Leveraging AI and Machine Learning to Address ADA Non-Compliance in Web Applications: A Novel Approach to Enhancing Accessibility

Abstract

It is crucial to follow the Americans with Disabilities Act (ADA) standards for web applications to make sure that all customers with disabilities have equal access to the digital spaces. Nevertheless, it is seen that numerous web platforms still experience major accessibility problems stemming from non-adherence to these guidelines. This paper proposes a new technique that applies AI and ML to identify and correct such weaknesses in web applications. Using AI tools and ML algorithms, the system can detect violations of accessibility, predict possible barriers, and recommend timely changes, thus making the process of remediation much easier. We also look at the development of dynamic interfaces and custom accessibility features that meet the needs of users with disabilities. This approach aims at bridging the gap between the current accessibility guidelines and their real-world application and provides a solution that is easily implementable by the developers, so that the web applications are accessible to everyone. The study proves that the combination of AI and ML can significantly improve the availability of web applications that do not meet ADA requirements, thus promoting equal access to the web.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 3 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.