UNITAR Online Catalogue
CIFAL York - AI and Feminism: Foundations of a Critical Dialogue on Gender, Power, and Technology
Personas
CIFAL York, York University, Centre for Feminist Research
Antecedentes
This interdisciplinary course bridges foundational concepts in artificial intelligence (AI) with feminist theories, offering a critical framework to examine how AI technologies reinforce or challenge societal power dynamics. Participants will explore key feminist principles, including intersectionality, alongside AI fundamentals such as machine learning and algorithmic design. Through lectures, case studies, and reflective activities, the course emphasizes critical theory and ethical considerations, equipping attendees with tools to interrogate and reimagine AI systems in ways that promote equity and inclusion.
Objetivos del evento
- Define key terms and historical developments in both feminist theory (e.g., waves of feminism, intersectionality) and AI (e.g., machine learning, deep learning).
- Explore how feminist critiques and critical theories can be applied to ethical challenges in AI.
- Investigate how biases related to race, gender, and other identities are embedded in data and algorithms.
- Engage with case studies to identify how algorithms, such as facial recognition systems, perpetuate or challenge social hierarchies.
- Reflect on the ethical development of AI through a feminist lens.
- Identify actionable steps for applying feminist principles to AI research and development.
Objetivos del aprendizaje
- Define key terms and historical developments in both feminist theory (e.g., waves of feminism, intersectionality) and AI (e.g., machine learning, deep learning).
- Explore how feminist critiques and critical theories can be applied to ethical challenges in AI.
- Investigate how biases related to race, gender, and other identities are embedded in data and algorithms.
- Engage with case studies to identify how algorithms, such as facial recognition systems, perpetuate or challenge social hierarchies.
- Reflect on the ethical development of AI through a feminist lens.
- Identify actionable steps for applying feminist principles to AI research and development.
Contenido y estructura
10:00AM - 12:00PM Introduction to Feminism and AI
- Lecture: Overview of Feminism (60 min)
Topics: Waves of feminism, key theorists, and intersectionality. - Lecture: AI Basics (60 min)
Topics: Definition, history, and types of AI, including machine learning and deep learning.
12:00PM - 1:30PM Structural Inequalities and AI
- Lecture and Discussion: Oppression is not only derogatory remarks, discriminatory behaviour, or overt violence. In this session, we will discuss concepts like social construction and value-laden knowledge to understand the structural basis of social inequalities. We will consider the example of search engines to illustrate how discrimination gets “baked into” our technologies. (30 min)
Topics: Social construction, value-laden knowledge, structural inequality, and algorithms. - Activity: Group Discussion (30 min)
Topic: How does AI replicate or challenge gender and racial hierarchies? - Activity: Group Presentations (30 min)
1:30PM - 2:30PM LUNCH BREAK
2:30PM - 4:00PM Intersectionality in AI Systems
- Lecture: Intersectionality (45 min)
Topics: Kimberlé Crenshaw’s theory, how intersectionality influences data and algorithms in AI. - Case Study Discussion (45 min)
Activity: Analyzing case studies of biased algorithms (e.g., facial recognition failures based on race and gender).
4:00PM - 4:30PM Reflective Session: Feminist AI Ethics
- Activity: Individual Reflection (15 min)
Write a reflection on AI development through a feminist lens. - Discussion: Share reflections with peers (15 min).
4:30PM - 5:00PM Wrap-Up and Takeaways
- Summary of key concepts and next steps for deeper exploration.
Metodología
- Comprehensive Curriculum: Covering everything from the fundamentals of feminism and AI to intersectionality, ethics, and bias.
- Expert Instruction: Led by experts in the fields of feminism and AI, this course will deliver lectures on feminism, AI, and intersectionality.
- Collaborative Environment: Discuss lecture topics and case studies alongside like-minded peers in a supportive and dynamic learning environment, fostering creativity and innovation.
- Reflective Session: Create a personal reflection based on course content and share ideas with your peers.
Público objetivo
This course is designed for Feminist students, Eng. And Comp. Sciences student and researchers, Students interested in AI and Feminism, data scientists, enterprise and NGO employees interested in the topic.