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 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.
['Wald|Mike|M|']
personalisation disability localisation artificial intelligence classification‐