Automating Media Accessibility: An Approach for Analyzing Audio Description Across Generative Artificial Intelligence Algorithms

ABSTRACT

A surge in public availability of emerging GenAI-AD has brought back the promises of automated accessibility for people who cannot see or see well. This article tests those promises through a double-rendering method that asks GenAI-AD engines to describe a simple portrait of a person and then returns these generated texts into GenAI-AD engines for visualizations of what they earlier had described, revealing insights about GenAI efficacies, ethics, and biases.

KEYWORDS:

• Audio description
• alt-text
• blind
• bias
• disability studies
• ethics
• generative artificial intelligence
• low vision
• media accessibility
• Kamala Harris

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the U.S. National Endowment for the Humanities under Grant 276851-21; the U.S. National Park Service under Grant P20AC01084-01; and Google, under a grant administered via the UH Foundation, 127-7390-4.

Notes on contributors

Daniel Bergin

Daniel Bergin is a PhD student in Communication & Information Sciences at the University of Hawaiʻi at Mānoa. He researches media futures, artificial intelligence, digital transformation, change management, and cybersecurity in the School of Communication and Information. He can be reached via: [email protected].

Brett Oppegaard

Brett Oppegaard is an associate professor in the School of Communication and Information at the University of Hawaiʻi at Mānoa in Honolulu, HI. He researches media-production processes and products at intersections of Technical Communication, Rhetoric, Human-Computer Interaction, Disability Studies, Digital Inequalities, and Journalism. For his recent research involving Audio Description, he has been honored by Helen Keller Services with the AccessAbility Award (2022), The Best of Heritage, ICOM, and Europa Nostra with the Projects of Influence Award (2021), The American Alliance of Museums’ The MUSE Award (2020), and the American Council of the Blind’s Margaret Pfanstiehl Audio Description Achievement Award in R&D (2019). His Audio Description research during that time also has been supported by grants from the U.S. National Park Service, the U.S. National Endowment for the Humanities, the U.S. National Endowment for the Arts, and Google. He can be reached via: [email protected].