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Capitol Webinar November 2025 Emily Barnes.pdf

Slides from a Capitol Technology University webinar presented on Nov. 20, 2025 by Dr. Emily Barnes. Dr. Barnes discusses gender bias in Artificial Intelligence (Large Language Modeling) that impacts AI results and interaction. The webinar was the third and final webinar in the Cyber Leadership Series, hosted by the University's Center for Women in Cyber.

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1 Thank you for joining us! The webinar will begin at 12:00 p.m. (Noon) Eastern
Presented by Dr. Emily Barnes 2 Ms. AI – This is Your Moment! Addressing Gender Bias in AI
Bill Gibbs, M.A. Webinar Host 1. About Capitol Technology University 2. Session Pointers / Special Announcement 3. The Center for Women in Cyber 4. About the Presenter 5. Presentation 6. Q & A / Discussion 7. Upcoming Webinars 8. Recording, Slides, Certificate Agenda 3
About Established in 1927, and based in Laurel, MD, we are one of the few private Universities in the U.S. specifically dedicated to STEM-Based academic programs. The University offers regionally accredited degrees at the Associate, Bachelor, Master, and Doctoral levels. 4
• We will answer questions at the conclusion of the presentation. At any time you can post a question in the text chat and we will answer as many as we can. • Microphones and webcams are not activated for participants. • A link to the recording and to the slides will be sent to all registrants and available on our webinar web page. • A participation certificate is available by request for both Live Session and On Demand viewers. Session Pointers 5
We recognize three Webinar Participants who have attended 10 webinars IN A ROW! We Salute Three Very Special Participants! 6 Maša Radulović Serbia Racqel Massey USA Aslak Molvær Norway
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Presented by Dr. Emily Barnes 8 Ms. AI – This is Your Moment! Addressing Gender Bias in AI
Dr. Emily Barnes 9 • AI researcher, higher education leader and advocate for ethical technology & women in STEM • Has served as a university provost, chief digital learning officer, and consultant • Senior Content Strategist, UX/UI with Core Education • Board Advisor to Capitol’s Artificial Intelligence Center of Excellence (AICE) • Author of 24 scholarly articles • PhD in Artificial Intelligence and EdD in Organizational Leadership
Ms. AI – This is Your Moment Addressing Gender Bias in AI Dr. Emily Barnes
Gender Bias in Machine Learning Underrepresentation of Women in Technology A Way Forward Contents Generations of AI Bias Manifestation of Bias in Everyday AI Factors Contributing to Bias in AI AI and machine learning developer, has 17 years experience in higher education experience, serving as a Provost, COO, and Chief Digital Learning Officer, revolutionizing learning with a focus on AI-driven educational strategies. Dr. Emily Barnes Why it Matters
The idea that machines can act like humans AI based on rules created by humans. “Human-readable” AI that explains its choices. ARTIFICIAL INTELLIGENCE 1960 2020 AGENTIC AI SYMBOLIC SYSTEMS EXPLAINABLE AI (XAI) AI that can plan, take action, and manage tasks autonomously. 1950 2022 AI modeled on the human brain’s neural system. NEUROMORPHIC AI ARTIFICIAL GENERAL INTELLIGENCE (AGI) AI that can learn and reason across many domains. Shift from “rules given by humans” to “patterns learned by computers.” MACHINE LEARNING (ML) Uses ML algorithms (a.k.a. neural networks) to process data like a human brain DEEP LEARNING (DL) 1990 2010 AI that can create new content— text, images, audio, video, code. GENERATIVE AI (GENAI) 2024 2026 CYNTHIA DWORK DIFFERENTIAL PRIVACY; FAIRNESS IN ALGORITHMS KAREN SPÄRCK JONES (1935–2007) INFORMATION RETRIEVAL & NLP Generations of Women in AI FEI-FEI LI COMPUTER VISION & IMAGENET EMILY M. BENDER COMPUTATIONAL LINGUISTICS; LLMS, “STOCHASTIC PARROTS” JOY BUOLAMWINI ALGORITHMIC JUSTICE LEAGUE TIMNIT GEBRU ETHICAL AI, BIAS LLM KATE CRAWFORD SOCIAL, POLITICAL, AND ENVIRONMENTAL IMPACTS OF AI (ATLAS OF AI) MIRA MURATI EXECUTIVE LEADERSHIP OF FRONTIER MODELS LILIAN WENG SAFETY & ALIGNMENT OF LLMS ADA LOVELACE (1840) FIRST PUBLISHED COMPUTER PROGRAM EDITH CLARKE CLARKE CALCULATOR ANGELA JIANG PRODUCT LEADERSHIP FOR LLM-BASED SYSTEMS CYNTHIA BREAZEAL SOCIAL AND AUTONOMOUS ROBOTS ANCA DRAGAN – HUMAN–ROBOT INTERACTION AND REWARD DESIGN JULIE SHAH HUMAN–AI COLLABORATION IN WORKPLACES
Trained Model Data Pre-processed Data Training Data (Bulk of Data) Testing Data Data is Split Learning Algorithm Cross Validation Model “tuning” Predicted Values Evaluate Performance Selecting Features Cleaning Organizing Structured (datasets) Unstructured (everything) Interpret Results Forming Conclusions DATA IS CREATED, CHOSEN , AND COLLECTED BY HUMANS OUTPUTS REFLECT HUMANITY HUMANS DECIDE WHAT TO USE, KEEP AND WHAT MATTERS I’M A HUMAN STILL HUMAN STILL HUMAN STILL HUMAN DEVELOPED BY HUMANS START HERE Gender Bias in Machine Learning
Underrepresentation of Women in Technology 12% AI Researchers 22% 26% AI Professionals Women in STEM
HISTORICAL DATA MISALIGNED MODELS LACK OF AWARENESS UNPRODUCTIVE POLICY SHORT GAME Factors Contributing to Bias in AI
Manifestation of Bias Across Industries AI IN EDUCATION AI AT WORK AI IN FINANCE AI AT HOME AI IN THE MEDICINE
Manifestation of Bias in Medicine BIAS IN CLINICAL AI AGAINST BLACK & HISPANIC PATIENTS (2023–2024) Recent work (e.g., Yale and Rutgers–Newark affiliated teams) shows that AI algorithms used for risk prediction and treatment planning can systematically under- recommend care or mis-estimate risk for Black and Latinx patients, potentially worsening existing health inequities. EDUCATION WORKPLACE FINANCE HOME MEDICINE Haider, S. A., et al. (2024). A systematic review on AI-driven racial disparities in health care.
Manifestation of Bias in The Workplace EDUCATION WORKPLACE FINANCE HOME MEDICINE WORKPLACE – WORKDAY AI HIRING BIAS LAWSUIT (2024–2025) In Mobley v. Workday, a class action alleges that Workday’s AI-driven applicant screening and recommendation tools discriminate on the basis of race, age, and disability, and a court has allowed key claims to move forward. Seyfarth Shaw LLP. (2024, July 19). Mobley v. Workday: Court holds AI service providers could be directly liable for employment discrimination under agent theory.
Manifestation of Bias in Finance EDUCATION WORKPLACE FINANCE HOME MEDICINE FINANCE – APPLE CARD CREDIT LIMIT BIAS (2019) Women reported much lower Apple Card credit limits than male spouses/partners with similar or better credit profiles, triggering an investigation into potential algorithmic gender bias. Hern, A. (2019, November 10). Apple Card issuer investigated after claims of sexist credit checks. The Guardian.
Manifestation of Bias in Home UNESCO 2024 STUDY ON LLM GENDER & STEREOTYPE BIAS A 2024 UNESCO study examined major large language models and found they consistently generated regressive gender stereotypes, homophobia, and racialized content. For example, women four times more likely to be described in domestic roles than men. EDUCATION WORKPLACE FINANCE HOME MEDICINE UNESCO. (2024, July 5). Generative AI: UNESCO study reveals alarming evidence of regressive gender stereotypes.
Manifestation of Bias in Education EDUCATION – UK A-LEVEL GRADING ALGORITHM (2020) The UK exam regulator (Ofqual) replaced in-person exams with an algorithm that standardized teacher-predicted grades. It disproportionately downgraded students from poorer state schools. EDUCATION WORKPLACE FINANCE HOME MEDICINE Electronic Privacy Information Center. (2020, August 19). Algorithm in the UK disadvantaged poorer students in grade calculations.
Include a diverse body of people to review and supervise Conduct Bias auditing and produce assessment plans Establish ethical guidelines and policy Be transparent and disclose usage Plan regular updates and new training data A Way Forward Release datasets for public inspection or for third-party audit Conduct the conversation of bias in AI Balance data sets for current mass audiences
Why it Matters • Fairness and Equity • Accuracy and Reliability • Public Trust and Adoption • Legal and Ethical Compliance • Economic Implications • Innovation and Creativity • Global Perspectives • Timeliness of the Movement Women and Girls Everyone • Reinforcement of Gender Stereotypes • Discrimination in Employment and Career Advancement • Impact on Healthcare • Educational Disparities • Economic Disparities • Social and Psychological Impact • Limitation of Women’s Voices in Technology Development • Global Consequences
F o r Y o u r A t t e n t i o n Thank You
Upcoming Webinars 25 captechu.edu/webinars-and-podcasts/cap-tech-talks-webinars Critical Information Infrastructure Protection and Cybersecurity in New York State January 15, 2026 Dr. Robb Shawe No webinar in December. Enjoy the Holidays!
To Register for Webinars or View On Demand Webinars: www.captechu.edu/webinars-and-podcasts/ cap-tech-talks-webinars 26
Join us for Master’s and Doctoral Virtual Information Sessions. Held monthly. To learn more: Email: gradadmit@captechu.edu • Phone: 1- 800-950-1992 Capitol offers regionally accredited degrees related to this webinar. 27
Recording, Slides & Certificate A copy of the slides and a link to the recording, and presentation handouts will be sent to all registrants. Watch for an email A Certificate of Completion is available upon request to both live session and On Demand viewers Simply reply to the email 28
Thank You! This concludes today’s webinar Watch for a follow up email that contains: 1. How to get a Participation Certificate (Available by request for both Live Session and On Demand viewers) 2. Link to the webinar recording and slides 3. Presentation handouts Thanks for Joining Us! 29