reservoir of untapped creativity, diversity of thought, and engines of innovation that must 

be woven into the fabric of the STEM enterprise, both in Federal agencies and institutions 

of higher education funded by these agencies.
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CHAPTER IV. BEST, PROMISING, AND EMERGING PRACTICES

The IPG Best Practices Subgroup identified a range of best and promising practices that 14 

Federal agencies are using to raise awareness about bias and minimize its impact (see 

visualization of agency efforts in Figure 1 and links to agency websites for summaries in Appendix 

A). This chapter highlights selected successful practices that the Federal science and engineering 

agencies have used to: (1) reduce the impact of bias within their own workforce; or (2) generate 

basic research, development, and tested interventions on bias in academia.

KEY WORD VISUALIZATION OF AGENCY EFFORTS

Figure 1. Visualization Summary: In general, best practices for mitigating the impact of bias are related to organizational 

performance and the grant-making process. The participating agencies identified the following best practices for sharing 

information: participation in career fairs, collaborative efforts, strategic recruitment, work with professional organizations, 

regular participation in recruitment events, the use of inclusive work teams, career-guidance resources, and sharing new 

opportunities to reduce the impact of bias and promote diversity and inclusion. This graphic was created specifically for this 

report using data collected from the IPG agencies. 
Increasing Diversity in the STEM Workforce by Reducing the Impact of Bias

25

Practices for Reducing the Impact of Bias in the Federal STEM Workforce

Figure 2. The number of efforts from each agency, characterized by level of progress. This graph was created for this report using 

data collected from the IPG agencies. 

Best Practices

Best practices were defined as those that consistently mitigated explicit or implicit bias as 

indicated by the agencies’ metrics or indicators, which included replicability. Best practices 

identified are: 

• Analyses of mandated workforce data sets; 

• Implicit bias training;

• Conflict resolution; and

• Promoting work flexibility.
Increasing Diversity in the STEM Workforce by Reducing the Impact of Bias

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Analyses of Mandated Workforce Data Sets. Collecting data is, in itself, an effective intervention,

and also provides a means to assess progress and a tool to dispel myths about advantages 

experienced by women and minorities in hiring and promotion. Quantitative assessment of 

hiring and retention, and qualitative assessment of workplace climate and worker satisfaction,

are fundamental tools for diversifying the workforce and may point to manifestations of bias that 

can be addressed. Agencies consider annual data collection, reporting employee-work 

satisfaction, and analysis of workforce demographics for mandated Federal reports to constitute 

a best practice. These data are used to examine trends for potential workplace challenges and 

provide a vehicle for focusing on biases and barriers. Reports in which relevant data are provided 

include the Federal Equal Opportunity Recruitment Plan (FEORP),74 the Annual Equal 

Employment Opportunity (EEO) Program Status report known as MD-715,75 the Federal 

Employee Viewpoint Survey (FEVS) Trend Data and Action Planning,76 and the Annual Federal 

Equal Opportunity Statistical Report of Discrimination Complaints (EEOC 462 Report).77

Based on analyses of workforce demographics, USDA has implemented annual hiring goals for 

disabled veterans (10 percent) and other disabled employees (10 percent). USDA also prepares 

a Cultural Transformation Milestones and Metrics Report. This monthly report addresses areas 

such as diversity of employees and new hires, work-life balance and wellness, leadership, 

employee development, training, talent management, hiring reform and equal-opportunity 

accountability. Results are generated using self-reported data as well as data from the National 

Finance Center’s Time and Attendance databases. The results are compared over time to 

highlight improvements or areas in need of attention.

Agencies also use the FEVS results to conduct workplace climate assessments, and a few agencies 

have developed their own surveys. In addition to using FEVS results to evaluate progress, NASA

deploys its own comprehensive Diversity and Inclusion (D&I) Assessment Survey to all employees 

for a more in-depth measurement of its D&I efforts and to discover potential areas of bias or 

perceptions of exclusion that might exist in the workplace. NASA first deployed the survey in 

2010 and repeated it in 2014. NASA used the initial survey as a baseline assessment, with plans 

to measure progress by repeating the survey approximately every 3 years. The D&I Survey 

measures respondents’ perceptions about the meaning and culture of diversity and inclusion at 

NASA. It indicates strengths as well as areas and opportunities for improvement. Survey 

questions address whether employees believe that managers address bias (where it exists) or 

whether employees have felt marginalized or excluded, enabling NASA to measure perceptions 

around the presence of bias and to shape means of addressing bias, such as voluntary education 

and awareness opportunities that explore methods of bias mitigation. NASA compared the 2014 

 74 United States Office of Personnel Management/Office of Diversity and Inclusion, "Federal Equal Opportunity

 Recruitment Program (FEORP) Report to Congress: Fiscal Year 2014" (ODI-02364-FEORP 2/2016), 

 https://www.opm.gov/policy-data-oversight/diversity-and-inclusion/reports/feorp-2014.pdf 75 United States Equal Employment Opportunities Commission, "Annual Report on the Federal Work Force Part II: Work

 Force Statistics Fiscal Year 2011," (2014), https://www.eeoc.gov/federal/reports/fsp2011_2/upload/fsp2011_2.pdf 76 United States Office of Personnel Management, "Federal Employee Viewpoint Survey Results: Employees Influencing

 Change," (2016), https://www.fedview.opm.gov/2015FILES/2015_FEVS_Gwide_Final_Report.PDF 77 United States Equal Employment Opportunities Commission, "Annual Report on the Federal Work Force Part I: EEO 

 Complaints Processing Fiscal Year 2014," (2016), https://www.eeoc.gov/federal/reports/fsp2014/
Increasing Diversity in the STEM Workforce by Reducing the Impact of Bias

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survey results with the baseline survey and saw small improvement in employee perceptions on 

the presence of bias. The progress indicated by the survey results was presented to senior 

leadership and used to inform the Agency’s D&I Strategic Plan and Agency-wide D&I training. 

The results were also disaggregated for each of NASA’s field centers and shared with them to 

inform their D&I Plans.

Implicit Bias Training. Most agencies include implicit bias content in mandatory EEO and D&I 

training for supervisors and managers. Other opportunities for implicit-bias training include 

online-training modules, informational briefings, agency-wide workshops, and in-class training. 

USDA provides Department-wide online and instructor-led training opportunities through its 

AgLearn system. Examples of these trainings include: “Avoiding Discrimination and Bias; Training 

and Tips for Leaders/Managers” and related opportunities such as “Veteran Employment 

Training for Federal Hiring Manager/Human Resource Professionals” and “A Roadmap to Success: 

Hiring, Retaining and Including People with Disabilities.” United States Patent and Trademark 

Office’s (USPTO) New Inclusion Quotient Training (IQT) provided training to over 85 percent of 

its managers in the last 24 months. The New IQT empowers managers with tools designed to:

(1) transform inclusive practices into habits, and (2) measure inclusion through the inclusion 

quotient, which is derived from 20 FEVS questions. 

Conflict Resolution. Research shows that fair, effective, and accessible means for conflict 

resolution are essential for a healthy workplace, but unintended bias can corrupt the fairness of 

the process. It is therefore imperative to ensure that managers are cognizant of their own biases 

when managing conflict. Agencies reported that alternative dispute-resolution programs are

lowering the number of employees filing formal complaints about harassment, indicating that 

disputes are being resolved at an early stage. For example, over a 5-year period starting in 2006, 

NASA averaged 23 formal complaints about harassment. For the next 5 years following 

implementation of the Anti-Harassment Program (AHP), instituted in FY 2010, NASA averaged 16 

formal complaints about harassment, a 28 percent decline.78 Programs like AHP often promote 

understanding of diverse perspectives and unconscious assumptions about communication, 

people’s behavior, and high-quality performance. 

NASA’s Conflict Management Program (CMP) consists of basic conflict-management training as 

well as web-based refresher training, team training, executive sessions, webinars, and individual 

conflict consultations. CMP provides managers and employees with the communication and 

mitigation skills to explore sources of conflict, including implicit and structural biases, and to 

identify hidden sources of bias. In 2015, 70 percent of NASA survey respondents responded 

 78 NASA EEO complaints data posted pursuant to the No FEAR Act, http://odeo.hq.nasa.gov/nofear.html.
Increasing Diversity in the STEM Workforce by Reducing the Impact of Bias

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positively on items evaluating the fairness of Agency leadership, including the fair resolution of 

disputes. NASA ranked number one among large Federal agencies on these questions.79

Work Flexibility. Institutional bias includes policies that unintentionally discriminate against 

certain groups, creating unintended barriers to their participation in a workforce. Many historical 

structures and rules were created in times when the vast majority of employees were men, had 

wives who carried the majority of childcare duties, and whose first language was English. Today, 

institutional bias must be examined and mitigated because organizational structures still do not 

accommodate certain groups of modern workers who experience significant hardships that 

others do not. This section addresses practices intended to reduce structural biases in the 

workplace.

Work-life balance is a key determinant of employee contentment, and work flexibility contributes 

to one’s ability to attain that balance. Even when providing work flexibilities, leadership has to 

be proactive in collecting employee input concerning unconscious attitudes about flexibility 

options, conditions for their use, and potential impacts on performance assessments. Several 

agencies cited flexibility as a best practice for the Federal STEM workforce and have measured 

positive outcomes from implementing telework and alternative work schedules that enable 

employees to reduce commuting time and adjust their time at work to accommodate other life 

obligations. 

USDA encourages eligible employees to participate in the telework program through a variety of 

methods including “Telework Week” when eligible employees pledge to telework at least 1 day 

during the week of April 610 in order to promote the policy’s many benefits. Specifically, at the 

National Institute of Food and Agriculture (NIFA), participation increased from year to year up to 

53 percent (172 participants). The Department has an established goal that 60 percent of all

Core eligible employees participate in Core Telework. NIFA was at 91 percent at the end of FY

2014. Additionally, in a cost-savings analysis, USDA reported that the number of employees 

currently teleworking in both Core and Ad-hoc remains steady at approximately 21,000 

employees.

 79 Agency Rankings by Category, Effective Leadership: Fairness,” The Best Places to Work in the Federal Government,

 http://bestplacestowork.org/BPTW/rankings/categories/large/leadership_sub_fairness
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At the USPTO, the telework program supports one of the agency’s core missions—examination 

of often highly complex patent applications—by bolstering its 

ability to recruit and retain a highly skilled, diverse workforce. 

The telework program began in 1997 with 18 employees and 

now engages 10,601, with 84 percent of them teleworking 

between 1 and 5 days per week. Employees value the 

flexibility it offers to balance work and family and reduce the 

time and environmental footprint associated with commuting; 

the practice also contributes to uniting a geographically 

dispersed workforce.

Promising Practices

Promising practices are defined as those that are consistent 

with principles established by research but have not been the 

subject of evaluation. Every agency contributed examples of 

their own practices, including the following: 

• Diversity change agents;

• Diversity toolkits;

• Technical qualifications boards; and

• Proposal review experiments.

Diversity Change Agents. The Change Agent strategy at 

Department of Education (ED) involves robust training that 

helps Diversity Change Agents (DCAs) confront their own 

biases and to learn the principles and practices of effecting 

organizational change. The DCAs then provide diversity 

training, which includes a module on implicit bias, to others in 

the Department. 

Diversity Toolkits. Department of Labor (DOL) has an online 

library of policy guidance to inform employees of their rights 

and responsibilities. NASA’s Promising Practices Guide80 is a 

catalogue that showcases innovative EEO and D&I efforts of 

the Agency and its field centers. The promising practices are 

grouped around six themes: leadership commitment, attracting top talent, cultivating excellence, 

teamwork and innovation, serving the American people, and advancing the STEM pipeline. 

 80 “Promising Practices for Equal Opportunity, Diversity, and Inclusion,” National Aeronautics & Space Administration, last 

 modified July 2015, http://odeo.hq.nasa.gov/documents/PromPract_8-20-15_TAGGED.pdf.

Reducing Bias in Peer 

Review

NIH’s Center for Scientific Review 

(CSR) is playing a key role in 

understanding the role of implicit 

bias in unexplained racial 

disparities in R01 grant awards. 

With the input from a team of 

experts in implicit bias, 

stereotyping, decision-making, and 

research training, CSR completed 

two challenges (under the prize 

authority provided by the American 

COMPETES Reauthorization Act of 

2010) that engaged the scientific 

community in finding new methods 

to detect possible bias in peer 

review given the unavailability of 

valid metrics for evaluating implicit 

bias in the review process. This 

subcommittee on Peer Review 

discouraged over-reliance on the 

Implicit Associations Test and 

cautioned possible resistance

associated with mandatory implicit 

bias training for reviewers. CSR 

surveyed and conducted focus 

groups with a diverse sample of 

new investigators to garner their 

insights on the fairness of NIH peer 

review and the challenges they face 

in seeking NIH funding. The value 

of anonymization of grant 

applications in reducing bias will be 

tested in an upcoming project that 

examines the effect of altering PI 

race, gender, and university 

affiliation on reviewer scores of 

R01 applications.
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30

Examples of promising practices cited in the Guide include: D&I training that focuses on implicit 

bias, is designed for executive leadership, managers, supervisors, and employees, and includes 

opportunities for practical application; making the Agency’s websites more accessible to 

individuals with vision disabilities; increasing work-life flexibilities; and partnering effectively with 

local educational and business communities to engage and inspire young people from all 

backgrounds. One purpose of the Promising Practices Guide is to enhance cross-pollination 

among the NASA Centers. The Guide will be regularly updated. 

Technical Qualifications Boards. The Environmental Protection Agency (EPA) has developed 

Technical Qualifications Boards based on a substantial body of research indicating that when 

evaluators establish criteria before review, they apply less bias to their evaluation of people or 

their accomplishments. These panels review and evaluate qualifications and contributions of all 

candidates for promotion to senior (GS14-15) levels for research positions against published 

guidelines, in an attempt to ensure consistent, fair, and equitable treatment of candidates for 

promotion throughout EPA’s research organization.

Proposal Review Experiments. DOE is increasing the diversity of STEM professionals who review 

DOE proposals by opening up the reviewer pools to diverse faculty from both majority institutions 

and minority-serving institutions. The current reviewer database is being expanded to include 

demographic data about reviewers and at least one reviewer from an underrepresented minority 

group will participate in each review panel. A few agencies are planning to analyze the scientific 

review process, including NSF. See also DOE and NIH highlights on the proposal-review process 

that follow in this and the next section.
Increasing Diversity in the STEM Workforce by Reducing the Impact of Bias

31

NSF is continuously testing novel review approaches. One new and two recent pilots offer potential 

to strengthen the review process by: (1) attracting an untapped reviewer pool, (2) removing barriers 

to scientists serving as reviewers, and (3) reducing evaluation bias, if present. 

Pilot 1: Virtual Panels. NSF has experimented with having reviewers participate virtually, using 

teleconferencing, videoconferencing, or virtual worlds. Motivations for using wholly virtual panels 

have included enhanced opportunities for reviewer training, greater flexibility in how panels are 

structured, and removing barriers for those who may not be able to travel due to physical limitations, 

family responsibilities, or other restrictions. In addition to completely virtual panels, many panels 

now provide the option for panelists to attend remotely, if they cannot attend in person. The option 

of attending such a hybrid panel virtually enhances the participation of women. For example, looking 

at those reviewers for whom gender information was available in FY 2013, 29 percent of the reviewers 

who participated in hybrid panels by traveling to NSF were women while a larger percent (34 percent) 

of those who participated virtually were women. 

Pilot 2: Mechanism Design. The Mechanism Design pilot examined a review mechanism in which the 

investigators who submit proposals also review some of the competing proposals. The usual policy 

on avoiding conflict of interests was applied. In the pilot, each proposal received seven reviews and

the average review ratings were used to determine a consensus ranking of all of the proposals. The 

rating of the PI’s own proposal was then supplemented with “bonus points” depending upon the 

degree to which his or her ordering of the assigned seven proposals agrees with the consensus 

ranking. The awarding of bonus points is the step that game theory suggests should provide an 

incentive to each reviewer to give a fair and thorough rating and ranking of the proposals to which he 

or she is assigned. This encourages objectivity and promotes openness in the review process by 

requiring the individual to consider how other objective reviewers would assess each proposal. The 

NSF program officer then uses the reviewers’ comments, ratings, and rankings as the primary input 

for his or her funding recommendations. 

Pilot 3: Reviewer Orientation. Beginning in 2017, many research programs at NSF will provide an 

orientation, by webinar, to panel reviewers before they prepare their individual written reviews of 

proposals. The orientation will include tips on how to structure reviews, a reminder of the review 

criteria, and techniques for consciously mitigating the potential influence of unconscious, cognitive 

biases. This pilot will test whether such an orientation enhances the quality of written reviews. It will 

also examine whether providing information about strategies for mitigating unconscious biases leads 

to any changes in the distributions of review ratings received by proposals from investigators who 

belong to underrepresented minorities.

Strengthening the Scientific Review Process 
Increasing Diversity in the STEM Workforce by Reducing the Impact of Bias

32

Emerging Practices

One exploratory practice is unconscious bias training for search committees. This type of 

unconscious-bias training at NIH is an institutional practice used with scientific directors and the 

search committees in charge of hiring their scientific workforce. This effort also will attempt to 

assess, with data, the effectiveness of implicit-bias training. In a similar effort, DOE’s Argonne 

National Lab has launched a training initiative on implicit bias aimed at hiring committees. The 

training efforts are based on the general consensus reported by research that biases can be 

unlearned and replaced with new mental associations. 

Other emerging practices include ED’s work to facilitate a training series to discuss race and 

equity, including the impact of bias. These conversations address inequity across demographic 

groups and are expected to help mitigate bias and create a more inclusive environment for all 

employees. Additionally, Department of Interior (DOI) is using focus groups as well as climate 

surveys, DOL has special training for the entire DOL workforce entitled “Blindspot: Hidden Biases 

of Good People,” and EPA has a Hiring and Promotions Safeguard Pilot Program. More recent 

efforts that are underway or planned are presented later in this section.

A significant impetus for work by the U.S. Department of Transportation (DOT) to reduce the 

impact of bias within the agency is the Ladders of Opportunity Agenda, a major DOT priority. The 

department-wide Ladders of Opportunity internal program focuses on the importance of 

harnessing the talents of each individual, embracing diversity and promoting self-advocacy, and 

considering how these priorities help ensure that equity considerations are part of all DOT 

decisions. Among the activities advancing this effort, DOT hosted a Diversity and Inclusion Forum 

to showcase the important role that diversity has played in the history of transportation, 

presented the first in series of planned diversity, self-advocacy, leadership events entitled “DOT 

Planting Seeds: Select Your Destination,” and created a Diversity and Inclusion Council to identify 

innovative ways to engage and educate DOT employees on issues related to diversity and 

inclusion in workplace practices. The Departmental Director, and Deputy Director, of the Office 

of Human Resource Management, are members of the Office of Personnel Management’s 

Diversity and Inclusion in Government (DIG) Council, attending monthly meetings and serve as 

liaisons to DOT leadership. 

In addition, the Department of Homeland Security (DHS) has developed a new tool, the 

“Inclusive Behaviors Job Aid,” to provide managers and supervisors concrete examples of how to 

be consciously inclusive in the workplace. In 2016, DHS also began developing a bank of interview 

questions that focus on the candidates’ experience in managing diverse and inclusive workplaces. 

This effort is founded upon research that indicates that underrepresented groups are often 

excluded from key positions simply because they are not deliberately included, but that by 

learning about the power of implicit bias and ways to mitigate it, organizations can create more 

inclusive workplaces. 81

 

81 Corporate Executive Board, “Beneath the Surface of Diversity Recruiting: Activating Inclusion Recruiting.” 2011. 

https://www.cebglobal.com/home.html/
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Federal-Academic Interface: Research, Development, and Interventions on Bias

The Federal Government has had a vast impact on the academic STEM workforce by funding a 

substantial body of basic and applied research on bias. Basic research has produced a rich 

understanding of bias, while the applied research has built on this understanding to design 

evidence-based interventions that have been tested on campuses across the Nation. Several 

agencies have advanced both of these efforts. 

Basic Research

NSF has supported basic research, development, and implementation activities. Through its

Science of Broadening Participation portfolio, NSF has invested in research on the theories, 

DOE aims to reflect in their training program the belief that implicit bias is not inherent in 

people, but rather it is a reaction to experiences, including words and actions over the 

lifetime of an individual. In order to mitigate this bias, there need to be experiential 

learning opportunities to change how employees are reviewed. It is through active, 

engaged learning that DOE hopes to reduce the impacts of bias. DOE will make the 

following recommendations for providing experiential learning related to implicit bias: 

• Individual Professional-Development Training: DOE will encourage employees to take a 

series of courses made available through the online-learning center on topics related 

to bias mitigation and communicating across cultures. DOE will recommend making 

courses mandatory for managers and team leaders on an annual basis and optional for 

non-supervisory employees. Employees will receive a certificate in bias mitigation 

once all courses are completed. 

• Bias Interrupters: DOE will develop and promote “bias interrupters” as a resource for 

managers and employees who complete training related to bias mitigation. The “bias 

interrupters” will be tips and practices that employees can use to improve the 

objectivity and quality of decisions about hiring, promotions, career development 

opportunities, and performance appraisals.

• DOE Community: DOE will leverage its workforce to challenge stereotypes in STEM 

fields by engaging underrepresented communities in hands-on technology 

demonstrations. By introducing learners to new technology applications like the 

Smartphone Microscope, which utilizes relatively familiar, non-intimidating 

technologies like a camera and microscope, DOE will tap into a learner’s conscious and 

unconscious experiences with STEM disciplines. By leveraging the Smartphone 

Microscope, developed by one of its National Labs, along with its eclectic Federal 

workforce, DOE can positively impact STEM education efforts in certain targeted

communities.

DOE Implicit-Bias Training 
Increasing Diversity in the STEM Workforce by Reducing the Impact of Bias

34

methods, and analytic techniques of the social, behavioral, economic, and learning sciences to 

better understand the barriers that hinder and factors that enhance the participation of women 

and underrepresented minorities in STEM. Some of the powerful findings that emerge from this 

type of research include:

• Gender bias impacts hiring. A male job applicant for an academic positions was rated as 

more competent and would be offered a higher salary than an identical female 

applicant.

82

• Assertiveness training does not improve women’s ability to negotiate. Sometimes 

women did not know that they could ask for what they want, and sometimes they did not 

ask because they had learned that society reacts badly to women asserting their own 

needs and desires.

83

• Letters of recommendation disadvantage women. Female applicants were described in 

more communal (social or emotive) terms and male applicants were described in more 

agentic (active or assertive) terms.

84

• Stereotype threat impacts performance. If there are negative stereotypes about a 

specific group, its members are likely to become anxious about their performance if 

attention is called to their membership in that group, which may hinder their ability to 

perform at their maximum level in part due to diversion of blood away the cognitive 

centers of the brain following activation of stereotype threat.85,86

NIH also supports basic research on bias, stereotypes, and interventions to combat the impact of 

bias. Some examples are research on causal factors and interventions that promote support 

careers of women in biomedical and behavioral sciences and engineering87 and NIH Directors 

Pathfinder Award, designed to encourage exceptionally creative individual scientists to develop 

 82 Corinne A. Moss-Racusin, John F. Dovidio, Victoria L. Brescoll, Mark J. Graham, and Jo Handelsman, "Science Faculty’s Subtle

 Gender Biases Favor Male Students," Proceedings of the National Academy of Sciences 109, no. 41 (2012): 16474-16479. 83Hannah Riley Bowles, Linda Babcock, and Lei Lai, "Social Incentives For Gender Differences in the Propensity to Initiate

 Negotiations: Sometimes it Does Hurt to Ask," Organizational Behavior and Human Decision Processes 103, no. 1 (2007): 84-

 103. 84Juan M. Madera, Michelle R. Hebl, and Randi C. Martin, "Gender and Letters of Recommendation for Academia: Agentic and 

 Communal Differences," Journal of Applied Psychology 94, no. 6 (2009): 1591. 85 Claude M. Steele and Joshua Aronson, "Stereotype Threat and the Intellectual Test Performance of African Americans,"

 Journal of Personality and Social Psychology 69, no. 5 (1995): 797; Jason W Osborne, "Linking Stereotype Threat and

 Anxiety," Educational Psychology 27, no. 1 (2007): 135-154; Anne Maass, Claudio D'Ettole, and Mara Cadinu, "Checkmate? 

The Role of Gender Stereotypes in the Ultimate Intellectual Sport," European Journal of Social Psychology 38, no. 2 (2008): 

231-245. 86 Wendy Berry Mendes and Jeremy Jamieson, "Embodied Stereotype Threat: Exploring Brain and Body Mechanisms Underlying

 Performance Impairments," Stereotype Threat: Theory, Process, and Application (2011): 51-68.

87 Research on Causal Factors and Interventions that Promote and Support the Careers of Women in Biomedical and 

Behavioral Science and Engineering (R01), Program Announcement, RFA-GM-09-012.
Increasing Diversity in the STEM Workforce by Reducing the Impact of Bias

35

highly innovative and possibly transforming approaches for promoting diversity within the 

biomedical research workforce.

88

Findings from these NIH-supported basic research include:

• Gender bias in grant reviews. Grant reviewers implicitly hold different standards for male 

versus female applicants, and were more critical of female applicants and gave lower 

priority, approach, and significance scores to females in renewal applications.89

• Race bias in grant reviews. Black applicants are less likely to receive independent 

research grants compared to white applicants.90

• Reducing implicit gender-leadership bias in academic medicine with an educational 

Intervention. Educational intervention on implicit bias reduced faculty members’ implicit 

bias regarding women and leadership (as measured by the IAT).91

• Stereotype threat gender differences in academia. Female faculty report greater 

susceptibility to stereotype threat in academic sciences and lower confidence in the 

likelihood of advancement compared to males, despite similar career interest and 

identification.92 

Research, Development, and Intervention

Many programs have been initiated to transform the basic understanding of bias into practices 

that reduce its impact. Examples include the following:

• NSF’s ADVANCE: Increasing the Participation and Advancement of Women in Academic 

Science and Engineering Careers program93 since 2001 has generated training programs 

and curricula for reducing implicit bias. Several of these have been successful in extensive 

comparative studies and randomized controlled trials. The rigorously tested, successful 

programs provide a starting point for any agency or institution of higher education

 88 NIH Recovery Act Grants to Foster Scientific Workforce Diversity, National Institutes of Health, Turning Discovery into 

 Health, (2010), 

 https://www.nih.gov/news-events/news-releases/nih-recovery-act-grants-foster-scientific-workforce- diversity

89 Anna Kaatz, You-Geon Lee, Aaron F. Potvien, Wairimu Magua, ... Molly Carnes, “Analysis of NIH R01 Application Critiques, 

 Impact and Criteria Scores: Does the Sex of the Principal Investigator Make a Difference?” Academic Medicine : Journal of 

 the Association of American Medical Colleges, 91, no. 41 (2016): 1080–1088. 

90 Donna K. Ginther, Walter T. Schaffer, Joshua Schnell, Beth Masimore, Faye Liu, Laurel L. Haak, and Raynard Kington. "Race, 

 ethnicity, and NIH research awards." Science 333, no. 6045 (2011): 1015-1019.

91 Sabine Girod, Magali Fassiotto, Daisy Grewal, Manwai Candy Ku, Natarajan Sriram, Brian A. Nosek, and Hannah Valantine. 

 "Reducing Implicit Gender Leadership Bias in Academic Medicine With an Educational Intervention." Academic Medicine: 

 Journal of the Association of American Medical Colleges (2016).

92 Magali Fassiotto, Elizabeth O. Hamel, Manwai Ku, Shelley Correll, Daisy Grewal, Philip Lavori, VJ Periyakoil, Allan Reiss, 

 Christy Sandborg, Gregory Walton, Marilyn Winkleby, and Hannah Valantine. "Women in Academic Medicine: 

 Measuring Stereotype Threat Among Junior Faculty." Journal of Women's Health 25, no. 3 (2016): 292-298.

93 ADVANCE Program Solicitation NSF 14-573. Also, www.portal.advance.vt.edu/
Increasing Diversity in the STEM Workforce by Reducing the Impact of Bias

36

interested in developing its own training program and advancing institutional 

transformation. Some are immediately transferable to any work environment and others 

need to be adapted to the Federal work environment. 

• The National Center for Women & Information Technology, funded by NSF and other 

collaborators, has developed case studies that offer solutions to reduce the impact of 

bias.

94

• NSF’s recently launched the Inclusion across the Nation of Communities of Learners of 

Underrepresented Discoverers in Engineering and Science (NSF INCLUDES) Initiative, will 

support new research, models, networks, and partnerships that lead to measureable 

progress at the national level and support efforts to scale the concepts of diversity and 

inclusion in STEM.95 A key tenet of INCLUDES is that the infusion of broadening 

participation research about biases and barriers can positively inform implementation 

strategies and practices. 

• NIH’s BUILD: Building Infrastructure Leading to Diversity Initiative is a set of grants issued

to undergraduate institutions to implement and study innovative approaches to engaging 

and retaining students from diverse backgrounds in biomedical research. Through 

implementing a variety of innovative intervention approaches to research skill building 

and training, mentorship and institutional change, the BUILD-funded institutions address 

various challenges faced by students, faculty and institutions, including the impact of 

bias.96

• NIH’s National Institute for General Medical Sciences (NIGMS) has been supporting the 

Research to Understand and Inform Interventions that Promote the Research Careers of 

Students in Biomedical and Behavioral Sciences, a program that facilitates dissemination 

and exchange of hypothesis-based research on interventions and initiatives that broaden 

participation in science- and engineering-research careers, including those that focus on 

the impact of implicit bias. It is designed to create a dialogue among behavioral science, 

social science, and education researchers, evaluators, and faculty in STEM fields who 

participate in intervention programs.97,98 NIGMS intentionally supports research on 

implicit bias through all of its diversity-focused programs as well as a range of research 

studies on implicit-bias interventions.

 94 See https://www.ncwit.org/resources/how-can-reducing-unconscious-bias-increase-women%E2%80%99s-success

 it/avoiding-unintended-gender; 

 https://www.ncwit.org/resources/how-can-reducing-unconscious-bias-increase-women%E2%80%99s-success-it/avoiding-

 gender-bias.

95 INCLUDES Program Solicitation NSF 16-544. http://www.nsf.gov/pubs/2016/nsf16544/nsf16544.htm

96 https://diversityprogramconsortium.org/pages/build

97 Conference reports can be found at http://understanding-interventions.org/reports/

98 http://grants.nih.gov/grants/guide/rfa-files/RFA-GM-14-013.html
Increasing Diversity in the STEM Workforce by Reducing the Impact of Bias

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Family-Friendly Policies and Programs 

Recently, agencies have been addressing stereotypes and institutional barriers related to gender 

roles that can undermine performance due to parental status and family responsibilities. Familyfriendly policies and practices for NIH-grant recipients include child-care support as part of 

conference grants, re-entry supplements, and up to 8 weeks of paid parental leave for National 

Research Service Award trainees. The Career-life Balance Initiative (CLB) at NSF is an agencylevel approach to attract, retain, and advance graduate students, postdoctoral fellows, and earlycareer researchers in STEM fields, especially women. CLB aims to develop a coherent and 

consistent set of new and existing career-life policies and practices agency-wide to expand 

dependent care and dual-career support to improve the STEM work environment in higher 

education, as well as CLB partnership pilots with colleges and universities to promote the 

development and advancement of diverse STEM talent. 

Compliance Reviews

Several agencies have tested the intervention of holding their grantees accountable for 

addressing implicit bias to encourage good practices. NIFA and NASA both conduct civil-rights 

and equal-employment-opportunity reviews in the institutions they fund. They attempt to 

ensure that their partners in research, education, and extension are in compliance with the rules 

and regulations pursuant to Federal civil-rights laws:

• Title VI of the Civil Rights Act of 1964 (prohibits discrimination on the basis of race, color, 

national origin, limited English proficiency);

• Title IX of the Education Amendments Act of 1972 (prohibits discrimination on the basis 

of gender);

• Age Discrimination Act of 1975 (prohibits discrimination on the basis of age); and

• The Rehabilitation Act of 1973 (prohibits discrimination on the basis of disability).

In assessing grantee compliance, NASA reviews methods of administration that can have 

unintentional adverse impacts, including policies and practices such as admission criteria, 

student advising, and classroom or research participation. Written reports with findings and 

recommendations that include identifying and addressing implicit bias are provided to university 

presidents or heads of museum and science centers. NASA’s compliance reports have made 

recommendations to STEM programs to address the presence of unconscious bias in meaningful 

ways, such as training tailored to a particular audience. NASA has published a “Title IX and STEM”

series that highlights promising grantee practices that assess the presence of bias and steps to 

address it.99 Holding grantees accountable for reducing the impact of implicit bias is a strategic 

component for promoting diversity in STEM.

 99 “Title IX & STEM: Promising Practices for Science, Technology, engineering, & Mathematics,” National Aeronautics & Space

 Administration, http://odeo.hq.nasa.gov/documents/71900_HI-RES.8-4-09.pdf; “Title IX & STEM: A Guide for Conducting 

 Title IX Self-Evaluations in Science, Technology, Engineering, and Mathematics Programs,” National Aeronautics & Space

 Administration, http://odeo.hq.nasa.gov/documents/TITLE_IX_STEM_Self-Evaluation_Fillable.pdf
Increasing Diversity in the STEM Workforce by Reducing the Impact of Bias

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Resources: Data Series Reports and Guides

Many past efforts to address bias in government, academia, professional societies, and industry 

have resulted in resources that can enable other institutions to launch initiatives to address 

implicit bias quickly and easily (see Appendix B). Some materials can be used as presented and 

others may need to be adapted to the organization or target audience, but the variety and 

abundance of the materials available provide many choices to address particular topics, use a 

preferred mode of presentation, or target an audience based on their role in an organization 

(e.g., leadership, managers, employees, or students).

Gaps and Scaling Questions

Promising practices are those that have been sustained and adopted or adapted in multiple 

places, but have not yet been studied or analyzed. For example, with monthly events, agencies 

collect participation counts but have not yet examined the impact of such practices. There is an 

opportunity here to address this question so that the agencies better understand if, how, and 

why these long-standing practices mitigate the impact of bias. If these agencies start to collect 

more evidence about the effectiveness and efficiency of promising practices and analyze those 

data, the activity might become a best practice. Data collection should focus on the ramifications 

of bias—hiring, retention, promotion, and cultural experiences of women, underrepresented 

minorities, and persons with disabilities—rather than on attempting to measure bias itself, which 

is neither easy to measure nor indicative of success in mitigating its effects.

The following scaling questions are relevant as agencies enhance their efforts to mitigate the 

impact of bias in the STEM workforce and learning environments:

• How are best practices adapted across the Federal agencies, including those from 

industry and professional societies?

• What evidence is needed to move from promising to best practices?

• What are the new features of longstanding practices that are appropriate for scale-up 

and what are incentives for more agencies to pilot new efforts, such as exploring 

creative approaches to prevent bias within the grant review process?

• Should the exploratory practice transition to a supportable practice?

• What are impactful strategies for collaborating with higher education to mitigate bias 

in a way that can be replicated?
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CHAPTER V. RECOMMENDATIONS AND IMPLEMENTATION

 

The reduction of the impact of bias in STEM disciplines and the workforce is critical if America is 

to maintain its competitive edge in the global economy of the 21st century, and fully utilize the 

Nation’s vast diversity of talent. Strategies for addressing the impact of bias in the STEM

workforce, both internally and in Federally funded institutions of higher education, must start 

with a commitment from top leadership to advance equality and cultivate the best talent, 

independent of demographic affiliations. The practices that reduce the impact of bias must be 

integrated into each agency’s core mission and at all levels of the organization. Predicated on 

these essential principles, the IPG developed the following policy recommendations to mitigate 

biases in: (1) the Federal STEM workforce, and (2) STEM programs in institutions of higher 

education that receive Federal funding.

Recommendation to Address Bias in the Federal Workforce

As research has shown, every Federal Agency and Department have a strategic interest in

developing an infrastructure to address bias (where it exists) and increase opportunity for 

diversity within its own workforce. It is critical that each agency integrate mechanisms for 

addressing bias into its strategic plan. Mitigating bias (where it exists) in the workplace requires

deliberate focus, including strong agency-wide communications strategies.

The following recommendation identifies key components that should be at the foundation of all

agencies’ and departments’ strategies for addressing bias. Implementation steps will need to 

accommodate each agency’s culture and work environment. 

Recommendation 1: All Federal agencies and departments should exercise leadership at all 

levels, including senior officials, STEM program and administrative managers, human-capital 

officials and diversity and inclusion officials, to reduce the impact of bias in their internal 

operations.

• Incorporation of diversity, inclusion, and bias mitigation into strategic plan. Agency 

strategic plans must convey the value of diversity, inclusion, equality of opportunity, and 

the importance of explicit- and implicit-bias mitigation. The principles of promoting 

diversity must permeate the agency, across senior leadership and all employees.

• Visible and regular participation, deep engagement, and demonstrated accountability by 

agency and department leaders in the dialogue and activities aimed at increasing diversity.

Agency leaders should be proactive, committed, and personally engaged in advancing the 

STEM workforce, reducing bias, and shifting cultures toward greater internalization of the 

principles of diversity and inclusion. Examples of actions include:
Increasing Diversity in the STEM Workforce by Reducing the Impact of Bias

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o Empowering staff with key responsibilities in improving diversity and inclusion by 

ensuring regular communications with the agency or departmental head who should 

regularly discuss with them policies and practices that reduce bias, and by ensuring 

this work is included in the staff’s key performance goals and metrics and is not 

considered a voluntary activity; and

o Ensuring strong collaboration in bias-mitigation initiatives among key offices within 

the agency or department, such as the Office of the Secretary, Administrator, or 

Director; EEO; D&I; Human Resources, Education; Small Business; and with external 

stakeholders, such as program offices and professional and advocacy groups. 

• Implementation of an organizational cycle of recruiting, hiring, and promotion practices that 

encourages diversity and reduce the impact of bias such as:

o Enhancing opportunities to draw from a diverse pool of applicants;

o Ensuring the use of objective and transparent criteria in hiring and promotion;

o Evaluating employee perceptions of fairness in the workplace; and

o Assessing demographics in hiring, promotion, performance ratings, and awards.

• Bias mitigation education and training. Each agency and department should develop or 

expand education and training on implicit- and explicit-bias mitigation.

• Employee empowerment and workforce engagement. Each agency and department should 

empower its employees at every level through support for attaining work-life balance

(including flexible work schedules), advancing employee-education and professionaldevelopment opportunities, and maintaining good communication. Agencies and 

departments should ensure that these aspects of the workplace are distributed fairly, and 

they should strive to build inclusive, collaborative, and open work environments and fully 

engage employees in bias-reduction efforts. Bias reduction, like all matters pertaining to 

culture change, requires a sustained effort over time. Examples of good practices include:

o Encouraging all employees to participate in opportunities for staff to be heard and 

receive feedback, have appropriate access to critical information, and making efforts 

to build or maintain an environment where diverse ideas and viewpoints are 

respected, valued, and encouraged; 

o Administering anonymous climate surveys agency-wide to reveal local manifestation 

of hidden bias and unfairness; 

o Conducting voluntary focus groups to assess whether alternative viewpoints are 

respected, valued, and encouraged, and whether employees are knowledgeable 

about critical developments in the Agency; 

o Maintaining a continuous program of learning opportunities, as resources permit, to 

ensure that all employees have basic skills to identify implicit bias (where it exists) and 

are expected to model behaviors that can mitigate the effects of such bias. These can 
Increasing Diversity in the STEM Workforce by Reducing the Impact of Bias

41

include multilayered training opportunities for senior managers, supervisors, and 

employees to develop awareness of and a means of mitigating implicit bias, and 

customized training that includes examples of hidden bias, forms of unfairness that 

are hurtful and demotivating, and safe forums in which to discuss these issues; and,

o Developing and maintaining robust work-life-balance policies, advocating for and 

utilizing flexibilities to the maximum extent practicable, and equitable access to these 

programs.

• Establishing bias-mitigation goals, techniques, and accountability mechanisms. Each agency 

should establish bias-reduction goals and accountability mechanisms to determine whether 

goals are being met. Accountability mechanisms should include the creation of an implicitbias-diagnostic tool to inform users and offer skill-development opportunities to aid 

individuals, teams, and organizations to better identify and, where it exists, better address 

unconscious biases. Examples include:

o Establishing appropriate benchmarks and monitoring performance over time; and 

o Reporting on challenges and progress in bias mitigation in relevant reports.

Recommendation to Address Bias at Federally Funded Academic Institutions of 

Higher Education

Overarching policy recommendations designed to help increase participation in the STEM fields 

through bias-reduction efforts at Federally funded institutions must reflect the strategic 

foundation on which Federal agencies are engaging with these programs, whether higher 

education, research institutes, or laboratories. The recommendations in this section are 

therefore based on the premise that agencies will incorporate these initiatives into their own 

strategic planning. A commitment to culture change must flow from top leadership and

permeate the agency at every level to exert influence in the academic environments that receive 

Agency support.

Recommendation 2: All Federal agencies should encourage the institutions they fund to 

implement bias-mitigation strategies by providing a model in the proposal-review process and by 

offering technical assistance and support for advancing inclusive STEM environments. Strategies 

and activities should include:

• Ensuring diversity in membership of grant-review panels; 

• Establishing a systematic means of collecting and analyzing data on the entire life cycle of

the grant-making process to analyze success rates in getting grants across groups; 

• Offering training or conducting workshops with staff involved in the grant-selection

process about the potential for bias to shape Federal-grant-making processes;

• Hosting workshops with grantees and institutional stakeholders to share research findings 

and develop bias interventions; 
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42

• Incorporating work-life-balance policies into grant-making processes and grant award 

agreements; 

• Offering grantees resources on diversity and promising practices for mitigating the 

effects of bias undertaken by government, academia, and the private sector;

• Encouraging grant recipients to conduct evidence-based workshops on implicit bias for 

department chairs, professors, deans, and administrators at all levels of the STEM 

pipeline; and 

• Encouraging institutional grantees to have strong family-friendly and work-life-balance 

policies in place (e.g., tenure-clock extensions, daycare, and lactation facilities). 

Recommendation for Cross-cutting Government Leadership: STEM Workforce 

and Federally Funded Institutions of Higher Education

It is incumbent upon the Federal Government as a leading investor in STEM research and 

development to serve as an example of strong leadership for all efforts to ensure that all 

Americans are welcomed into the STEM workforce. Although each agency will implement 

diversity strategies that are tailored to its culture and needs, efforts will be most effective if they 

are coordinated across agencies. Accordingly, the agencies will need a renewed spirit of 

coordination that accommodates the variation among agencies.

Recommendation 3: OSTP, OPM, and DOJ—as the agency responsible for coordinating 

government-wide civil-rights efforts—should coordinate Federal agencies among themselves 

and with Federally funded institutions to reduce the impact of bias in the STEM workforce. Ways 

to accomplish this include:

• Serving as focal points, clearinghouses, and distribution points for bias-reduction 

strategies and best practices for both Federal agencies and Federally funded institutions. 

• Coordinating civil-rights compliance efforts.

o DOJ should continue to coordinate agencies’ efforts to assess compliance among 

Federally funded institutions and assist them in their efforts to increase diversity 

through bias-reduction efforts. To address the goals presented in the GAO’s 

December 2015 report on challenges facing women, which is relevant to all 

underrepresented groups, DOJ should also:

 Facilitate interagency information-sharing on best practices in Title IX 

compliance; and

 Improve and coordinate compliance reviews across agencies.

• Providing guidance to agencies related to performance and accountability in efforts to 

mitigate the impact of explicit and implicit bias (where it exists) by investigation of 

potential measurement tools;
Increasing Diversity in the STEM Workforce by Reducing the Impact of Bias

43

• Spurring greater strategic coordination, sharing, and collaborating on successful 

programs aimed at reducing bias and increasing diversity at Federally funded institutions. 

• Strengthening University-Community-Government collaborations to mitigate the impact 

of bias and expand pathways to Federal STEM employment. Examples include the 

following:

o Providing outreach to local schools, colleges, and universities to provide diverse role 

models to inspire students and encourage students to study STEM by dispelling

stereotypes about who can participate in STEM; 

o Working with community organizations, STEM-advocacy groups, and professional 

societies to encourage underrepresented populations to pursue STEM careers, 

provide bias-training opportunities, and share innovative practices to mitigate the 

impact of bias in STEM; and

o Working with professional societies and other STEM organizations to expand 

recruitment efforts for the Federal workforce by providing role models for

underrepresented populations to inspire and dispel stereotypes among junior 

members of the STEM community. 

Implementation and Next Steps

To guide the implementation of these recommendations, the Interagency Policy Group further 

proposes the following actions. 

• An interagency body, acting as a community of practice and drawn from the NSTC and 

OPM bodies should: 

o Coordinate, share, and review the Government-wide implementation and scaling of 

best and promising practices; 

o Develop design principles for adapting practices to specific environments; 

o Identify gaps in research, policy, and practice; 

o Develop a diagnostic tool, comprising a set of quantitative metrics and strategies, to 

track increased diversity in the Federal STEM workforce by the reduction of the impact 

of bias where it may exist; and

o Develop a comprehensive, updated inventory of policies and practices by the Federal 

government and Federally funded institutions to reduce the impact of bias in the 

STEM workforce.

• OPM and Federal agencies should implement a public-engagement campaign, 

strategically targeted to key stakeholders, to highlight the existence, challenges, and 

impacts of bias and ways to reduce it in the STEM workforce.
Increasing Diversity in the STEM Workforce by Reducing the Impact of Bias

44

• Each Federal agency and department should develop a 1-page plan of action to 

implement and institutionalize policies and practices for reducing the impact of bias on 

the education, employment, and advancement of members of groups historically 

underrepresented or underserved in STEM fields. Such policies and practices must be 

designed to make careers in STEM more welcoming and conducive to the success of all 

people, including women, members of underrepresented ethnic and racial groups, and 

people with disabilities. Agency plans should be best-practice driven, include measurable 

goals, and be published in easy-to-understand forms, such as dashboards, to be reviewed 

by OPM on an annual basis. 

• Federal agencies, institutions of higher education, and the proposed interagency 

implementation body should develop accountability measures that can be used to assess 

progress on reducing bias (where it exists) in STEM education and the workforce. This 

work should entail the development of a system of measurements and strategies to 

measure progress that would include creation of an implicit-bias-diagnostic tool. These 

entities should also review research to accelerate progress in the emerging fields of biasmitigation, diversity, and inclusion policies and practices. Best practices emerging from 

research should also be brought back in-house to accelerate use of new research-based 

tools and strategies in practice.
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APPENDIX A 

Summaries of Agency Reports on Efforts to Mitigate the Impact of Bias

As part of the IPG process to identify best and promising practices, agencies and departments 

provided reports on their efforts to raise awareness about bias and mitigate its impact using the 

format below.

I. Agency STEM Workforce (including National and Federal Laboratories)

a. Implicit Individual Bias: Unintended and unconscious assumptions, often based on 

stereotypes about gender or ethnicity, which influence individuals’ judgments 

about other people or their work.

b. Implicit Institutional Bias: Institution or agency policy and practices that make it 

more difficult for members of certain groups to succeed.

c. Explicit Bias: Intentional, consciously articulated beliefs that spur discriminatory 

attitudes and behaviors.

II. Federally Funded Institutions of Higher Education STEM Workforce

(e.g., Graduate and Postdoctoral Students; Faculty; Staff; Administrators; or Institutional 

 Climate)

a. Implicit Individual: Unintended and unconscious assumptions, often based on 

stereotypes about gender or ethnicity, which influence individuals’ judgments 

about other people or their work.

b. Implicit Institutional: Institution or agency policy and practices that make it more 

difficult for members of certain groups to succeed.

c. Explicit: Intentional, consciously articulated beliefs that spur discriminatory 

attitudes and behaviors.

Agency and department websites for their respective one-page summaries on their efforts to 

raise awareness about bias and mitigate its impact can be found below:

• Department of Agriculture (USDA): https://www.ascr.usda.gov/about-oascr

• Department of Defense (DOD): http://www.dodstem.us/blog-posts/2016/dodagency-final-report-mitigating-bias-in-stem-workforce

• Department of Education (ED): http://innovation.ed.gov/what-we-do/stem/ 

• Department of Energy (DOE): http://www.energy.gov/diversity/bias-mitigation

• Department of Health and Human Services (DHHS): www.hhs.gov/stem-biasmitigation-report
Increasing Diversity in the STEM Workforce by Reducing the Impact of Bias

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• Department of Homeland Security (DHS): https://www.dhs.gov/publication/stemworkforce-diversity

• Department of Interior (DOI): https://www.doi.gov/notices/stem-eo

• Department of Labor (DOL): https://blog.dol.gov/2016/07/20/strengthening-ourworkforce-through-diversity-and-opportunity/

• Department of Transportation (DOT): https://www.transportation.gov/civilrights/about-docr/reducing-impact-bias-stem-workforce

• Environmental Protection Agency (EPA): 

https://www.epa.gov/aboutepa/reducing-impact-bias-stem-workforce

• National Aeronautics and Space Administration (NASA): 

http://odeo.hq.nasa.gov/documents/NASA_Mitigating_Bias_Agency_Progress_Rpt

_6-23-16_tagged.pdf

• National Science Foundation (NSF): 

http://www.nsf.gov/od/broadeningparticipation/bp.jsp

• National Oceanic and Atmospheric Administration (NOAA), a Bureau of the 

Department of Commerce: http://www.epp.noaa.gov/

• United States Patent and Trademark Office (USPTO), a Bureau of the Department 

of Commerce: http://www.uspto.gov/about-us/organizational-offices/officeequal-employment-opportunity-and-diversity/reducing-impact

• Smithsonian Institution (SI): http://www.si.edu/oeema/DivInitiatives.htm
Increasing Diversity in the STEM Workforce by Reducing the Impact of Bias

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APPENDIX B

Resources for Reducing the Impact of Bias

Federal Agencies

ED published a practice guide, “Encouraging Girls in Math and Science”, to provide specific and 

coherent evidence-based recommendations that educators can use to encourage girls in the 

fields of math and science. The target audience is teachers and other school personnel with 

direct contact with students, such as coaches, counselors, and principals. The practice guide is 

available online at: http://ies.ed.gov/ncee/wwc/PracticeGuide.aspx?sid=5.

DOL’s Bureau of Labor Statistics produces several data series with detail by occupation, including 

specific STEM occupations. Available data include employment and wages by detailed 

occupation (from the Occupational Employment Statistics program), 10-year occupational 

employment projections (from the Employment Projections program), information on job 

characteristics and duties (from the Occupational Outlook Handbook), data on employee benefits 

(from the National Compensation Survey), and data on workplace safety (from the Survey of 

Occupational Injuries and Illnesses and the Census of Fatal Occupational Injuries). Taken 

together, these data series provide information to employers, employees, career counselors, 

students, researchers, and others on the current employment and working conditions of many 

detailed occupations, including STEM occupations. 

NASA’s MissionSTEM website, http://missionstem.nasa.gov/, supplements the agency’s 

compliance review program and is the centerpiece of its civil-rights technical assistance to 

grantees. MissionSTEM is embedded in the core operations of NASA’s grantee civil-rights 

compliance efforts, and is deemed essential for making progress in this arena. The site provides 

a wide array of material on civil rights, including promising practices. NASA performs regular 

analytics on the site to measure its success. This includes monitoring “hits” and “views,” which 

consistently trend upward following email blasts to alert grantees of new content. Grantees have 

applauded NASA for technical-assistance efforts. In September 2014, NASA added a learning tool 

on implicit bias to its Mission STEM website.

100 The tool, “Unconscious Bias in STEM: Addressing 

the Challenges,” is designed to assist administrators, faculty, staff, and students of programs 

funded by NASA by providing them with a better understanding of bias and how it can impact 

STEM-educational environments. 

 

100 http://missionstem.nasa.gov/eLearn.html
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NSF’s ADVANCE provides a repository of information on bias that includes the following policies 

and practices being employed in higher education: leadership (e.g., Office of Institutional Equity), 

policy (e.g., career flexibility policies), training/education (e.g., training for college deans and 

department chairs, double blind survival strategies videos, research synthesis), recruitment (e.g., 

large and diverse pool of nominees/applicants, search committee strategies and guidelines such 

as STRIDE [Strategies and Tactics for Recruiting to Improve Diversity and Excellence]), retention 

(e.g., faculty retention toolkit), advancement (e.g., recognition for diversity contributions), and 

accountability (e.g., bias assessments, analysis of culture survey data).101

The Smithsonian Science Education Center (SSEC), (https://ssec.si.edu/) provides extensive 

training for teachers at the K-12 level across the country and has developed a comprehensive 

curriculum (Science and Technology ConceptsSTCTM) that supports higher-order teaching 

strategies to improve student learning of STEM, integrated with history, art and culture in grades 

K-8. A five-year rigorous study of its “Leadership and Assistance for Science Education Reform 

(LASER)” model demonstrates that LASER leads to measurably higher student achievement for 

all students—especially those students who are economically disadvantaged, are English 

language learners, or have disabilities.102 Together with Shell Oil Foundation and the National 

Science Teachers’ Association, the SSEC has also embarked on a 3-year initiative to address the 

unintended assumptions, often based on stereotypes about gender or ethnicity, which have 

served as a possible barrier in the advancement of STEM teachers of color serving in leadership

positions. Three distinct strategies have been prioritized for mitigating systemic biases and 

improving the engagement of minority teachers in STEM: (1) focusing on city- and district-level 

systems change; (2) starting a marketing campaign to rebrand the teaching profession to make it 

more attractive, including to people of color; and (3) increasing and improving mentorship and 

the intentional instruction of leadership competencies for STEM teachers. 

Industry

Many large STEM related companies (e.g., chemicals, petroleum, engineering, etc.) have affinity 

groups, e.g., a group for women, Hispanics, LGBT, etc., but not implicit-bias training. There is 

considerable implicit bias training in Silicon Valley. A number of consulting firms and industry 

membership organizations provide implicit bias training or models of best practices. But the 

training is proprietary. Some of the consulting firms providing implicit-bias training include: 

Workplace Answers, Catalyst, Cook Ross, Nextions, Paradigm, Vaya, and Unitive.103 Some 

companies that have addressed implicit bias include: 

 101 ADVANCE Program Solicitation NSF 14-573. Also, www.portal.advance.vt.edu/

102 https://ssec.si.edu/laser-i3

103 http://www.forbes.com/sites/ellenhuet/2015/11/02/rise-of-the-bias-busters-how-unconscious-bias-became-silicon-valleysnewest-target/
Increasing Diversity in the STEM Workforce by Reducing the Impact of Bias

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• Arla Foods has a 2-day bias-training session for leadership teams. Among the results 

(local) are a 25 percent increase in the success rate of recruitment and the annual 

engagement survey (global) show a 19 percent increase in the employees experiencing 

that their differences are being used more. Leaders report that they are much more 

conscious about challenging “us” and “them” groups, and more actively seek out diverse 

perspectives.”104 

• Google developed a workshop,105 in which employee participants reported positive 

impacts; participants were significantly more aware, had greater understanding, and 

were more motivated to overcome bias. In addition to partnering with organizations like 

the Clayman Institute and the Ada Initiative to further research and awareness, Google is 

taking action to ensure that decision-making—from promoting employees to marketing 

products—is objective and fair. Four practices are being utilized to reduce the influence 

of bias: gather facts, create a structure for making decision, be mindful of subtle cues, and 

foster awareness.106 Google also has an implicit-bias training video up on YouTube.107

• Salesforce Inc. is addressing unconscious bias through awareness training and 

competencies-based interviewing processes. Additionally, a process is in place where at 

least one female candidate or underrepresented minority is interviewed for executive 

positions. Over the last 12 months, nearly 40 percent of all new Salesforce hires in the 

United States were either women, African American, Hispanic, Native American, 

Hawaiian, or two or more races—an increase of approximately 5 percent over the 38 

percent in the previous 12 months.108

Professional Societies

A brief review of the websites of selected professional STEM organizations resulted in identifying 

several important resources related to bias mitigation. Engineers Australia has an Action Plan for 

Mitigating Gender Bias that can be found at this website.109

 104 Source: Nudging the Unconscious Mind for Inclusiveness Tinna C. Nielsen & Lisa Kepinski, October 2014© Copyrights 

 reserved Tinna C. Nielsen and Lisa Kepinski, 2014. Please request permission from the authors to share this Whitepaper 

 with others beyond your own personal use. 

 http://weprinciples.org/files/attachments/WHITE_PAPER_Nudging_the_Unconscious_Mind_Nielsen_&_Kepinski_Oct_2014. 

pdf

105 https://library.gv.com/unconscious-bias-at-work-22e698e9b2d#.i614ebsqz

106 “Google’s Workplace Diversity Still has a Long Way to Go” Kia Kokalitcheva/Fortune, Time Magazine, June 1, 2015, 

https://googleblog.blogspot.com/2014/09/you-dont-know-what-you-dont-know-how.html

107 https://www.youtube.com/watch?v=nLjFTHTgEVU

108 https://www.salesforce.com/blog/2015/08/working-toward-more-diverse-salesforcefuture.html?utm_content=buffer4c30b&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer Cindy 

 Robbins is the EVP, Global Employee Success, Salesforce.

109 https://www.engineersaustralia.org.au/sites/default/files/wie_ind ustryblueprint_p3_digital.pdf
Increasing Diversity in the STEM Workforce by Reducing the Impact of Bias

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The following three professional societies, in collaboration with AWIS, have developed best 

practices for selection of awardees that address implicit bias: the American Chemical Society;110

the Mathematical Association of America;111 and the American Geophysical Union.112

The Society for Neuroscience has a video on implicit bias that is available only to members 113

and also a Department Chair Training to Increase Women in Neuroscience (IWiN) project to tackle 

these issues within the neuroscience community. The website114 indicates progress has been 

made in some institutions for the project as a whole, but not clearly due to implicit-bias training.

 110 http://www.acs.org/content/dam/acsorg/funding/awards/national/selection-committee-best-practices 

 percent20_2015.pdf

111 http://www.maa.org/sites/default/files/pdf/sections_archived/Sections_ImplicitBiasStatement.pdf 

112 http://honors.agu.org/files/2014/11/AWARDS-SUGGESTED-BEST-PRACTICES-MAY2011.pdf

113 http://neuronline.sfn.org/Articles/Diversity/2015/Questioning-Implicit-Bias

114 http://www.sfn.org/Careers-and-Training/Women-in-Neuroscience/Department-Chair-Training-to-IncreaseDiversity/Promotion-and-Tenure/Leveling-the-Playing-Field
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ABBREVIATIONS 

ADVANCE: Increasing the Participation and Advancement of Women in Academic Science 

and Engineering Careers

BUILD: Building Infrastructure Leading to Diversity 

CLF: Civilian Labor Force

CSR: Center for Scientific Review

DCA: Diversity Change Agents

CEO: Chief Executive Officer

CMP: Conflict Management Program

CRD: Civil Rights Division

D&I: Diversity & Inclusion

DISP: Diversity and Inclusion Strategic Plan

DOC: Department of Commerce

DOD: Department of Defense

DOE: Department of Energy

DHHS: Department of Health and Human Services

DHS: Department of Homeland Security

DOL: Department of Labor

DOI: Department of Interior

DOJ: Department of Justice

DOT: Department of Transportation

DPC: Domestic Policy Council

ED: Department of Education

EEOC: Equal Employment Opportunity Commission

EEO: Equal Employment Opportunity

EPA: Environmental Protection Agency

EO: Equal Opportunity

EOP: Executive Office of the President

EPP: Educational Partnership Program

ERG: Employee Resource Groups

ETA: Employment and Training Administration

FEORP: Federal Equal Opportunity Recruitment Plan

FEVS: Federal Employee Viewpoint Survey

FY: Fiscal Year

GAO: Government Accountability Office

HHS: U.S. Department of Health and Human Services

IAT: Implicit Association Test

INCLUDES: Inclusion across the Nation of Communities of Learners of Underrepresented 

Discoverers in Engineering and Science

IPG: Interagency Policy Group
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IQT: Inclusion Quotient Training

IWiN: Increase Women in Neuroscience

LASER: Leadership and Assistance for Science Education Reform

LGBT: Lesbian, Gay, Bisexual, Transgender

NASA: National Aeronautics and Space Administration

NCWIT: National Center for Women & Information Technology

NIFA: National Institutes for Food and Agriculture

NIH: National Institutes of Health

NIGMS: National Institute for General Medical Sciences

NOAA: National Oceanic and Atmospheric Administration

NSF: National Science Foundation

NSTC: National Science and Technology Council

ODI: Office of Diversity and Inclusion

OPM: Office of Personnel Management

OSTP: Office of Science and Technology Policy

PASSS: Pattern-recognition, Action-oriented, Self-interest, Stability, Social

RCR: Responsible Conduct of Research

R&D: Research & Development

SBP: Science of Broadening Participation

S&E: Science and Engineering

S&T: Science and Technology

SEEDS: Similarity, Expedience, Experience, Distance, Safety

SES: Senior Executive Service

STC: Science and Technology Concepts

STEM: Science, Technology, Engineering and Mathematics

STRIDE: Strategies and Tactics for Recruiting to Improve Diversity and Excellence

US: United States

USDA: United States Department of Agriculture

USGS: United States Geological Survey

USPTO: United States Patent and Trademark Office