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Perspectives on Promoting Algorithmic Justice in Artificial Intelligence and Machine Learning Education in K–12
Fri, April 12, 9:35 to 11:05am, Pennsylvania Convention Center, Floor: Level 100, Room 115BSession Type: Structured Poster Session
Abstract
Despite the increasing interest in justice and fairness in computing, in education little attention has been paid to learning algorithmic justice. Today we recognize that algorithmic systems are not neutral, they reflect the values of their creators, and are not the solution to all problems—often causing or perpetuating existing problems. In this session, we offer diverse perspectives on the state of algorithmic justice in learning research, from how youth understand issues of justice in artificial intelligence/machine learning (AIML) systems they use in their everyday lives, to how they consider algorithmic justice when making applications. Researching and fostering understanding of issues of algorithmic justice is increasingly important to prepare youth to participate in a world where AIML is ubiquitous.
Sub Unit
Chairs
Papers
Middle School Students’ Perceptions of Ethical Implications of Artificial Intelligence/Machine Learning (Poster 1) - Helen Zhang, Boston College; Irene A. Lee, Massachusetts Institute of Technology; Katherine Strong Moore, Massachusetts Institute of Technology
Bodies in AIML, Embodied Justice: Discussing Algorithmic Harm and Justice Through Sports-Based Culturally Relevant Computing Education (Poster 2) - Vishesh Kumar, Northwestern University; Marcelo A.B. Worsley, Northwestern University
Integrating Artificial Intelligence/Machine Learning Into History Classrooms: Reasoning About Data Bias Through Modeling With Primary Sources (Poster 3) - Jeanne M. McClure, North Carolina State University; Franziska Bickel, North Carolina State University; Cansu Tatar, Northern Illinois University; Victoria Newton, North Carolina State University; Cassandra Rubinstein, North Carolina State University; Shiyan Jiang, North Carolina State University; Jie Chao, The Concord Consortium; Carolyn Penstein Rose, Carnegie Mellon University; Christy M. Byrd, North Carolina State University; Amato Nocera, North Carolina State University
Collaborative Auditing of Machine Learning Applications: High School Youth Identifying Issues of Algorithmic Justice (Poster 4) - Luis Morales-Navarro, University of Pennsylvania; Yasmin B. Kafai, University of Pennsylvania
Scaffolding Youth Sensemaking Around Algorithmic Fairness (Poster 5) - Jean Salac, University of Washington; Amy J. Ko, University of Washington
Liberatory Computing Framework: Empowering High School Students to Mitigate Systemic Oppression Through Data Activism (Poster 6) - Raechel Walker, Massachusetts Institute of Technology; Cynthia L. Breazeal, Massachusetts Institute of Technology
Black Life Within K–12 Artificial Intelligence and Machine Learning Education (Poster 7) - Stephanie T. Jones, Northwestern University
Utilizing Co-Design to Introduce Technological and Algorithmic Bias in Middle School Computer Science Lessons (Poster 8) - Merijke Coenraad, Digital Promise Global; David Weintrop, University of Maryland
Youth Reasoning About Generative AIML (Artificial Intelligence and Machine Learning) and Its Possible Future Roles (Poster 9) - Jaemarie Solyst, Carnegie Mellon University; Ellia Yang, Carnegie Mellon University; Shixian Xie, Carnegie Mellon University; Amy E. Ogan, Carnegie Mellon University; Jessica Hammer, Carnegie Mellon University; Motahhare Eslami, Carnegie Mellon University
Dance and Creative Computing as Spaces for Critically Engaging and Learning About Artificial Intelligence/Machine Learning (Poster 10) - Francisco Castro, New York University; Kayla DesPortes, New York University
A Socially Relevant AI/ML (Artificial Intelligence/Machine Learning) Curricular Module for High School Students (Poster 11) - Shuchi Grover, Looking Glass Ventures; Isabella Gransbury, North Carolina State University; Veronica Catete, North Carolina State University; Tiffany Barnes, North Carolina State University