Diversity in Tech Statistics: In today's tech-driven world, discussions about diversity in the technology sector have gained significant traction. Recent statistics shed light on the disparities and opportunities within this industry. According to data from various sources, including reports from leading tech companies and diversity advocacy groups, the lack of diversity remains a prominent issue. For example, studies reveal that only 25% of computing jobs in the United States are held by women, while Black and Hispanic individuals make up just 9% of the tech workforce combined. Additionally, research indicates that LGBTQ+ individuals are underrepresented in tech, with only 2.3% of tech workers identifying as LGBTQ+. Despite these challenges, there are promising signs of progress. Companies are increasingly recognizing the importance of diversity and inclusion initiatives, with some allocating significant resources to address these issues. For instance, tech giants like Google and Microsoft have committed millions of USD to diversity programs aimed at recruiting and retaining underrepresented talent. As discussions surrounding diversity in tech continue to evolve, understanding the statistical landscape is crucial in fostering meaningful change and creating a more inclusive industry for all. Editor’s Choice In 2021, 7.9% of the US labor force was employed in technology. Women hold only 26.7% of tech employment, while men hold 73.3% of these positions. White Americans hold 62.5% of the positions in the US tech sector. Asian Americans account for 20% of jobs, Latinx Americans 8%, and Black Americans 7%. 83.3% of tech executives in the US are white. Black Americans comprised 14% of the population in 2019 but held only 7% of tech employment. For the same position, at the same business, and with the same experience, women in tech are typically paid 3% less than men. The high-tech sector employs more men (64% against 52%), Asian Americans (14% compared to 5.8%), and white people (68.5% versus 63.5%) compared to other industries. The tech industry is urged to prioritize inclusion when hiring, mentoring, and retaining employees to bridge the digital skills gap. Black professionals only account for 4% of all tech workers despite being 13% of the US workforce. Hispanic professionals hold just 8% of all STEM jobs despite being 17% of the national workforce. Only 22% of workers in tech are ethnic minorities. Gender diversity in tech is low, with just 26% of jobs in computer-related sectors occupied by women. Companies with diverse teams have higher profitability, with those in the top quartile for gender diversity being 25% more likely to have above-average profitability. Every month, the tech industry adds about 9,600 jobs to the U.S. economy. Between May 2009 and May 2015, over 800,000 net STEM jobs were added to the U.S. economy. STEM jobs are expected to grow by another 8.9% between 2015 and 2024. The percentage of black and Hispanic employees at major tech companies is very low, making up just one to three percent of the tech workforce. Tech hiring relies heavily on poaching and incentives, creating an unsustainable ecosystem ripe for disruption. Recruiters have a significant role in disrupting the hiring process to support diversity and inclusion. You May Also Like To Read Outsourcing Statistics Digital Transformation Statistics Internet of Things Statistics Computer Vision Statistics

According to a survey conducted in 2024, over **** of Americans believed that diversity, equity, and inclusion (DEI) efforts in the workplace generally helped Black women, Black men, and Hispanic women in the United States. A further ** percent shared this same belief for Hispanic men. In contrast, only ** percent said that DEI practices helped white men at work in that year.

In 2023, approximately ** percent of Goldman Sachs' total U.S. workforce identified as white. The second-largest ethnic group was Asian at ** percent, followed by Hispanic at ***** percent and Black/African American at **** percent. The share of white employees declined compared to previous years, while the representation of Asian and Hispanic employees saw a slight increase. Meanwhile, the share of Black/African American employees experienced a slight decrease.

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Explore demographic data on the Massachusetts executive branch workforce. Track our progress toward our goals to reflect the diversity of the people we serve, and to stand out as an employer of choice.

Institutions of higher education (IHE) throughout the United States have a long history of acting out various levels of commitment to diversity advancement, equity, and inclusion (DEI). Despite decades of DEI “efforts,” the academy is fraught with legacies of racism that uphold white supremacy and prevent marginalized populations from full participation. Furthermore, politicians have not only weaponized education but passed legislation to actively ban DEI programs and censor general education curricula (https://tinyurl.com/antiDEI). Ironically, systems of oppression are particularly apparent in the fields of Ecology, Evolution, and Conservation Biology (EECB)–which recognize biological diversity as essential for ecological integrity and resilience. Yet, amongst EECB faculty, people who do not identify as cis-heterosexual, non-disabled, affluent white males are poorly represented. Furthermore, IHE lack metrics to quantify DEI as a priority. Here we show that only 30.3% of US-faculty positions advertised in EECB from Jan 2019-May 2020 required a diversity statement; diversity statement requirements did not correspond with state-level diversity metrics. Though many announcements “encourage women and minorities to apply,” empirical evidence demonstrates that hiring committees at most institutions did not prioritize an applicant’s DEI advancement potential. We suggest a model for change and call on administrators and faculty to implement SMART (i.e., Specific, Measurable, Achievable, Realistic, and Timely) strategies for DEI advancement across IHE throughout the United States. We anticipate our quantification of diversity statement requirements relative to other application materials will motivate institutional change in both policy and practice when evaluating a candidate’s potential “fit”. IHE must embrace a leadership role to not only shift the academic culture to one that upholds DEI, but to educate and include people who represent the full diversity of our society. In the current context of political censure of education including book banning and backlash aimed at Critical Race Theory, which further reinforce systemic white supremacy, academic integrity and justice are more critical than ever. Methods Here we investigated the (lack of) process in faculty searches at IHE for evaluating candidates’ ability to advance DEI objectives. We quantified the prevalence of required diversity statements relative to research and/or teaching statements for all faculty positions posted to the Eco-Evo Jobs Board (http://ecoevojobs.net) from January 2019 - May 2020 as a proxy for institutional DEI prioritization (Supplement). We also mapped the job posts that required diversity statements geographically to gauge whether and where diversity is valued in higher education across the US. Data analysis We pulled all faculty jobs posted on Eco-Evo jobs board (http://ecoevojobs.net) from Jan 1, 2019, to May 31, 2020. For each position, we recorded the Location (i.e., state), Subject Area, Closing Date, Rank, whether or not the position is Tenure Track, and individual application materials (i.e., Research statement, Teaching statement, combined Teaching and Research statement, Diversity statement, Mentorship statement). Of the 543 faculty positions posted during this time, we eliminated 299 posts because the web links were broken or application information was no longer available (i.e., “NA”), leaving 244 faculty job posts. For each of the retained posts, we coded the requirement of teaching, research, diversity, and/or mentorship statements as follows:

"Yes” = statement required “No” = statement not required “Other” = application materials did not explicitly require a Diversity Statement (i.e., option or suggested that applicants include a statement on diversity and inclusion as a component of their teaching and/or research statement or in their cover letter)

Data visualization We created a Sankey diagram using Sankey Flow Show (THORTEC Software GmbH: www.sankeyflowshow.com) to compare diversity and representation from the general population, through (Science, Technology, Engineering, and Mathematics) STEM academia (a career hierarchy often referred to as the “leaky pipeline”). We procured population data from the US Census Bureau (US Department of Commerce: https://www.census.gov/quickfacts/fact/table/US/PST045219) and quantified the diversity/representation in Conservation Biology (https://datausa.io/profile/cip/ecology-evolution-systematics-population-biology#demographics) and Ecology (https://datausa.io/profile/cip/conservation-biology) using Data USA (developed by Deloitte Touche Tohmatsu Limited and Datawheel). We used the 2015 Diversity Index (produced by PolicyLink and the USC Program for Environmental and Regional Equity: https://nationalequityatlas.org/indicators/Diversity_index/Ranking:33271/United_States/false/Year(s):2015/) to quantify relative ethnic diversity per state, and graphed Figure 2B using the tidyverse, rgdal, broom, and rgeos packages in R (see Base code used to produce Figure 2 in R, below). The Diversity index measures the representation of White, Black, Latino, Asian/Pacific Islander, Native American, and Mixed/other race in a given population. A maximum possible diversity score (1.79) would indicate even representation of all ethnic/racial groups. We checked all figures using the Color Blindness Simulator (ColBlindor: https://www.color-blindness.com/coblis-color-blindness-simulator/) to maintain inclusivity.

The American Medical Informatics Association (AMIA) Task Force on Diversity, Equity, and Inclusion (DEI) was established to address systemic racism and health disparities within biomedical and health informatics, aligning with AMIA’s mission to transform healthcare through trusted science, education, and informatics practice. AMIA member voices, responding to police brutality and COVID-19's impact on Black and/or African American communities, spurred the creation of AMIA’s DEI initiatives. The Task Force proposed actionable recommendations to the AMIA Board of Directors across five domains: translational bioinformatics, clinical research, consumer, clinical, and public health informatics. In the first 9 months, the Task Force (1) created a logic model to support workforce diversity and raise AMIA's DEI awareness, (2) conducted an environmental scan of other associations’ DEI activities, (3) developed a DEI framework for AMIA meetings, (4) gathered member feedback, (5) cultivated DEI educational resources, (6) created an informational session on Board nominations and diversity, (7) reviewed the Board’s Strategic Planning documentation to align DEI efforts, (8) led a program to increase attendee diversity at the 2020 AMIA Virtual Annual Symposium, and (9) standardized data collection of socially-assigned race and ethnicity data. The Task Force’s collaborative and comprehensive approach helped AMIA understand its member diversity within the context of systemic racism and health equity. This work supported marginalized groups, broadened the research agenda, and positioned AMIA as a DEI leader in informatics. Transforming informatics and AMIA to represent the diversity of those AMIA serves is a journey; AMIA’s journey is still unfolding.

This dataset tracks annual two or more races student percentage from 2013 to 2023 for Della S. Lindley Elementary School vs. California and Palm Springs Unified School District

Historical Dataset of Della S. Lindley Elementary School is provided by PublicSchoolReview and contain statistics on metrics:Total Students Trends Over Years (1987-2023),Total Classroom Teachers Trends Over Years (1990-2023),Distribution of Students By Grade Trends,Student-Teacher Ratio Comparison Over Years (1990-2023),American Indian Student Percentage Comparison Over Years (1988-2014),Asian Student Percentage Comparison Over Years (1988-2023),Hispanic Student Percentage Comparison Over Years (1991-2023),Black Student Percentage Comparison Over Years (1992-2023),White Student Percentage Comparison Over Years (1991-2023),Two or More Races Student Percentage Comparison Over Years (2013-2023),Diversity Score Comparison Over Years (1991-2023),Free Lunch Eligibility Comparison Over Years (1993-2023),Reduced-Price Lunch Eligibility Comparison Over Years (2002-2023),Reading and Language Arts Proficiency Comparison Over Years (2010-2022),Math Proficiency Comparison Over Years (2010-2022),Overall School Rank Trends Over Years (2010-2022)

BackgroundThe integration of Artificial Intelligence (AI) in nephrology has raised concerns regarding bias, fairness, and ethical decision-making, particularly in the context of Diversity, Equity, and Inclusion (DEI). AI-driven models, including Large Language Models (LLMs) like ChatGPT, may unintentionally reinforce existing disparities in patient care and workforce recruitment. This study investigates how AI models (ChatGPT 3.5 and 4.0) handle DEI-related ethical considerations in nephrology, highlighting the need for improved regulatory oversight to ensure equitable AI deployment.MethodsThe study was conducted in March 2024 using ChatGPT 3.5 and 4.0. Eighty simulated cases were developed to assess ChatGPT’s decision-making across diverse nephrology topics. ChatGPT was instructed to respond to questions considering factors such as age, sex, gender identity, race, ethnicity, religion, cultural beliefs, socioeconomic status, education level, family structure, employment, insurance, geographic location, disability, mental health, language proficiency, and technology access.ResultsChatGPT 3.5 provided a response to all scenario questions and did not refuse to make decisions under any circumstances. This contradicts the essential DEI principle of avoiding decisions based on potentially discriminatory criteria. In contrast, ChatGPT 4.0 declined to make decisions based on potentially discriminatory criteria in 13 (16.3%) scenarios during the first round and in 5 (6.3%) during the second round.ConclusionWhile ChatGPT 4.0 shows improvement in ethical AI decision-making, its limited recognition of bias and DEI considerations underscores the need for robust AI regulatory frameworks in nephrology. AI governance must incorporate structured DEI guidelines, ongoing bias detection mechanisms, and ethical oversight to prevent AI-driven disparities in clinical practice and workforce recruitment. This study emphasizes the importance of transparency, fairness, and inclusivity in AI development, calling for collaborative efforts between AI developers, nephrologists, policymakers, and patient communities to ensure AI serves as an equitable tool in nephrology.