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Cited by 189Ethics and Social Impacts of AIDigital Accessibility for DisabilitiesAssistive Technology in Communication and Mobility

The DOI matches its Crossref record; 37 of the 37 references it cites were found in a registry or an index.

Toward fairness in AI for people with disabilities SBG@a research roadmap

Guo, Anhong, Kamar, Ece, Vaughan, Jennifer Wortman, Wallach, Hanna, Morris, Meredith Ringel·2020·ACM SIGACCESS Accessibility and Computing
DOI 10.1145/3386296.3386298
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Abstract

AI technologies have the potential to dramatically impact the lives of people with disabilities (PWD). Indeed, improving the lives of PWD is a motivator for many state-of-the-art AI systems, such as automated speech recognition tools that can caption videos for people who are deaf and hard of hearing, or language prediction algorithms that can augment communication for people with speech or cognitive disabilities. However, widely deployed AI systems may not work properly for PWD, or worse, may actively discriminate against them. These considerations regarding fairness in AI for PWD have thus far received little attention. In this position paper, we identify potential areas of concern regarding how several AI technology categories may impact particular disability constituencies if care is not taken in their design, development, and testing. We intend for this risk assessment of how various classes of AI might interact with various classes of disability to provide a roadmap for future research that is needed to gather data, test these hypotheses, and build more inclusive algorithms.

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Research works printed but not linked: 22

  1. Julia Angwin, Jeff Larson, Surya Mattu, Lauren Kirchner, and ProPublica. 2016. Machine Bias: There's software used across the country to predict future criminals. And it's biased against blacks. (2016). Retrieved July 3, 2019 from https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing
  2. Apple Inc. 2019c. macOS Catalina - Introducing Voice Control. Your all-access to all devices. (2019). Retrieved July 3, 2019 from https://www.apple.com/macos/catalina-preview/#accessibility
  3. Jeffrey P. Bigham and Patrick Carrington. 2018. Learning from the Front: People with Disabilities as Early Adopters of AI. In HCIC 2018.
  4. Joy Buolamwini and Timnit Gebru. 2018. Gender shades: Intersectional accuracy disparities in commercial gender classification. In Conference on Fairness, Accountability and Transparency. 77--91.
  5. Min Chu, Yong Zhao, and Sheng Zhao. 2010. Providing personalized voice font for text-to-speech applications. (April 6 2010). US Patent 7,693,719.
  6. Duolingo. 2019. Say hello to the Bots. The most advanced way to learn a language. (2019). Retrieved July 3, 2019 from http://bots.duolingo.com
  7. John Greer Elias, Wayne Carl Westerman, and Myra Mary Haggerty. 2010. Multi-touch gesture dictionary. (Nov. 23 2010). US Patent 7,840,912.
  8. Linda G Gottermeier and S Kushalnagar Raja. 2016. User Evaluation of Automatic Speech Recognition Systems for Deaf-Hearing Interactions at School and Work. Audiology Today 28, 2 (2016), 20--34.
  9. Grammarly Inc. 2019. Great Writing, Simplified. (2019). Retrieved July 3, 2019 from https://www.grammarly.com
  10. HireVue. 2019. HireVue - Hiring Intelligence | Assessment & Video Interview Software. (2019). Retrieved July 3, 2019 from https://www.hirevue.com
  11. LookTel. 2019. LookTel Money Reader. (2019). Retrieved July 3, 2019 from http://www.looktel.com/moneyreader
  12. Microsoft. 2019a. Azure Kinect DK - BODY TRACKING SDK. (2019). Retrieved July 3, 2019 from https://azure.microsoft.com/en-us/services/kinect-dk/
  13. Microsoft. 2019b. Custom Voice. (2019). Retrieved July 3, 2019 from https://speech.microsoft.com/customvoice
  14. Microsoft. 2019d. Immersive Reader - An AI Service that helps users read and comprehend text. (2019). Retrieved July 3, 2019 from https://azure.microsoft.com/en-us/services/cognitive-services/immersive-reader/
  15. Microsoft. 2019e. Seeing AI. (2019). Retrieved July 3, 2019 from https://www.microsoft.com/en-us/ai/seeing-ai
  16. Microsoft. 2019f. Windows Hello: Discover facial recognition on Windows 10. (2019). Retrieved July 3, 2019 from https://www.microsoft.com/en-us/windows/windows-hello
  17. Motion Analysis. 2019. Sports Biomechanics. (2019). Retrieved July 3, 2019 from https://motionanalysis.com/industry/sports-biomechanics/
  18. Antony Nicol, Chris Casey, and Stuart MacFarlane. 2002. Children are ready for speech technology-but is the technology ready for them. Interaction Design and Children, Eindhoven, The Netherlands (2002).
  19. OrCam. 2019. OrCam MyEye 2 - For the Blind and Visually Impaired. (2019). Retrieved July 3, 2019 from https://www.orcam.com/en/myeye2/
  20. KNFB Reader. 2018. KNFB Reader gives you easy access to print and files, anytime, anywhere. (2018). Retrieved July 3, 2019 from https://knfbreader.com
  21. TopResume. 2019. Get to Know the 5 Most Popular Pre-Employment Personality Tests. (2019). Retrieved July 3, 2019 from https://www.topresume.com/career-advice/how-to-pass-the-pre-employment-personality-test
  22. Shari Trewin. 2018. AI Fairness for People with Disabilities: Point of View. arXiv preprint arXiv:1811.10670 (2018). · arXiv

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Metadata

Authors
Guo, A., Kamar, E., Vaughan, J. W., Wallach, H., & Morris, M. R.
Affiliations
Microsoft (United States) (US)
ORCID
Venue
ACM SIGACCESS Accessibility and Computing
Publisher
Association for Computing Machinery (ACM)
Published
2020-03-02
Issue
125
Pages
1-1
ISSN
  • 1558-1187 (ISSN-L)
  • 1558-2337
DOI
10.1145/3386296.3386298
OpenAlex
W3009306974
Cited by
128
References
36
Citations by year
2026: 7 · 2025: 23 · 2024: 26 · 2023: 34 · 2022: 19 · 2021: 15 · 2020: 3
FWCI
24.19
Type
journal article
Language
en
Open access
closed
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)
Locations
ACM SIGACCESS Accessibility and Computing (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
  • Social Sciences › Human Factors and Ergonomics › Digital Accessibility for Disabilities
  • Health Sciences › Health Professions › Occupational Therapy › Assistive Technology in Communication and Mobility
Keywords
algorithmic fairness, people with disabilities, algorithmic discrimination, accessibility, speech recognition, deaf and hard of hearing, assistive technology, research roadmap

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