CAN-ASC-6.5 – Accessible and Equitable Generative and Physical Artificial Intelligence Systems
Information
Table of contents
Technical committee members
Lisa Snider, Senior Digital Accessibility Consultant and Trainer, Access Changes Everything Inc.
Nancy McLaughlin, Senior Policy Advisor on Accessibility, Canadian Radio-television and Telecommunications Commission
John Willis, Senior Program Advisor, OPS Accessibility Office, Centre of Excellence for Human Rights.
Jutta Treviranus (Chairperson), Director of the Inclusive Design Research Centre and Professor, OCAD University
Gary Birch, Executive Director, Neil Squire Society
Lisa Liskovoi, Senior Inclusive Designer and Digital Accessibility Specialist, Inclusive Design Research Centre, OCAD University
Clayton Lewis, Professor, University of Colorado
Julia Stoyanovich, Associate Professor and Director, Tandon School of Engineering, New York University
Anne Jackson, Professor, Seneca College
Kave Noori, Artificial Intelligence Policy Officer, European Disability Forum
Mia Ahlgren, Human Rights and Disability Policy Officer, Swedish Disability Rights Federation
Sambhavi Chandrashekar (Vice-Chairperson), Global Accessibility Lead, D2L Corporation
Julianna Rowsell, Senior Product Manager, Product Equity, Adobe
Kate Kalcevich, Head of Accessibility Innovation, Fable
Saeid Molladavoudi, Senior Data Science Advisor, Statistics Canada
Merve Hickok, Founder, President and Research Director, Alethicist.org, Center for AI and Digital Policy, University of Michigan
Tara Connelly MA RP – Assistant Director Research and Development Accessibility Institute Carleton University
Areas of Focus
The purpose of CAN-ASC-6.5 is to develop a standard that goes above mandatory minimum technical specifications and produces equity- based technical requirements.
There are common areas where people with disabilities may face barriers related to artificial intelligence technologies, including generative and physical artificial intelligence systems. These include, but are not limited to:
- User interfaces
- Training data and model assumptions
- Language, communication, and comprehension
- Visual content generation and interpretation
- Audio and speech generation and recognition
- Cognitive accessibility and usability
- Reliability, accuracy, and trustworthiness
- Privacy, consent, and data governance
- Human oversight, escalation, and redress