Innovation is often scored on what a system can do—not on who can use it safely, effectively, and on equal terms. On May 17, 2027, we ask what technical progress should mean when Deaf people help define the problem, the evidence, and the evaluation.
Beating a narrow benchmark doesn’t automatically help people in the real world. Inclusive innovation pays attention to language, culture, context, privacy, accessibility, and power—and checks whether new tools expand agency or just move the old barriers around.
For AI researchers and engineers, founders, designers, Deaf technologists, accessibility specialists, funders, educators, and innovation leaders.
- What should count as progress in AI for Deaf people
- Research priorities: data, language, interfaces, privacy, evaluation
- Deaf expertise across the full innovation lifecycle
- From prototype to deployment without losing accountability
Speakers*
- Sam Sepah — Lead AI Accessibility Research Program Manager, Google. Runs AI-accessibility R&D and collaborated on RIT/NTID’s Grammar Laboratory, a bilingual AI tool for Deaf students.
- Nandita Gupta — Senior AI Product Manager, Cisco Webex. Shapes Webex accessibility and language-AI roadmap, including ASR, AI governance, and inclusive collaboration products.
- Bonnie Parisi — Vice President, Product Accessibility, Salesforce. Leads AI-powered accessibility skills and the Accessibility Agent, bringing product engineering and disability inclusion together.
- Marc Laval — Senior Front-End Engineer and Web Accessibility Expert, Meta. Builds accessibility testing and AI agents intended to detect, fix, and verify accessibility issues across Meta products.
- Suresh Krishnan — Senior Manager, Product Management Technical, PXT Accessibility, Amazon. Leads global AI-driven accessibility initiatives and born-accessible design for products and operations.
- James Green — Global Head of Accessibility, Consumer and Community Banking, JPMorgan Chase. Named accessibility leader for accessible web/app design at a major financial institution, with direct product and customer relevance.
- Erin Finton — Lecturer, Liberal Studies, RIT/NTID. Developed Grammar Laboratory, an agentic AI tool using ASL/English bilingual personalization for Deaf student language learning.
- Tae Oh — Professor, School of Information; Strategic Initiatives and Innovation Director, NTID, RIT. Works at the intersection of IoT, AI, HCI, cybersecurity, and intelligent assistive tools for accessible environments.
- Rosiana Natalie — MIDAS Postdoctoral Data Science Fellow, University of Michigan. Studies accountable human-AI collaboration and personalization for accessibility, offering a research-lab perspective on inclusive AI systems.
- Richard E. Ladner — Professor Emeritus, Paul G. Allen School, University of Washington; founding member, CREATE. Longtime accessible-computing researcher focused on Deaf/HH technology, ASL datasets, and broadening disability participation in computing.
TBC
Register below — seats are limited.
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