AI-Powered Real-time Accessibility Enhancement: A Solution for Web Content Accessibility Issues

Samir Dash

doi:10.15575/join.v9i1.1310

Authors

Samir Dash Cisco Systems (India) Pvt. Ltd., Bengaluru, Karnataka, India

DOI:

https://doi.org/10.15575/join.v9i1.1310

Keywords:

Artificial Intelligence, Accessibility, web, Semantic Web, ARIA

Abstract

The web accessibility landscape is a significant challenge, with 96.3% of home pages displaying issues with Web Content Accessibility Guidelines (WCAG). This paper addresses the primary accessibility issues, such as missing Accessible Rich Internet Applications (ARIA) landmarks, ill-formed headings, low contrast text, and inadequate form labeling. The dynamic nature of modern web and cloud applications presents challenges, such as developers' limited awareness of accessibility implications, potential code bugs, and API failures. To address these issues, an AI-enabled system is proposed to dynamically enhance web accessibility. The system uses machine learning algorithms to identify and rectify accessibility issues in real-time, integrating with existing development workflows. Empirical evaluation and case studies demonstrate the efficacy of this solution in improving web accessibility across diverse scenarios.

References

A. Hambley, Y. Yesilada, M. Vigo, and S. Harper, “Web Structure Derived Clustering for Optimised Web Accessibility Evaluation,” in Proceedings of the ACM Web Conference 2023, New York, NY, USA: ACM, Apr. 2023, pp. 1345–1354. doi: 10.1145/3543507.3583508.

Z. Jordanoski and M. Meyerhoff Nielsen, “The challenge of web accessibility: an evaluation of selected government websites and service portals of high, middle and low-income countries,” in Proceedings of the 16th International Conference on Theory and Practice of Electronic Governance, New York, NY, USA: ACM, Sep. 2023, pp. 101–110. doi: 10.1145/3614321.3614343.

G. Carlsson, O. Jonsson, S. Olander, M. Salén, E. Månsson Lexell, and B. Slaug, “Exploration of a Web-based accessibility tool for public facilities,” Facilities, vol. 41, no. 15/16, pp. 66–84, Dec. 2023, doi: 10.1108/F-10-2022-0132.

Y. Bai, J. Grzeslo, B. Min, and K. Jayakar, “Accessibility of local government websites: influence of financial resources, county characteristics and local demographics,” Univers Access Inf Soc, vol. 20, no. 4, pp. 851–861, Nov. 2021, doi: 10.1007/s10209-020-00752-5.

N. Palani, The Web Accessibility Project. Boca Raton: Auerbach Publications, 2022. doi: 10.1201/9781003299431.

T. W. Hostetler et al., “Web accessibility trends and implementation in dynamic web applications,” Feb. 2022.

A. Hambley, “Empirical web accessibility evaluation for blind web users,” ACM SIGACCESS Accessibility and Computing, no. 129, pp. 1–5, Jan. 2021, doi: 10.1145/3458055.3458057.

B. Gomes, J. Rios, and K. R. H. Rodrigues, “Challenges for the Implementation of Accessible Web and Mobile Systems,” 2020, pp. 138–158. doi: 10.1007/978-3-030-46130-0_8.

C. Kearney-Volpe and A. Hurst, “Accessible Web Development,” ACM Trans Access Comput, vol. 14, no. 2, pp. 1–32, Jun. 2021, doi: 10.1145/3458024.

K. Wallace, “Workshop: Accessibility in Web Design,” in 2022 IEEE International Professional Communication Conference (ProComm), IEEE, Jul. 2022, pp. 462–463. doi: 10.1109/ProComm53155.2022.00091.

S. S. Macakoglu and S. Peker, “Web accessibility performance analysis using web content accessibility guidelines and automated tools: a systematic literature review,” in 2022 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA), IEEE, Jun. 2022, pp. 1–8. doi: 10.1109/HORA55278.2022.9799981.

N. Palani, The Web Accessibility Project. Boca Raton: Auerbach Publications, 2022. doi: 10.1201/9781003299431.

K. Boyalakuntla, A. S. M. Venigalla, and S. Chimalakonda, “WAccess -- A Web Accessibility Tool based on WCAG 2.2, 2.1 and 2.0 Guidelines,” Jul. 2021.

R. S. Germano and I. Frango Silveira, “WCAG-Easy Tool?: A tool based in the WCAG to learn web accessibility,” in 2022 17th Iberian Conference on Information Systems and Technologies (CISTI), IEEE, Jun. 2022, pp. 1–6. doi: 10.23919/CISTI54924.2022.9820012.

R. P. M. Fortes, H. L. Antonelli, and A. de Lima Salgado, “Accessibility and Usability Evaluation of Rich Internet Applications,” in Proceedings of the 22nd Brazilian Symposium on Multimedia and the Web, New York, NY, USA: ACM, Nov. 2016, pp. 7–8. doi: 10.1145/2976796.2988221.

F. Zambrano and E. Muñoz, “Statistical machine learning methods applied in the study of web accessibility: a literature review,” Minerva, vol. 1, no. Special, pp. 150–157, Dec. 2022, doi: 10.47460/minerva.v1iSpecial.90.

J. O. Connor, Pro HTML5 Accessibility. Berkeley, CA: Apress, 2012. doi: 10.1007/978-1-4302-4195-9.

M. M. Alnahari, J. Chakraborty, M. Mohamed, and A. Ali-Gombe, “Arabic web accessibility analysis: findings from a usability study of Arabian web developers,” Human-Intelligent Systems Integration, vol. 4, no. 3–4, pp. 81–96, Dec. 2022, doi: 10.1007/s42454-022-00045-7.

Open AI, “Fine-tuning,” Open AI Documentation. Accessed: Apr. 15, 2024. [Online]. Available: https://platform.openai.com/docs/guides/fine-tuning/preparing-your-dataset.

S. Rahman, S. Khan, and F. Porikli, “A Unified Approach for Conventional Zero-Shot, Generalized Zero-Shot, and Few-Shot Learning,” IEEE Transactions on Image Processing, vol. 27, no. 11, pp. 5652–5667, Nov. 2018, doi: 10.1109/TIP.2018.2861573.