Artificial IntelligenceIndex Report 2021CHAPTER 6:Diversity in AICHAPTER 6 PREVIEWArtificial IntelligenceIndex Report 20211
Artificial IntelligenceIndex Report 2021CHAPTER 6:DIVERSITY IN AICHAPTER 6:Chapter PreviewOverview3Chapter Highlights46.1 GENDER DIVERSITY IN AI5Women in Academic AI Settings5Women in the AI Workforce6Women in Machine Learning Workshops 7Workshop Participants7Demographics Breakdown86.2 RACIAL AND ETHNICDIVERSITY IN AI10New AI PhDs in the United Statesby Race/Ethnicity10New Computing PhDs in theUnited States by Race/Ethnicity11CS Tenure-Track Facultyby Race/Ethnicity12Black in AI126.3 GENDER IDENTITY ANDSEXUAL ORIENTATION IN AI13Queer in AI13Demographics Breakdown13Experience as Queer Practitioners15APPENDIX17ACCESS THE PUBLIC DATACHAPTER 6 PREVIEW2
Artificial IntelligenceIndex Report 2021CHAPTER 6:DIVERSITY IN AIOV E R V I E WOverviewWhile 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 sexualorientation not only risks creating an uneven distribution of power in the workforce, butalso, equally important, reinforces existing inequalities generated by AI systems, reducesthe scope of individuals and organizations for whom these systems work, and contributesto unjust outcomes.This chapter presents diversity statistics within the AI workforce and academia. It drawson 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 diversityin some dimension in the field. The data is neither comprehensive nor conclusive. Inpreparing this chapter, the AI Index team encountered significant challenges as a resultof the sparsity of publicly available demographic data. The lack of publicly availabledemographic 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 technologydevelopment. The diversity issue in AI is well known, and making more data availablefrom 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 AIprofessionals with disabilities; nor does it consider diversity through an intersectionallens. 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 organizationsthat 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.CHAPTER 6 PREVIEW3
Artificial IntelligenceIndex Report 2021CHAPTER 6:DIVERSITY IN AICHAPTERHIGHLIGHTSCHAPTER 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 PhDprograms in North America have accounted for less than 18% of all PhD graduates onaverage, 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 AfricanAmerican. The percentage of white (non-Hispanic) new computing PhDs has changed littleover the last 10 years, accounting for 62.7% on average. The share of Black orAfrican 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 Conferenceon Neural Information Processing Systems (NeurIPS), has grown significantly in recentyears. 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 theyview the lack of inclusiveness in the field as an obstacle they have faced in becominga queer practitioner in the AI/ML field. More than 40% of members surveyed said theyhave experienced discrimination or harassment as a queer person at work or school.CHAPTER 6 PREVIEW4
Artificial IntelligenceIndex Report 2021CHAPTER 6:DIVERSITY IN AI6.1 G E N D E RDIVERSITY IN AI6.1 GENDER DIVERSITY IN AIWO M E N I N ACA D E M I CAI SETTINGSTENURE-TRACK FACULTY at CS DEPARTMENTS ofTOP UNIVERSITIEStheWORLDby GENDER,TENURE-TRACKaroundFACULTYat CSDEPARTMENTSofChapter 4 introduced the AI Index survey that evaluatesTOPUNIVERSITIESaroundtheWORLDbyGENDER,AY 2019-20the state of AI education in CS departments at topAY 2019-20Source: AI Index,2020 Chart: 2021 AI Index ReportSource: AI Index, 2020 Chart: 2021 AI Index Reportuniversities around the world, along with the ComputerResearch Association’s annual Taulbee Survey on the110 (16.1%)enrollment, production, and employment of PhDs110(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 CSfaculty remains low. Female graduates of AI PhD programsand CS PhD programs have accounted for 18.3% of allPhD graduates on average within the past 10 years (Figure6.1.1). Among the 17 universities that completed the AIIndex 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%)MaleFigure 6.1.2FEMALE NEW AI and CS PHDS (% of TOTAL NEW AI and CS PHDS) in NORTH AMERICA, 2010-19Source: CRA Taulbee Survey, 2020 Chart: 2021 AI Index ReportFemale New AI PhDs (% of All New AI PhDs)30%25%22.1% AI20%20.3% gure 6.1.1CHAPTER 6 PREVIEW5
Artificial IntelligenceIndex Report 2021CHAPTER 6:DIVERSITY IN AI6.1 G E N D E RDIVERSITY IN AIWO M E N I N T H E A I WO R K F O R C EChapter 3 introduced the “global relative AI skillspenetration rate,” a measure that reflects the prevalenceof AI skills across occupations, or the intensity with whichpeople in certain occupations use AI skills. Figure 6.1.3shows 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 ofselect countries, theAI skills penetrationrate for women is lowerthan it is for men.RELATIVE AI SKILLS PENETRATION RATE by GENDER, 2015-20Source: LinkedIn, 2020 Chart: 2021 AI Index ReportIndiaUnited StatesSouth ted KingdomFemaleMaleSouth AfricaItaly0123Relative AI Skills Penetration RateFigure 6.1.32 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 samplebecause 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 pictureas other countries, and should be interpreted accordingly.CHAPTER 6 PREVIEW6
Artificial IntelligenceIndex Report 2021CHAPTER 6:DIVERSITY IN AI6.1 G E N D E RDIVERSITY IN AIWO M E N I N M AC H I N E L E A R N I N GWO R KS H O P SWomen in Machine Learning, founded in 2006 byHanna Wallach, Jenn Wortman, and Lisa Wainer, is anorganization that runs events and programs to supportwomen in the field of machine learning (ML). Thissection presents statistics from its annual technicalworkshops, which are held at NeurIPS. In 2020, WiMLalso hosted for the first time a full-day “Un-Workshop” atthe International Conference on Machine Learning 2020,which drew 812 participants.Workshop ParticipantsThe number of participants attending WiML workshops atNeurIPS has been steadily increasing since the workshopswere first offered in 2006. According to the organization,the WiML workshop in 2020 was completely virtualbecause of the pandemic and delivered on a new platform(Gather.Town); these two factors may make attendancenumbers 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 expandedtheir programs to include mentoring roundtables, wheremore senior participants offer one-on-one feedback andprofessional advice, in addition to the main session thatincludes keynotes and poster presentations. Similaropportunities may have contributed to the increase inattendance since 2014. Between 2016 and 2019, the WiMLworkshop attendance is on average about 10% of theoverall NeurIPS attendance.NUMBER of PARTICIPANTS at WIML WORKSHOP at NEURIPS, 2006-20Source: Women in Machine Learning, 2020 Chart: 2021 AI Index Report1,000925Number of 14201320122011201020092008200720060Figure 6.1.4CHAPTER 6 PREVIEW7
Artificial IntelligenceIndex Report 2021CHAPTER 6:DIVERSITY IN AIDemographics BreakdownThe 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 whichthe 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 NorthAmerica (Figure 6.1.6). Further, as shown in Figure 6.1.7,students—including PhD, master’s, and undergraduatestudents—make up more than half the participants(54.6%). Among participants who work in the industry,research scientist/engineer and data scientist/engineerare the most commonly held professional positions.PARTICIPANTS of WIML WORKSHOP at NEURIPS(% ofTOTAL) byofGENDER,2020 at NEURIPSPARTICIPANTSWIML WORKSHOPSource:in MachineLearning, 2020 Chart: 2021 AI Index Report(%Womenof TOTAL)by GENDER,2020Source: Women in Machine Learning, 2020 Chart: 2021 AI Index Report6.1 G E N D E RDIVERSITY IN AIAmong theparticipants, 89.5%were women and/or nonbinary, 10.4%were men, and a largemajority were fromNorth America. Further,students—includingPhD, master’s, andundergraduatestudents—make upmore than half theparticipants (54.6%).10.4%10.4%Man Man89.5%Woman and/or nonbinary89.5%Woman and/or nonbinaryFigure 6.1.5CHAPTER 6 PREVIEW8
Artificial IntelligenceIndex Report 2021CHAPTER 6:DIVERSITY IN AI6.1 G E N D E RDIVERSITY IN AIPARTICIPANTS of WIML WORKSHOP at NEURIPS (% of TOTAL) by CONTINENT of RESIDENCE, 2020Source: Women in Machine Learning, 2020 Chart: 2021 AI Index ReportNorth AmericaEuropeAsiaAfricaCentral, South America, and the CarribeanAustralia and OceaniaMiddle East0%10%20%30%40%50%60%% of ParticipantsFigure 6.1.6PARTICIPANTS of WIML WORKSHOP at NEURIPS (% of TOTAL) by TOP 10 PROFESSIONAL POSITIONS, 2020Source: Women in Machine Learning, 2020 Chart: 2021 AI Index ReportPhD StudentResearch Scientist/EngineerMSc StudentData scientist/EngineerUndergraduate StudentPostdoctoral ResearcherSoftware EngineerProfessor (Pre-Tenure)Professor (Post-Tenure)Program/Product Manager0%5%10%15%20%25%30%35%% of ParticipantsFigure 6.1.7CHAPTER 6 PREVIEW9
Artificial IntelligenceIndex Report 20216. 2 R AC I A LAND ETHNICDIVERSITY IN AICHAPTER 6:DIVERSITY IN AI6.2 RACIAL AND ETHNIC DIVERSITY IN AIN 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 YAccording to the CRA Taulbee Survey, among the new AI PhDs in 2019 who are U.S. residents, the largestpercentage (45.6%) are white (non-Hispanic), followed by Asian (22.4%). By comparison, 2.4% were AfricanAmerican (non-Hispanic) and 3.2% were Hispanic (Figure 6.2.1).NEW U.S. RESIDENT AI PHDS (% of TOTAL) by RACE/ETHNICITY, 2019NEW U.S. RESIDENTAICRAPHDS(% Survey,of TOTAL)by RACE/ETHNICITY,Source:Taulbee2020 Chart:2021 AI Index Report 2019Source: CRA Taulbee Survey, 2020 Chart: 2021 AI Index Report22.4% Asian45.6% White(non-Hispanic)22.4% Asian45.6% White(non-Hispanic)2.4% Black or AfricanAmerican(non-Hispanic)3.2%Hispanic1.6% Multiracial(non-Hispanic)3.2%Hispanic1.6% Multiracial(non-Hispanic)24.8% Unknown24.8% UnknownCHAPTER 6 PREVIEW2.4% Black or AfricanAmerican(non-Hispanic)Figure 6.2.110
Artificial IntelligenceIndex Report 20216. 2 R AC I A LAND ETHNICDIVERSITY IN AICHAPTER 6:DIVERSITY IN AINEW COMPUTING PHDS INT H E U N I T E D S TAT E S BYR AC E / E T H N I C I T YThe CRA surveyindicates that thepercentage of white(non-Hispanic) newPhDs has changed littleover the last 10 years,accounting for 62.7%on average.Figure 6.2.2 shows all PhDs awarded in the United Statesto 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 sameperiod 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 PhDsand 8.5% for CS PhDs).NEW COMPUTING PHDS, U.S. RESIDENT (% of TOTAL) by RACE/ETHNICITY, 2010-19Source: CRA Taulbee Survey, 2020 Chart: 2021 AI Index Report70%New Computing PhDs, U.S. Resident (% of Total)60%58.9% White (non-Hispanic)50%40%30%24.4% Asian20%10%8.5% Unknown3.2% Hispanic, any race2.5% Black or African American (non-Hispanic)1.7% Multiracial (non-Hispanic)0%0.6% Native Hawaiian/Pac Islander0.3% Amer Indian or Alaska 20212022Figure 6.2.2CHAPTER 6 PREVIEW11
Artificial IntelligenceIndex Report 2021C S T E N U R E -T R AC KFAC U LT Y BY R AC E /ETHNICITY6. 2 R AC I A LAND ETHNICDIVERSITY IN AICHAPTER 6:DIVERSITY IN AITENURE-TRACK FACULTY (% of TOTAL) at CS DEPARTMENTSof TOP UNIVERSITIES in the WORLD by RACE/ETHNICITY, 2019-20TENURE-TRACKFACULTY(% ofReportTOTAL) at CS DEPARTMENTSSource:AI Index, 2020 Chart:2021 AI Indexof TOP UNIVERSITIES in the WORLD by RACE/ETHNICITY, 2019-20Source: AI Index, 2020 Chart: 2021 AI Index Report0.8%Hispanic,0.6% 0.8%Latino, orHispanic,Blackor0.6%Black orAfricanLatino, or SpanishSpanishAfricanorigin14.3%14.3%AsianFigure 6.2.3 shows data from the AI Indexeducation survey.3 Among 15 universitiesthat completed the question pertainingto the racial makeup of their faculty,approximately 67.0% of the tenure-trackfaculty are white, followed by Asian (14.3%),other races (8.3%), and mixed/other race,ethnicity, or origin (6.3%). The smallestrepresentation among tenure-track facultyare teachers of Black or African and ofHispanic, Latino, or Spanish origins, whoaccount for 0.6% and 0.8%, respectively.Asianorigin2.7%2.7%Middle EasternorMiddleNorth AfricanEastern orNorth African6.3%Mixed/other6.3%race,ethnicity, or Mixed/otherorigin67.0%White8.3%Other races67.0%Whiterace,ethnicity, or origin8.3%Other racesFigure 6.2.3B L AC K I N A IBlack in AI (BAI), founded in 2017 by Timnit Gebruand Rediet Abebe, is a multi-institutional andtranscontinental initiative that aims to increase thepresence of Black people in the field of AI. As of 2020, BAIhas around 3,000 community members and allies, hasheld more than 10 workshops at major AI conferences,and has helped increase the number of Black peopleparticipating at major AI conferences globally 40-fold.Figure 6.2.4 shows the number of attendees, submittedpapers, and accepted papers from the annual Black inAI Workshop, which is co-located with NeurIPS.4 Thenumbers of attendees and accepted papers in 2019are 2.6 times higher than in 2017, while the number ofaccepted papers is 2.1 times higher.NUMBER OF ATTENDEES, SUBMITTED PAPERS, and ACCEPTED PAPERS at BLACK in AI WORKSHOP CO-LOCATEDwith NEURIPS, 2017-19Source: Black in AI, 2020 Chart: 2021 AI Index ReportNumber 190100200300Number of Attendees and Papers400500Figure 6.2.43 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.CHAPTER 6 PREVIEW12
Artificial IntelligenceIndex Report 2021CHAPTER 6:DIVERSITY IN AI6. 3 G E N D E R I D E N T I T YA N D S E X UA LO R I E N TAT I O N I N A I6.3 GENDER IDENTITY ANDSEXUAL ORIENTATION IN AIQUEER IN AIThis section presents data from a membership surveyby 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 WilliamAgnew, Raphael Gontijo Lopes, and Eva Breznik, QAIbuilds a visible community of queer and ally AI/MLscientists through meetups, poster sessions, mentoring,and other initiatives.Demographics BreakdownAccording 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); around37.0% 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 totalmembers, respectively. Moreover, the past three years ofsurveys show that students make up the majority of QAImembers—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, 2020Source: Queer in AI, 2020 Chart: 2021 AI Index ualOthers0%5%10%15%20%25%30%% of RespondentsFigure 6.3.15 Queer in AI presents the survey results at its workshop at the annual NeurIPS conference.CHAPTER 6 PREVIEW13
Artificial IntelligenceIndex Report 2021CHAPTER 6:DIVERSITY IN AI6. 3 G E N D E R I D E N T I T YA N D S E X UA LO R I E N TAT I O N I N A IQAI MEMBERSHIP SURVEY: WHAT IS YOUR GENDER IDENTITY, 2020Source: Queer in AI, 2020 Chart: 2021 AI Index ReportCis MaleCis FemaleGender QueerGender FluidNonBinary and OthersTrans Female0%5%10%15%20%25%30%35%% of RespondentsFigure 6.3.2QAI MEMBERSHIP SURVEY: HOW WOULD YOU DESCRIBE YOUR POSITION, 2018-20Source: Queer in AI, 2020 Chart: 2021 AI Index 020Others0%5%10%15%20%25%30%35%40%45%% of RespondentsFigure 6.3.3CHAPTER 6 PREVIEW14
Artificial IntelligenceIndex Report 2021CHAPTER 6:DIVERSITY IN AIExperience as Queer Practitioners6. 3 G E N D E R I D E N T I T YA N D S E X UA LO R I E N TAT I O N I N A IAmong surveyed QAImembers, 81.4% regardthe lack of role modelsas being a major obstaclefor their careers, and70.9% think the lack ofcommunity contributes tothe same phenomenon.QAI also surveyed its members on their experiencesas queer AI/ML practitioners. As shown in Figure6.3.4, 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 samephenomenon. Almost half the respondents alsoview the lack of inclusiveness in the field as anobstacle. Moreover, more than 40% of QAI membershave experienced discrimination or harassmentas 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/MLPRACTITIONER, 2020Source: Queer in AI, 2020 Chart: 2021 AI Index ReportLack of Role ModelsLack of CommunityLack of InclusivenessLack of Work/School SupportEconomic Hardship 0%80%% of RespondentsFigure 6.3.4CHAPTER 6 PREVIEW15
Artificial IntelligenceIndex Report 20216. 3 G E N D E R I D E N T I T YA N D S E X UA LO R I E N TAT I O N I N A ICHAPTER 6:DIVERSITY IN AIMore than 40% of QAI members have experienceddiscrimination 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, 2020Source: Queer in AI, 2020 Chart: 2021 AI Index Report0 times1 time2 times5 timesOthers0%5%10%15%20%25%30%35%40%45%50%55%% of RespondentsFigure 6.3.5CHAPTER 6 PREVIEW16
Artificial IntelligenceIndex Report 2021APPENDIXCHAPTER 6:DIVERSITY IN AIAPPENDIXLINKEDINAI Skills PenetrationThe 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 byLinkedIn members in a given entity (occupation,industry, etc.) in 2015–2020. Re-weight skill frequencies using a TF-IDF model to getthe top 50 most representative skills in that entity. These50 skills compose the “skill genome” of that entity. Compute the share of skills that belong to the AI skillgroup 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 frequencywith which they appear in LinkedIn members’ profiles. Iffour of the skills that engineers possess belong to the AIskill group, this measure indicates that the penetration ofAI skills is estimated to be 8% among engineers (e.g., 4/50).Global Comparison: By GenderThe relative AI skill penetration by country for genderprovides an in-depth decomposition of AI skillspenetration across female and male labor pools andsample countries.Interpretation: A country’s relative AI skill penetrationrate of 2 for women means that the average penetrationof AI skills among women in that country is two timesthe global average across the same set of occupationsamong women. If, in the same country, the relative AIskill 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 bydividing 1.9 by 2 and then subtracting 1, or 2/1.9-1) forthe same set of occupations.Relative AI Skills PenetrationTo allow for skills penetration comparisons acrosscountries, the skills genomes are calculated and arelevant 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 asin the benchmark, for an overlapping set of occupations.CHAPTER 6 PREVIEW17
South Korea, Singapore, and Australia are the closest to reaching equity in terms of the AI skills penetration rate of females and males. 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
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tion diversity. Alpha diversity Dα measures the average per-particle diversity in the population, beta diversity Dβ mea-sures the inter-particle diversity, and gamma diversity Dγ measures the bulk population diversity. The bulk population diversity (Dγ) is the product of diversity on the per-particle
DEDICATION PART ONE Chapter 1 Chapter 2 Chapter 3 Chapter 4 Chapter 5 Chapter 6 Chapter 7 Chapter 8 Chapter 9 Chapter 10 Chapter 11 PART TWO Chapter 12 Chapter 13 Chapter 14 Chapter 15 Chapter 16 Chapter 17 Chapter 18 Chapter 19 Chapter 20 Chapter 21 Chapter 22 Chapter 23 .
diversity of the other strata. Beta (β) Diversity: β diversity is the inter community diversity expressing the rate of species turnover per unit change in habitat. Gamma (γ) Diversity : Gamma diversity is the overall diversity at landscape level includes both α and β diversities. The relationship is as follows: γ
alpha, beta, and gamma diversity. Alpha (α) diversity is local diversity, the diversity of a forest stand, a grassland, or a stream. At the other extreme is gamma (γ) diversity, the total regional diversity of a large area that contains several communities, such as the eastern deciduous forests
Alpha, gamma and beta diversity are theoretical constructs that describe the hierarchical, multiscale nature of diversity. Phyto-chemical alpha diversity is the average diversity at the scale of a single sampling unit (i.e. ‘local’ diversity). Gamma diversity is
local diversity (alpha diversity) and the complement of species composition among sites within the region (beta diversity), and how these diversities contribute to regional diversity (gamma diversity) [35, 37]. The influence of alpha and beta diversities on gamma diversity is an essential aspect of local and landscape level conservation plans .
About the husband’s secret. Dedication Epigraph Pandora Monday Chapter One Chapter Two Chapter Three Chapter Four Chapter Five Tuesday Chapter Six Chapter Seven. Chapter Eight Chapter Nine Chapter Ten Chapter Eleven Chapter Twelve Chapter Thirteen Chapter Fourteen Chapter Fifteen Chapter Sixteen Chapter Seventeen Chapter Eighteen
18.4 35 18.5 35 I Solutions to Applying the Concepts Questions II Answers to End-of-chapter Conceptual Questions Chapter 1 37 Chapter 2 38 Chapter 3 39 Chapter 4 40 Chapter 5 43 Chapter 6 45 Chapter 7 46 Chapter 8 47 Chapter 9 50 Chapter 10 52 Chapter 11 55 Chapter 12 56 Chapter 13 57 Chapter 14 61 Chapter 15 62 Chapter 16 63 Chapter 17 65 .
HUNTER. Special thanks to Kate Cary. Contents Cover Title Page Prologue Chapter 1 Chapter 2 Chapter 3 Chapter 4 Chapter 5 Chapter 6 Chapter 7 Chapter 8 Chapter 9 Chapter 10 Chapter 11 Chapter 12 Chapter 13 Chapter 14 Chapter 15 Chapter 16 Chapter 17 Chapter
Chapter 3 Chapter 4 Chapter 5 Chapter 6 Chapter 7 Chapter 8 Chapter 9 Chapter 10 Chapter 11 Chapter 12 Chapter 13 Chapter 14 Chapter 15 Chapter 16 Chapter 17 Chapter 18 Chapter 19 Chapter 20 . Within was a room as familiar to her as her home back in Oparium. A large desk was situated i
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techniques were described in chapter 4. It's important to combine two or more diversity techniques to get full advantage of diversity techniques. Some diversity combining techniques were described in chapter 5. The mathematical equations needed for simulation in diversity combining techniques were collected and defined in chapter 6.
Mary Barton A Tale of Manchester Life by Elizabeth Cleghorn Gaskell Styled byLimpidSoft. Contents PREFACE1 CHAPTER I6 CHAPTER II32 CHAPTER III51 CHAPTER IV77 CHAPTER V109 CHAPTER VI166 CHAPTER VII218 i. CHAPTER VIII243 CHAPTER IX291 CHAPTER X341 CHAPTER XI381 CHAPTER XII423 CHAPTER XIII450 CHAPTER XIV479 CHAPTER XV513 CHAPTER XVI551
IBM & Diversity: Why IBM works Diversity At IBM, diversity means more than the race, sex or physical abilities of an employee. Diversity is also about cultural differences, lifestyle, age, background, experience, religion, economic and social status, sexual orientation and marital st
cerned with the phenomenon of alpha-diversity, the species richness of samples representing communities (generally 102 -1IO mi2) (Whittaker, 1977). MacArthur (1965) and others use the term within-habitat diversity as a synonym of alpha-diversity. The diversity of landscapes (106_108 M2) is gamma-diversity. Each level or scale of inventory
May 15, 2008 · CHAPTER THREE CHAPTER FOUR CHAPTER FIVE CHAPTER SIX CHAPTER SEVEN CHAPTER EIGHT CHAPTER NINE CHAPTER TEN CHAPTER ELEVEN . It is suggested that there is a one-word key to the answer among the four lofty qualities which are cited on every man's commission. . CHAPTER TWO. CHAPTER THREE.
Book II Chapter I Chapter II Chapter III Chapter IV Chapter V Chapter VI Chapter VII Chapter VIII Chapter IX Chapter X Chapter XI Chapter XII Chapter XIII Chapter XIV Book III . The Storm and Stress period in German literature had been succeeded by the Romantic movement, but Goethe's classicism rendered him unsympathetic to it. Nevertheless .
Core 6 – Equality and Diversity . Status Core – this is a key aspect of all jobs and of everything that everyone does. It underpins all dimensions in the NHS KSF. Levels 1 Act in ways that support equality and value diversity . 2. Support equality and value diversity . 3. Promote equality and value diversity
4 2014 Diversity and Inclusion Report Our Approach The RBC Diversity Blueprint The Diversity Leadership Council continued to make progress on the RBC Diversity Blueprint 2012-2015 which outlines our pr