Coverage of Artificial Intelligence and Machine Learning within Academic Literature, Canadian Newspapers, and Twitter Tweets: The Case of Disabled People
Bibliographic record
Abstract
Artificial intelligence (AI) and machine learning (ML) advancements increasingly impact society and AI/ML ethics and governance discourses have emerged. Various countries have established AI/ML strategies. “AI for good” and “AI for social good” are just two discourses that focus on using AI/ML in a positive way. Disabled people are impacted by AI/ML in many ways such as potential therapeutic and non-therapeutic users of AI/ML advanced products and processes and by the changing societal parameters enabled by AI/ML advancements. They are impacted by AI/ML ethics and governance discussions and discussions around the use of AI/ML for good and social good. Using identity, role, and stakeholder theories as our lenses, the aim of our scoping review is to identify and analyze to what extent, and how, AI/ML focused academic literature, Canadian newspapers, and Twitter tweets engage with disabled people. Performing manifest coding of the presence of the terms “AI”, or “artificial intelligence” or “machine learning” in conjunction with the term “patient”, or “disabled people” or “people with disabilities” we found that the term “patient” was used 20 times more than the terms “disabled people” and “people with disabilities” together to identify disabled people within the AI/ML literature covered. As to the downloaded 1540 academic abstracts, 234 full-text Canadian English language newspaper articles and 2879 tweets containing at least one of 58 terms used to depict disabled people (excluding the term patient) and the three AI terms, we found that health was one major focus, that the social good/for good discourse was not mentioned in relation to disabled people, that the tone of AI/ML coverage was mostly techno-optimistic and that disabled people were mostly engaged with in their role of being therapeutic or non-therapeutic users of AI/ML influenced products. Problems with AI/ML were mentioned in relation to the user having a bodily problem, the usability of AI/ML influenced technologies, and problems disabled people face accessing such technologies. Problems caused for disabled people by AI/ML advancements, such as changing occupational landscapes, were not mentioned. Disabled people were not covered as knowledge producers or influencers of AI/ML discourses including AI/ML governance and ethics discourses. Our findings suggest that AI/ML coverage must change, if disabled people are to become meaningful contributors to, and beneficiaries of, discussions around AI/ML.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.040 | 0.100 |
| Science and technology studies | 0.017 | 0.008 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".