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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?

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.

IBM say that that “Project Debater is the first AI system that can debate humans on complex topics. The goal is to help people build persuasive arguments and make well-informed decisions.” They showcased a fascinating debate on pre-school subsidies with experts and a live audience in San Francisco. Project Debater won the argument!

Project Debater has a very clear synthetic female voice and puts together enormous amounts of data about a subject and presents it in a very succinct way that makes perfect sense.

One wonders how this system could perhaps be used to work with those who need to be encouraged to have conversations and like to use technology rather than face humans in a debate. Also to help with practicing debating skills when social behaviour skills need encouraging and shyness needs to be overcome. The system could possibly help those with cognitive disabilities understand issues and be trained to allow more time between statements, perhaps to slow down a little and to use easy to comprehend language. Lots of ideas to think about and IBM have provided more information on how the project works.

The Department of Health and Social Care has published an updated Code of conduct for data-driven health and care technology with a reorganised set of principles that are easier to read. Austin Clarke says that:

“The code encourages technology companies to meet a gold-standard set of principles to protect patient data to the highest standards. It has been drawn up with the help of industry, academics and patient groups.

The aim is to make it easier for suppliers to develop technologies that tackle some of the biggest issues in healthcare, such as dementia, obesity and cancer. It will also help health and care providers choose safe, effective and secure technology to improve the services they provide.”

The 10 Principles are as follows:

The principles

• Principle 1: Understand users, their needs and the context
• Principle 2: Define the outcome and how the technology will contribute to it
• Principle 3: Use data that is in line with appropriate guidelines for the purpose for which it is being used
• Principle 4: Be fair, transparent and accountable about what data is being used
• Principle 5: Make use of open standards
• Principle 6: Be transparent about the limitations of the data used
• Principle 7: Show what type of algorithm is being developed or deployed, the ethical examination of how the data is used, how its performance will be validated and how it will be integrated into health and care provision
• Principle 8: Generate evidence of effectiveness for the intended use and value for money
• Principle 9: Make security integral to the design
• Principle 10: Define the commercial strategy