AI and Inclusion versus AI and Assistance.

Over the last few months we have been debating what inclusion means to us all and how AI and machine learning can help.

There have been many ways in which we have seen the assistive side of the technology and how it can be used with apps that support speech technologies, image recognition and automatic captions.  These assistive technologies have benefitted us all but, what about the barriers that could be removed to make it even easier for people to feel included?

equity rather than equality
A picture illustrating the concepts of equality, equity and justice.

Courtesy Advancing Equity and Inclusion: A guide for municipalities, City for All Women Initiative (CAWI), Ottawa

Areas such as digital accessibility, augmentative and alternative forms of communication (AAC)  and AI in education, are just a few of the subjects we have been exploring.  

If you are surfing the web using a screen reader, and it fails to work because text descriptions for images or labels on forms have been omitted you may well be unable to complete the task in hand.  However, alternative text can be generated automatically using image recognition and if the text around that image is explored in more detail, there is a chance that the accuracy of the alternative text can be improved with more contextual information.  If people don’t consider accessibility when developing web sites and their content we need many more machine enabled accessibility checks that actually work effectively without too many false positives or negatives.  Then we need the automatic fixes to make sense of these barriers!

If someone with a communication difficulty who uses symbols wants to join a conversation where everyone is talking at a rate of 150 plus words per minute it is hard to compete only managing around 10 – 12 words per minute.  It should be possible to speed input with better forms of prediction and language correction when users need to choose symbols. Once again context sensitivity could help.

Why can’t we also make symbol sets interchangeable so that users who work with one set of symbols are not dependent on the text translation to work with other AAC users – the ability to harmonise symbol sets with some standardisation should be possible – maybe image recognition and better use of natural language processing could help.

A conference with the ED-ICT network will hopefully result in discussions around the support AI could provide in education. Several AI technologies have been mentioned in international reports that could have an impact on some of our students when coping with the barriers of day to day life in universities. Could the better use of natural language processing further improve automatic captioning for lecture capture and provide more accurate search results when looking for academic papers, Biometrics and Blockchain in Education could offer enhanced security for aspects of our management systems and assessments perhaps allowing better supporting strategies for those who benefit from remote access. i feel these aspects of inclusive education need more research to support disabled students.