Youth empowerment is a key theme across all of my sessions, which will challenge learners to develop critical data literacy and multidisciplinary research skills to critique, improve, and reimagine AI ethics. Topics include algorithmic bias, labor rights, consent & privacy, surveillance, environmental justice, and critical history. These sessions are guided by culturally relevant education, problem-based learning, and UDL pedagogies.
Critical AI will include the following sessions:
Intro to AI, Storytelling, and Creative Writing
AI & Consent: Ethics of AI Art
Can AI Protect Your Art?
From Educator to Critical AI Researcher
Gender Shades: Auditing AI
YRMedia: Erase Your Face
From Broken Glass to Data Centers
How “Intelligent” is AI?
Evan (he/him) is an AI educator and researcher with experience building industry-grade AI systems at companies such as Microsoft and Amazon. He holds a BS + MS in Computer Science from Stanford, where he co-taught the largest introductory course in Machine Learning.
As a K-12 educator, Evan has also taught at GEAR UP Washington and San Diego Unified School District's data science programs. As YDSL’s Executive Director, Evan’s role empowers YDSL team members to bring our unique skillsets (as a diverse team of leaders in education and industry) to support our shared mission to bring data science education to K-12.
Thema (Tay-mah) Monroe-White is an Associate Professor of Artificial Intelligence and Policy in the Schar School of Policy and Government and the Department of Computer Science at George Mason University. She is particularly concerned with understanding the pathways to achieving social and economic empowerment for minoritized groups via AI education, and emancipatory data science, a justice-centered approach to computational and quantitative inquiry that challenges algorithmic biases, advances racial equity, and reimagines how data and AI can serve marginalized communities. She investigates the intersections of bias mitigation, critical computational methods, and racial equity across science and technology education. Dr. Monroe-White has received multiple grants to study equity in K-20 learning ecosystems for the purpose of designing inclusive, data-driven pedagogies that broaden participation in AI and data science. She is an advisory board member and fellow of the Institute in Critical Quantitative and Mixed Methodologies (ICQCM), has served on the Bureau of Labor Statistics (BLS) Technical Advisory Committee, and contributes regularly to national dialogues on equitable and emancipatory AI education through forums at the White House, the National Academies, and other convenings. Thema holds a PhD in Science, Technology, and Innovation Policy from the Georgia Institute of Technology, and Master’s and Bachelor’s degrees from Howard University.
As equity-minded classroom educators, we believe that EEAI fellows are well-positioned to be leaders in AI ethics and already possess all the tools to do so. As someone with a dual background in the AI industry and K-12 education, I'm excited to demystify AI ethics by building bridges between industry practices and the multidisciplinary skills that are already taught in K-12 classrooms. We see AI literacy as more than just "using AI", but rather critiquing, researching, evaluating, (re)designing, and reimagining AI.
The hope is that EEAI fellows will leave our program with three things: (1) a deeper knowledge of how AI works and how it is situated as a technology in broader societal contexts, (2) AI research skills that are actionable in allowing educators to develop their own AI ethics frameworks informed by data, and (3) learning resources and lesson templates that will allows EEAI fellows to integrate AI ethics learning experiences into their classrooms, across grade bands and subjects.
Machine Learning
The subfield of AI that leverages training data and statistical pattern matching algorithms to build models (nearly all modern systems, such as ChatGPT, use machine learning)
Algorithmic Bias
Algorithmic bias: statistical disparities in the outputs of algorithmic systems that lead to unjust or discriminatory outcomes.
Data Sovereignty
Data sovereignty: a principle that states that data ought to be governed by the entities who created it.
Auditing
Auditing: any independent assessment of an identified audit target via an evaluation of articulated expectations with the implicit or explicit objective of accountability.