Question 1

What portion of WCAG criteria does the automated check cover?

Accepted Answer

The ML-driven audit automatically verifies 30–40% of WCAG 2.1 AA rules, including color contrast, structure, roles, and focus management. The rest are addressed by specialized models for alt-text, visual contrast, and text readability. None of the manual steps are necessary.

Question 2

How extended is the deployment timeline?

Accepted Answer

Building the audit engine takes two to five months. Adding assistive capabilities like text alternatives, subtitles, and simplification adds three to six months. None of the timelines are fixed without a codebase review. local_entities: None

Question 3

What computational resources are required?

Accepted Answer

For the vision pipeline and LLM inference, we suggest 4×A10G graphics cards (handling 2500 pages in 40 minutes). For Whisper and text-to-text models, a CPU with AVX-512 is enough. None of the hardware is exotic. local_entities: None

Question 4

How is the solution incorporated into development workflows?

Accepted Answer

We plug axe-core and ML extensions into GitHub Actions or GitLab CI. New pull requests are blocked for fresh WCAG breaches. It also works with Storybook and Playwright for component-level checks. None of the integrations require deep code changes. local_entities: None

Question 5

What metrics assess alt-text quality?

Accepted Answer

We use ROUGE-L (0.67 after fine-tuning) and BLEU. A BERT-based classifier measures informativeness (F1 0.84). Contextual fit to the image is also evaluated. None of these metrics are perfect but they guide improvements. local_entities: None

Machine-Learning-Based Accessibility Suite

AI Development Areas

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