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AI Data-Driven Personalisation and Disability Inclusion

This study aims to help people working in the field of AI understand some of the unique issues regarding disabled people and examines the relationship between the terms “Personalisation” and “Classification” with regard to disability inclusion. Classification using big data struggles to cope with th...

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Detalles Bibliográficos
Autor principal: Wald, Mike
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7861332/
https://www.ncbi.nlm.nih.gov/pubmed/33733215
http://dx.doi.org/10.3389/frai.2020.571955
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author Wald, Mike
author_facet Wald, Mike
author_sort Wald, Mike
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description This study aims to help people working in the field of AI understand some of the unique issues regarding disabled people and examines the relationship between the terms “Personalisation” and “Classification” with regard to disability inclusion. Classification using big data struggles to cope with the individual uniqueness of disabled people, and whereas developers tend to design for the majority so ignoring outliers, designing for edge cases would be a more inclusive approach. Other issues that are discussed in the study include personalising mobile technology accessibility settings with interoperable profiles to allow ubiquitous accessibility; the ethics of using genetic data-driven personalisation to ensure babies are not born with disabilities; the importance of including disabled people in decisions to help understand AI implications; the relationship between localisation and personalisation as assistive technologies need localising in terms of language as well as culture; the ways in which AI could be used to create personalised symbols for people who find it difficult to communicate in speech or writing; and whether blind or visually impaired person will be permitted to “drive” an autonomous car. This study concludes by suggesting that the relationship between the terms “Personalisation” and “Classification” with regards to AI and disability inclusion is a very unique one because of the heterogeneity in contrast to the other protected characteristics and so needs unique solutions.
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spelling pubmed-78613322021-03-16 AI Data-Driven Personalisation and Disability Inclusion Wald, Mike Front Artif Intell Artificial Intelligence This study aims to help people working in the field of AI understand some of the unique issues regarding disabled people and examines the relationship between the terms “Personalisation” and “Classification” with regard to disability inclusion. Classification using big data struggles to cope with the individual uniqueness of disabled people, and whereas developers tend to design for the majority so ignoring outliers, designing for edge cases would be a more inclusive approach. Other issues that are discussed in the study include personalising mobile technology accessibility settings with interoperable profiles to allow ubiquitous accessibility; the ethics of using genetic data-driven personalisation to ensure babies are not born with disabilities; the importance of including disabled people in decisions to help understand AI implications; the relationship between localisation and personalisation as assistive technologies need localising in terms of language as well as culture; the ways in which AI could be used to create personalised symbols for people who find it difficult to communicate in speech or writing; and whether blind or visually impaired person will be permitted to “drive” an autonomous car. This study concludes by suggesting that the relationship between the terms “Personalisation” and “Classification” with regards to AI and disability inclusion is a very unique one because of the heterogeneity in contrast to the other protected characteristics and so needs unique solutions. Frontiers Media S.A. 2021-01-18 /pmc/articles/PMC7861332/ /pubmed/33733215 http://dx.doi.org/10.3389/frai.2020.571955 Text en Copyright © 2021 Wald. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Artificial Intelligence
Wald, Mike
AI Data-Driven Personalisation and Disability Inclusion
title AI Data-Driven Personalisation and Disability Inclusion
title_full AI Data-Driven Personalisation and Disability Inclusion
title_fullStr AI Data-Driven Personalisation and Disability Inclusion
title_full_unstemmed AI Data-Driven Personalisation and Disability Inclusion
title_short AI Data-Driven Personalisation and Disability Inclusion
title_sort ai data-driven personalisation and disability inclusion
topic Artificial Intelligence
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7861332/
https://www.ncbi.nlm.nih.gov/pubmed/33733215
http://dx.doi.org/10.3389/frai.2020.571955
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