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This study aims to explore the complexities of digital accessibility, focusing on the essential tasks of evaluating and monitoring the accessibility of digital content. It seeks to identify the main challenges encountered by practitioners on the field and to reveal best practices and research opportunities for enhancing digital accessibility. Using a mixed-methods approach, the study combines an online survey and in-depth interviews, gathering insights from 27 practitioners across 16 countries. The findings underscore substantial gaps in education and professional training within accessibility. Challenges identified include ensuring technical compliance while addressing user needs, limitations of current automated tools, especially for mobile accessibility, and the disparity between formal compliance and user-centric accessibility. The study highlights best practices such as comprehensive training, effective project management, and innovative testing strategies. This research underscores the need for refined evaluation methodologies and a deeper understanding of accessibility principles among stakeholders. It advocates for collaborative efforts to address the nuanced challenges of making digital spaces universally accessible. Future research should leverage emerging technologies, particularly Artificial Intelligence, to enhance accessibility evaluations and bridge the gap between technical compliance and user experience.

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The digitalisation of the public sphere is an ongoing process accelerated by the ubiquitousness of the internet. For the over a billion people estimated to live with an impairment, this digitalisation comes with barriers that can represent an altogether exclusion from the digital realm, hindering their full participation in society. This context should compel stakeholders involved in the development of digital products to consider accessibility as an essential requirement. However, that may not always be the case. This work is the product of a scoping literature review guided by the overarching topic of accessibility in the context of the web. After arguing that disability as a phenomenon might be more prevalent than one would think, it frames web accessibility as a human right that benefits all individuals, while also having important dimensions that businesses would regret ignoring.

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This article proposes the Artificial Intelligence Models Switching Mechanism (AIMSM), a novel approach to optimize system resource utilization by allowing systems to switch AI models during runtime in dynamic environments. Many real-world applications utilize multiple data sources and various AI models for different purposes. In many of those applications, every AI model doesn’t have to operate all the time. The AIMSM strategically allows the system to activate and deactivate these models, focusing on system resource optimization. The switching of each AI model can be based on any information, such as context or previous results. In the case study of an autonomous mobile robot performing computer vision tasks, the AIMSM helps the system to achieve a significant increment in performance, with a 50% average increase in frames per second (FPS) rate, for this specific case study, assuming that no erroneous switching occurred. Experimental results have demonstrated that the AIMSM can improve system resource utilization efficiency when properly implemented, optimize overall resource consumption, and enhance system performance. The AIMSM presented itself as a better alternative to permanently loading all the models simultaneously, improving the adaptability and functionality of the systems. It is expected that using the AIMSM will yield a performance improvement that is particularly relevant to systems with multiple AI models of a complex nature, where such models do not need to be all continuously executed or systems that will benefit from lower resource usage. Code is available at https://github.com/BrunoGeorgevich/AIMSM.

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