Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Artificial intelligence fairness in the context of accessibility research on intelligent systems for people who are deaf or hard of hearing

Artificial intelligence fairness in the context of accessibility research on intelligent systems... We discuss issues of Artificial Intelligence (AI) fairness for people with disabilities, with examples drawn from our research on HCI for AI-based systems for people who are Deaf or Hard of Hearing (DHH). In particular, we discuss the need for inclusion of data from people with disabilities in training sets, the lack of interpretability of AI systems, ethical responsibilities of access technology researchers and companies, the need for appropriate evaluation metrics for AI-based access technologies (to determine if they are ready to be deployed and if they can be trusted by users), and the ways in which AI systems influence human behavior and influence the set of abilities needed by users to successfully interact with computing systems. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM SIGACCESS Accessibility and Computing Association for Computing Machinery

Artificial intelligence fairness in the context of accessibility research on intelligent systems for people who are deaf or hard of hearing

Download PDF
5 pages

Loading next page...
 
/lp/association-for-computing-machinery/artificial-intelligence-fairness-in-the-context-of-accessibility-AgB49PDuql
Publisher
Association for Computing Machinery
Copyright
Copyright © 2020 Copyright is held by the owner/author(s)
ISSN
1558-2337
eISSN
1558-1187
DOI
10.1145/3386296.3386300
Publisher site
See Article on Publisher Site

Abstract

We discuss issues of Artificial Intelligence (AI) fairness for people with disabilities, with examples drawn from our research on HCI for AI-based systems for people who are Deaf or Hard of Hearing (DHH). In particular, we discuss the need for inclusion of data from people with disabilities in training sets, the lack of interpretability of AI systems, ethical responsibilities of access technology researchers and companies, the need for appropriate evaluation metrics for AI-based access technologies (to determine if they are ready to be deployed and if they can be trusted by users), and the ways in which AI systems influence human behavior and influence the set of abilities needed by users to successfully interact with computing systems.

Journal

ACM SIGACCESS Accessibility and Computing – Association for Computing Machinery

Published: Mar 2, 2020

References

  • Guidelines for Human-AI Interaction
    Amershi, Saleema; Weld, Dan; Vorvoreanu, Mihaela; Fourney, Adam; Nushi, Besmira; Collisson, Penny; Suh, Jina; Iqbal, Shamsi; Bennett, Paul N.; Inkpen, Kori; Teevan, Jaime; Kikin-Gil, Ruth; Horvitz, Eric
  • ACM Code of Ethics and Professional Conduct
    Machinery, Association for Computing
  • Modeling the Speed and Timing of American Sign Language to Generate Realistic Animations
    Al-khazraji, Sedeeq; Berke, Larwan; Kafle, Sushant; Yeung, Peter; Huenerfauth, Matt
  • Automatic speech recognition and speech variability: A review
    BenZeghiba, Mohamed Faouzi; De Mori, Renato; Deroo, Olivier; Dupont, Stephane; Erbes, T.; Jouvet, D.; Fissore, Luciano; Laface, Pietro; Mertins, Alfred; Ris, Christophe; Rose, Richard
  • Deaf and Hard-of-Hearing Perspectives on Imperfect Automatic Speech Recognition for Captioning One-on-One Meetings
    Berke, Larwan; Caulfield, Christopher; Huenerfauth, Matt
  • Methods for Evaluation of Imperfect Captioning Tools by Deaf or Hard-of-Hearing Users at Different Reading Literacy Levels
    Berke, Larwan; Kafle, Sushant; Huenerfauth, Matt
  • Gender shades: Intersectional accuracy disparities in commercial gender classification
    Buolamwini, Joy; Gebru, Timnit
  • CCD Transportation Task Force Autonomous Vehicle Principles
    Disabilities, Consortium for Citizens with
  • Automatic Speech Recognition Services: Deaf and Hard-of-Hearing Usability
    Glasser, Abraham
  • HireVue Official Site
    HireVue,
  • Sign Language in the Interface: Access for Deaf Signers
    Huenerfauth, Matt; Hanson, Vicki L.
  • Evaluating the Usability of Automatically Generated Captions for People who are Deaf or Hard of Hearing
    Kafle, Sushant; Huenerfauth, Matt
  • Will You Accept an Imperfect AI?: Exploring Designs for Adjusting End-user Expectations of AI Systems
    Kocielnik, Rafal; Amershi, Saleema; Bennett, Paul N.
  • Women, Minorities, and Persons with Disabilities in Science and Engineering, Report No
    Foundation, National Science
  • Preliminary Report Highway HWY18MH010
    Board, National Transportation Safety
  • Behavioral Changes in Speakers who are Automatically Captioned in Meetings with Deaf or Hard-of-Hearing Peers
    Seita, Matthew; Albusays, Khaled; Kafle, Sushant; Stinson, Michael; Huenerfauth, Matt
  • Exploring the Performance of Facial Expression Recognition Technologies on Deaf Adults and Their Children
    Shaffer, Irene Rogan
  • Gender and dialect bias in YouTube's automatic captions
    Tatman, Rachel
  • WFD and WASLI Issue Statement on Signing Avatars
    Deaf, World Federation of the