Artificial IntelligenceIndex Report 2021

CHAPTER 6:

Diversity in AI

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CHAPTER 6:
DIVERSITY IN AI

CHAPTER 6:

Chapter Preview
Overview

3

Chapter Highlights

4

6.1 GENDER DIVERSITY IN AI

5

Women in Academic AI Settings

5

Women in the AI Workforce

6

Women in Machine Learning Workshops 7
Workshop Participants

7

Demographics Breakdown8

6.2 RACIAL AND ETHNIC
DIVERSITY IN AI

10

New AI PhDs in the United Statesby Race/Ethnicity

10

New Computing PhDs in the
United States by Race/Ethnicity

11

CS Tenure-Track Facultyby Race/Ethnicity

12Black in AI

12

6.3 GENDER IDENTITY ANDSEXUAL ORIENTATION IN AI

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Queer in AI

13

Demographics Breakdown

13

Experience as Queer Practitioners

15

APPENDIX

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ACCESS THE PUBLIC DATACHAPTER 6 PREVIEW

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CHAPTER 6:
DIVERSITY IN AIOV E R V I E W

Overview
While artificial intelligence (AI) systems have the potential to dramatically affect society,the people building AI systems are not representative of the people those systems aremeant to serve. The AI workforce remains predominantly male and lacking in diversity inboth academia and the industry, despite many years highlighting the disadvantages andrisks this engenders. The lack of diversity in race and ethnicity, gender identity, and sexual
orientation not only risks creating an uneven distribution of power in the workforce, butalso, equally important, reinforces existing inequalities generated by AI systems, reduces
the scope of individuals and organizations for whom these systems work, and contributes
to unjust outcomes.
This chapter presents diversity statistics within the AI workforce and academia. It draws
on collaborations with various organizations—in particular, Women in Machine Learning(WiML), Black in AI (BAI), and Queer in AI (QAI)— each of which aims to improve diversity
in some dimension in the field. The data is neither comprehensive nor conclusive. In
preparing this chapter, the AI Index team encountered significant challenges as a resultof the sparsity of publicly available demographic data. The lack of publicly available
demographic data limits the degree to which statistical analyses can assess the impactof the lack of diversity in the AI workforce on society as well as broader technology
development. The diversity issue in AI is well known, and making more data available
from both academia and industry is essential to measuring the scale of the problem andaddressing it.
There are many dimensions of diversity that this chapter does not cover, including AI
professionals with disabilities; nor does it consider diversity through an intersectional
lens. Other dimensions will be addressed in future iterations of this report. Moreover,these diversity statistics tell only part of the story. The daily challenges of minorities andmarginalized groups working in AI, as well as the structural problems within organizations
that contribute to the lack of diversity, require more extensive data collection and analysis.

1 We thank Women in Machine Learning, Black in AI, and Queer in AI for their work to increase diversity in AI, for sharing their data, and for partnering with us.

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CHAPTERHIGHLIGHTS

CHAPTER HIGHLIGHTS• The percentages of female AI PhD graduates and tenure-track computer science(CS) faculty have remained low for more than a decade. Female graduates of AI PhD
programs in North America have accounted for less than 18% of all PhD graduates on
average, according to an annual survey from the Computing Research Association(CRA). An AI Index survey suggests that female faculty make up just 16% of all tenuretrack CS faculty at several universities around the world.
• The CRA survey suggests that in 2019, among new U.S. resident AI PhD graduates,
45% were white, while 22.4% were Asian, 3.2% were Hispanic, and 2.4% were African
American.
• The percentage of white (non-Hispanic) new computing PhDs has changed little
over the last 10 years, accounting for 62.7% on average. The share of Black or
African American (non-Hispanic) and Hispanic computing PhDs in the same period issignificantly lower, with an average of 3.1% and 3.3%, respectively.
• The participation in Black in AI workshops, which are co-located with the Conference
on Neural Information Processing Systems (NeurIPS), has grown significantly in recent
years. The numbers of attendees and submitted papers in 2019 are 2.6 times higher thanin 2017, while the number of accepted papers is 2.1 times higher.
• In a membership survey by Queer in AI in 2020, almost half the respondents said they
view the lack of inclusiveness in the field as an obstacle they have faced in becoming
a queer practitioner in the AI/ML field. More than 40% of members surveyed said they
have experienced discrimination or harassment as a queer person at work or school.

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6.1 G E N D E R
DIVERSITY IN AI

6.1 GENDER DIVERSITY IN AI
WO M E N I N ACA D E M I C
AI SETTINGSTENURE-TRACK FACULTY at CS DEPARTMENTS of
TOP UNIVERSITIES
the
WORLD
by GENDER,TENURE-TRACKaround
FACULTY
at CS
DEPARTMENTS
of
Chapter 4 introduced the AI Index survey that evaluates
TOP
UNIVERSITIES
aroundthe
WORLD
byGENDER,AY 2019-20
the state of AI education in CS departments at top
AY 2019-20
Source: AI Index,
2020 | Chart: 2021 AI Index Report
Source: AI Index, 2020 | Chart: 2021 AI Index Report
universities around the world, along with the Computer
Research Association’s annual Taulbee Survey on the
110 (16.1%)
enrollment, production, and employment of PhDs
110
(16.1%)
FemaleFemalein information, computer science, and computerengineering in North America.
Data from both surveys show that the percentage offemale AI and CS PhD graduates as well as tenure-track CS
faculty remains low. Female graduates of AI PhD programs
and CS PhD programs have accounted for 18.3% of all
PhD graduates on average within the past 10 years (Figure
6.1.1). Among the 17 universities that completed the AI
Index survey of CS programs globally, female faculty makeup just 16.1% of all tenure-track faculty whose primaryresearch focus area is AI (Figure 6.1.2).

575 (83.9%)
Male575 (83.9%)
Male

Figure 6.1.2

FEMALE NEW AI and CS PHDS (% of TOTAL NEW AI and CS PHDS) in NORTH AMERICA, 2010-19
Source: CRA Taulbee Survey, 2020 | Chart: 2021 AI Index Report

Female New AI PhDs (% of All New AI PhDs)

30%

25%
22.1% AI20%

20.3% CS

15%

10%
2010

2011

2012

2013

2014

2015

2016

2017

2018

2019
Figure 6.1.1

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6.1 G E N D E R
DIVERSITY IN AI

WO M E N I N T H E A I WO R K F O R C E
Chapter 3 introduced the “global relative AI skills
penetration rate,” a measure that reflects the prevalenceof AI skills across occupations, or the intensity with whichpeople in certain occupations use AI skills. Figure 6.1.3
shows AI skills penetration by country for female andmale labor pools in a set of select countries.2 The datasuggest that across the majority of these countries, theAI skills penetration rate for women is lower than thatfor men. Among the 12 countries we examined, India,
South Korea, Singapore, and Australia are the closest toreaching equity in terms of the AI skills penetration rateof females and males.

This data suggests thatacross the majority of
select countries, theAI skills penetration
rate for women is lower
than it is for men.

RELATIVE AI SKILLS PENETRATION RATE by GENDER, 2015-20
Source: LinkedIn, 2020 | Chart: 2021 AI Index Report

India
United States
South Korea
Singapore
China
CanadaFranceGermany
Australia
United Kingdom

FemaleMale

South Africa
Italy
0

1

2

3

Relative AI Skills Penetration Rate
Figure 6.1.3

2 Countries included are a select sample of eligible countries with at least 40% labor force coverage by LinkedIn and at least 10 AI hires in any given month. China and India were included in this sample
because of their increasing importance in the global economy, but LinkedIn coverage in these countries does not reach 40% of the workforce. Insights for these countries may not provide as full a picture
as other countries, and should be interpreted accordingly.

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WO M E N I N M AC H I N E L E A R N I N G
WO R KS H O P S
Women in Machine Learning, founded in 2006 by
Hanna Wallach, Jenn Wortman, and Lisa Wainer, is anorganization that runs events and programs to support
women in the field of machine learning (ML). Thissection presents statistics from its annual technicalworkshops, which are held at NeurIPS. In 2020, WiML
also hosted for the first time a full-day “Un-Workshop” at
the International Conference on Machine Learning 2020,
which drew 812 participants.

Workshop Participants
The number of participants attending WiML workshops at
NeurIPS has been steadily increasing since the workshops
were first offered in 2006. According to the organization,
the WiML workshop in 2020 was completely virtual

because of the pandemic and delivered on a new platform(Gather.Town); these two factors may make attendance
numbers harder to compare to those of previous years.
Figure 6.1.4 shows an estimate of 925 attendees in 2020,
based on the number of individuals who accessed thevirtual platform.
In the past 10 years, WiML workshops have expanded
their programs to include mentoring roundtables, wheremore senior participants offer one-on-one feedback andprofessional advice, in addition to the main session that
includes keynotes and poster presentations. Similar
opportunities may have contributed to the increase inattendance since 2014. Between 2016 and 2019, the WiML
workshop attendance is on average about 10% of theoverall NeurIPS attendance.

NUMBER of PARTICIPANTS at WIML WORKSHOP at NEURIPS, 2006-20
Source: Women in Machine Learning, 2020 | Chart: 2021 AI Index Report

1,000
925

Number of Participants

800

600

400

200

2020

2019

2018

2017

2016

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

0

Figure 6.1.4

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Demographics Breakdown
The following geographic, professional position, andgender breakdowns are based only on participants atthe 2020 WiML workshop at NeurIPS who consented tohaving the information aggregated and who spent atleast 10 minutes on the virtual platform through which
the workshop was offered. Among the participants,
89.5% were women and/or nonbinary, 10.4% were men(Figure 6.1.5), and a large majority were from North
America (Figure 6.1.6). Further, as shown in Figure 6.1.7,
students—including PhD, master’s, and undergraduate
students—make up more than half the participants(54.6%). Among participants who work in the industry,
research scientist/engineer and data scientist/engineer
are the most commonly held professional positions.

PARTICIPANTS of WIML WORKSHOP at NEURIPS(% of
TOTAL) byofGENDER,
2020 at NEURIPS
PARTICIPANTS
WIML WORKSHOP
Source:in Machine
Learning, 2020
| Chart: 2021 AI Index Report(%Womenof TOTAL)
by GENDER,
2020
Source: Women in Machine Learning, 2020 | Chart: 2021 AI Index Report

6.1 G E N D E R
DIVERSITY IN AI

Among theparticipants, 89.5%
were women and/or nonbinary, 10.4%
were men, and a large
majority were from
North America. Further,students—including
PhD, master’s, andundergraduate
students—make up
more than half theparticipants (54.6%).

10.4%10.4%
Man Man

89.5%
Woman and/or nonbinary

89.5%
Woman and/or nonbinary
Figure 6.1.5

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6.1 G E N D E R
DIVERSITY IN AIPARTICIPANTS of WIML WORKSHOP at NEURIPS (% of TOTAL) by CONTINENT of RESIDENCE, 2020
Source: Women in Machine Learning, 2020 | Chart: 2021 AI Index Report

North America
EuropeAsia
AfricaCentral, South America, and the Carribean
Australia and Oceania
Middle East
0%

10%

20%

30%

40%

50%

60%

% of Participants
Figure 6.1.6

PARTICIPANTS of WIML WORKSHOP at NEURIPS (% of TOTAL) by TOP 10 PROFESSIONAL POSITIONS, 2020
Source: Women in Machine Learning, 2020 | Chart: 2021 AI Index Report

PhD Student
Research Scientist/Engineer
MSc Student
Data scientist/Engineer
Undergraduate Student
Postdoctoral Researcher
Software Engineer
Professor (Pre-Tenure)
Professor (Post-Tenure)
Program/Product Manager
0%

5%

10%

15%

20%

25%

30%

35%

% of Participants
Figure 6.1.7

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6. 2 R AC I A L
AND ETHNIC
DIVERSITY IN AI

CHAPTER 6:
DIVERSITY IN AI

6.2 RACIAL AND ETHNIC DIVERSITY IN AI
N E W A I P H D S I N T H E U N I T E D S TAT E S BY R AC E / E T H N I C I T Y
According to the CRA Taulbee Survey, among the new AI PhDs in 2019 who are U.S. residents, the largest
percentage (45.6%) are white (non-Hispanic), followed by Asian (22.4%). By comparison, 2.4% were African
American (non-Hispanic) and 3.2% were Hispanic (Figure 6.2.1).

NEW U.S. RESIDENT AI PHDS (% of TOTAL) by RACE/ETHNICITY, 2019

NEW U.S. RESIDENT
AICRA
PHDS
(% Survey,of TOTAL)
by RACE/ETHNICITY,
Source:Taulbee2020 | Chart:
2021 AI Index Report 2019
Source: CRA Taulbee Survey, 2020 | Chart: 2021 AI Index Report

22.4% Asian
45.6% White(non-Hispanic)

22.4% Asian

45.6% White(non-Hispanic)
2.4% Black or African
American(non-Hispanic)
3.2%
Hispanic
1.6% Multiracial(non-Hispanic)

3.2%
Hispanic
1.6% Multiracial(non-Hispanic)

24.8% Unknown

24.8% Unknown

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2.4% Black or African
American(non-Hispanic)

Figure 6.2.1

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6. 2 R AC I A L
AND ETHNIC
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CHAPTER 6:
DIVERSITY IN AI

NEW COMPUTING PHDS IN
T H E U N I T E D S TAT E S BY
R AC E / E T H N I C I T Y

The CRA survey
indicates that thepercentage of white(non-Hispanic) new
PhDs has changed little
over the last 10 years,
accounting for 62.7%
on average.

Figure 6.2.2 shows all PhDs awarded in the United States
to U.S. residents across departments of computer science(CS), computer engineering (CE), and information (I)between 2010 and 2019. The CRA survey indicates thatthe percentage of white (non-Hispanic) new PhDs haschanged little over the last 10 years, accounting for 62.7%
on average. The share of new Black or African American(non-Hispanic) and Hispanic computing PhDs in the same
period is significantly lower, with an average of 3.1%
and 3.3%, respectively. We are not able to compare thenumbers between new AI and CS PhDs in 2019 becauseof the number of unknown cases (24.8% for new AI PhDs
and 8.5% for CS PhDs).

NEW COMPUTING PHDS, U.S. RESIDENT (% of TOTAL) by RACE/ETHNICITY, 2010-19
Source: CRA Taulbee Survey, 2020 | Chart: 2021 AI Index Report

70%

New Computing PhDs, U.S. Resident (% of Total)

60%
58.9% White (non-Hispanic)

50%

40%

30%
24.4% Asian

20%

10%

8.5% Unknown
3.2% Hispanic, any race
2.5% Black or African American (non-Hispanic)
1.7% Multiracial (non-Hispanic)

0%0.6% Native Hawaiian/Pac Islander
0.3% Amer Indian or Alaska Native

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022
Figure 6.2.2

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C S T E N U R E -T R AC K
FAC U LT Y BY R AC E /ETHNICITY

6. 2 R AC I A L
AND ETHNIC
DIVERSITY IN AI

CHAPTER 6:
DIVERSITY IN AITENURE-TRACK FACULTY (% of TOTAL) at CS DEPARTMENTS
of TOP UNIVERSITIES in the WORLD by RACE/ETHNICITY, 2019-20
TENURE-TRACKFACULTY(% ofReport
TOTAL) at CS DEPARTMENTS
Source:AI Index, 2020 | Chart:
2021 AI Index

of TOP UNIVERSITIES in the WORLD by RACE/ETHNICITY, 2019-20
Source: AI Index, 2020 | Chart: 2021 AI Index Report

0.8%
Hispanic,
0.6% 0.8%
Latino, or
Hispanic,
Black
or
0.6%
Black orAfricanLatino, or Spanish
SpanishAfricanorigin14.3%
14.3%Asian

Figure 6.2.3 shows data from the AI Index
education survey.3 Among 15 universities
that completed the question pertaining
to the racial makeup of their faculty,
approximately 67.0% of the tenure-track
faculty are white, followed by Asian (14.3%),
other races (8.3%), and mixed/other race,
ethnicity, or origin (6.3%). The smallest
representation among tenure-track faculty
are teachers of Black or African and of
Hispanic, Latino, or Spanish origins, who
account for 0.6% and 0.8%, respectively.

Asianorigin2.7%
2.7%
Middle Easternor Eastern or
MiddleNorth African
North African

6.3%
Mixed/other6.3%
race,
ethnicity, or Mixed/otherorigin
race,
67.0%
White

8.3%
Other races

67.0%
White

ethnicity, or origin

8.3%
Other races
Figure 6.2.3

B L AC K I N A I
Black in AI (BAI), founded in 2017 by Timnit Gebru
and Rediet Abebe, is a multi-institutional andtranscontinental initiative that aims to increase thepresence of Black people in the field of AI. As of 2020, BAI
has around 3,000 community members and allies, has
held more than 10 workshops at major AI conferences,and has helped increase the number of Black people

participating at major AI conferences globally 40-fold.
Figure 6.2.4 shows the number of attendees, submitted
papers, and accepted papers from the annual Black in
AI Workshop, which is co-located with NeurIPS.4 The
numbers of attendees and accepted papers in 2019are 2.6 times higher than in 2017, while the number of
accepted papers is 2.1 times higher.

NUMBER OF ATTENDEES, SUBMITTED PAPERS, and ACCEPTED PAPERS at BLACK in AI WORKSHOP CO-LOCATED
with NEURIPS, 2017-19
Source: Black in AI, 2020 | Chart: 2021 AI Index Report

Number of
Attendees

Submitted
Papers2017
2018

Accepted
Papers

2019
0

100

200

300
Number of Attendees and Papers

400

500
Figure 6.2.4

3 The survey was distributed to 73 universities online over three waves from November 2020 to January 2021 and completed by 18 universities, a 24.7% response rate. The 18 universities are Belgium:
Katholieke Universiteit Leuven; Canada: McGill University; China: Shanghai Jiao Tong University, Tsinghua University; Germany: Ludwig Maximilian University of Munich, Technical University of Munich;Russia: Higher School of Economics, Moscow Institute of Physics and Technology; Switzerland: École Polytechnique Fédérale de Lausanne; United Kingdom: University of Cambridge; United States:
California Institute of Technology, Carnegie Mellon University (Department of Machine Learning), Columbia University, Harvard University, Stanford University, University of Wisconsin–Madison,
University of Texas at Austin, Yale University.
4 The 2020 data are clearly affected by the pandemic and not included as a result. For more information, see the Black in AI impact report.

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O R I E N TAT I O N I N A I

6.3 GENDER IDENTITY ANDSEXUAL ORIENTATION IN AI
QUEER IN AI
This section presents data from a membership survey
by Queer in AI (QAI), 5 an organization that aims to makethe AI/ML community one that welcomes, supports,
and values queer scientists. Founded in 2018 by William
Agnew, Raphael Gontijo Lopes, and Eva Breznik, QAI
builds a visible community of queer and ally AI/ML
scientists through meetups, poster sessions, mentoring,and other initiatives.

Demographics Breakdown
According to the 2020 survey, with around 100 responses,

about 31.5% of respondents identify as gay, followedby bisexual, queer, and lesbian (Figure 6.3.1); around
37.0% and 26.1% of respondents identify as cis maleand cis female, respectively, followed by gender queer,
gender fluid, nonbinary, and others (Figure 6.3.2). Transfemale and male account for 5.0% and 2.5% of total
members, respectively. Moreover, the past three years ofsurveys show that students make up the majority of QAI
members—around 41.7% of all respondents on average(Figure 6.3.3), followed by junior-level professionals inacademia or industry.

QAI MEMBERSHIP SURVEY: WHAT IS YOUR SEXUAL ORIENTATION, 2020
Source: Queer in AI, 2020 | Chart: 2021 AI Index Report

Gay
Bisexual
Queer
LesbianStraight
Asexual
Pansexual
Others
0%

5%

10%

15%

20%

25%

30%

% of Respondents
Figure 6.3.1

5 Queer in AI presents the survey results at its workshop at the annual NeurIPS conference.

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A N D S E X UA L
O R I E N TAT I O N I N A I

QAI MEMBERSHIP SURVEY: WHAT IS YOUR GENDER IDENTITY, 2020
Source: Queer in AI, 2020 | Chart: 2021 AI Index Report

Cis Male

Cis Female

Gender Queer

Gender Fluid

NonBinary and Others

Trans Female
0%

5%

10%

15%

20%

25%

30%

35%

% of Respondents
Figure 6.3.2

QAI MEMBERSHIP SURVEY: HOW WOULD YOU DESCRIBE YOUR POSITION, 2018-20
Source: Queer in AI, 2020 | Chart: 2021 AI Index Report

Student

JuniorAcademic

JuniorIndustry

2018
2019
2020

Others

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

% of Respondents
Figure 6.3.3

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Experience as Queer Practitioners

6. 3 G E N D E R I D E N T I T Y
A N D S E X UA L
O R I E N TAT I O N I N A I

Among surveyed QAI
members, 81.4% regard
the lack of role models
as being a major obstacle
for their careers, and70.9% think the lack ofcommunity contributes tothe same phenomenon.

QAI also surveyed its members on their experiences
as queer AI/ML practitioners. As shown in Figure
6.3.4, 81.4% regard the lack of role models as beinga major obstacle for their careers, and 70.9% thinkthe lack of community contributes to the same
phenomenon. Almost half the respondents also
view the lack of inclusiveness in the field as an
obstacle. Moreover, more than 40% of QAI members
have experienced discrimination or harassment
as a queer person at work or school (Figure 6.3.5).
Around 9.7% have encountered discrimination orharassment on more than five occasions.

QAI MEMBERSHIP SURVEY: WHAT ARE OBSTACLES YOU HAVE FACED in BECOMING a QUEER AI/ML
PRACTITIONER, 2020
Source: Queer in AI, 2020 | Chart: 2021 AI Index Report

Lack of Role Models

Lack of Community

Lack of InclusivenessLack of Work/School Support

Economic Hardship Now

Harrassment/Discrimination
0%

10%

20%

30%

40%

50%

60%

70%

80%

% of Respondents
Figure 6.3.4

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More than 40% of QAI members have experienced
discrimination or harassment as a queer person at workor school. Around 9.7% have encountered discriminationor harassment on more than five occasions.

QAI MEMBERSHIP SURVEY: HAVE YOU EXPERIENCED DISCRIMINATION/HARASSMENT as a QUEER PERSON at YOURJOB or SCHOOL, 2020
Source: Queer in AI, 2020 | Chart: 2021 AI Index Report

0 times

1 time2 times5+ times

Others

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

50%

55%

% of Respondents
Figure 6.3.5

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APPENDIX

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APPENDIX
LINKEDINAI Skills Penetration
The aim of this indicator is to measure the intensity ofAI skills in an entity (in a particular country, industry,
gender, etc.) through the following methodology:• Compute frequencies for all self-added skills by
LinkedIn members in a given entity (occupation,industry, etc.) in 2015–2020.
• Re-weight skill frequencies using a TF-IDF model to get
the top 50 most representative skills in that entity. These
50 skills compose the “skill genome” of that entity.
• Compute the share of skills that belong to the AI skill
group out of the top skills in the selected entity.
Interpretation: The AI skill penetration rate signals theprevalence of AI skills across occupations, or the intensitywith which LinkedIn members utilize AI skills in theirjobs. For example, the top 50 skills for the occupation ofengineer are calculated based on the weighted frequency
with which they appear in LinkedIn members’ profiles. If
four of the skills that engineers possess belong to the AI
skill group, this measure indicates that the penetration of
AI skills is estimated to be 8% among engineers (e.g., 4/50).

Global Comparison: By Gender
The relative AI skill penetration by country for genderprovides an in-depth decomposition of AI skills
penetration across female and male labor pools andsample countries.
Interpretation: A country’s relative AI skill penetration
rate of 2 for women means that the average penetrationof AI skills among women in that country is two times
the global average across the same set of occupations
among women. If, in the same country, the relative AI
skill penetration rate for men is 1.9, this indicates thatthe average penetration of AI skills among women inthat country is 5% higher than that of men (calculated by
dividing 1.9 by 2 and then subtracting 1, or 2/1.9-1) for
the same set of occupations.

Relative AI Skills Penetration
To allow for skills penetration comparisons acrosscountries, the skills genomes are calculated and a
relevant benchmark is selected (e.g., global average).
A ratio is then constructed between a country’s andthe benchmark’s AI skills penetrations, controlling foroccupations.
Interpretation: A country’s relative AI skills penetrationof 1.5 indicates that AI skills are 1.5 times as frequent as
in the benchmark, for an overlapping set of occupations.

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