Accessibility of machine learning and generative AI draft - Comments from Josh

Hi all,

I hope you are all well. Good to see this document progressing.

Some comments from me on the AI and Machine learning document (I can log 
these on GH also if helpful):

Over all some of the use cases are really about repair. What does the 
innovation roadmap look like? Where do we need it to be? It is better 
here to identify what AI does poorly, and what it does well? I think 
this document needs to be clearer about that and with an eye on a what 
the goal is. I’m not sure this doc outlines what AI/ML does poorly and 
therefore how we can make what it does better, rather than seeing 
AI/Machine learning as a panacea.

We are better off getting a clear view and presenting that - as well as 
by assessing the pros/cons maybe suggesting areas for further 
exploration, rather than a shopping list for current automated 
accessibility tools (which is what it currently looks like to me).

Some editorial comments below:

#Comment 1 on Scope:

I dont think the word ‘clearly' should be used here. This document could 
be better as a question, 'What is the current bearing of AI on 
accessibility?' or a gap analysis.

Remove ‘clearly'

"This is a draft collection of relevant information related to 
cross-disability accessibility guidance of how developments in machine 
learning and generative Artificial Intelligence (AI) clearly bears an 
impact on web accessibility standards and processes. Given the rapid 
changes in the consumption and development of AI design, this is 
intended to be a starting point to group the accessibility implications 
of machine learning and generative AI technologies.”

# Editorial:

"1.1.2 Machine learning
Machine learning represents a field of study within the AI domain that 
has a focus towards [on] statistical algorithm[s]- capable of learning 
from data and performing tasks without specific instructions. This form 
of AI tends to focus on determinations and predictions.”

# Suggestion about focus of the document:

"As online generative AI platforms such as ‘ChatGPT’ continue to offer 
consumers the unrestricted ability to create text and images, including 
video and audio from a variety of inputs, [it is important to as the 
question 'what is the benefit of these advances from an accessibility 
perspective. This leads to a follow on question 'Are their potential 
harms or other challenges?. This is because accessibility as a disciple 
in fundamtentially a quality issue and the principle of GIBO (Garbage in 
Garbage out) applies very much in the context of generative AI.’]”

#Suggestion ' it also is important to consider how accessible these 
outputs will be presented [and how this will effect the user experience 
of someone with a disability or who is a user of Assitive Technology], 
and if machine learning algorithm may address broader accessibility 
issues in everyday tasks. ‘

#Suggestion:
"This could be done through traditional means such as the addition of 
alternative text to images [which brings up the fundamental question of 
'what is the quality of these alternate text desciptions' for images 
which do not have one. Or for recognising text that should [should have 
structural semantics identifying and fixing user interface componets 
that are not well formed, or are missing an accessible name or state 
information.”

# Follow on suggestion:

"Both of these use cases can to some degree today be assessed by 
automated accessibility checkers. There are quality issues here, as many 
checkers cannot make subjective assessments of if alternate text 
descriptions are actually useful. The question is to what degree can 
current AI or generative intelligence models bridge this gap and make 
qualitative repairs that are actually fit for purpose. This document 
aims to explore this and suggest where there are gaps and need for 
further research.”

I hope this helps!

Thanks

--
Joshue O’Connor
Director | InterAccess.ie