Chukwunonso Henry Nwokoye

Chukwunonso Henry Nwokoye, Maria J. P. Peixoto, Akriti Pandey et al.

The increasing integration of Artificial Intelligence (AI) into everyday life makes it essential to explain AI-based decision-making in a way that is understandable to all users, including those with disabilities. Accessible explanations are crucial as accessibility in technology promotes digital inclusion and allows everyone, regardless of their physical, sensory, or cognitive abilities, to use these technologies effectively. This paper presents a systematic literature review of the research on the accessibility of Explainable Artificial Intelligence (XAI), specifically considering persons with sight loss. Our methodology includes searching several academic databases with search terms to capture intersections between XAI and accessibility. The results of this survey highlight the lack of research on Accessible XAI (AXAI) and stress the importance of including the disability community in XAI development to promote digital inclusion and accessibility and remove barriers. Most XAI techniques rely on visual explanations, such as heatmaps or graphs, which are not accessible to persons who are blind or have low vision. Therefore, it is necessary to develop explanation methods through non-visual modalities, such as auditory and tactile feedback, visual modalities accessible to persons with low vision, and personalized solutions that meet the needs of individuals, including those with multiple disabilities. We further emphasize the importance of integrating universal design principles into AI development practices to ensure that AI technologies are usable by everyone.

Chukwunonso Henry Nwokoye, Blessing Oluchi, Sharna Waldron et al.

The lack of epidemiological data in wireless sensor networks (WSNs) is a fundamental difficulty in constructing robust models to forecast and mitigate threats such as viruses and worms. Many studies have examined different epidemic models for WSNs, focusing on how malware infections spread given the network's specific properties, including energy limits and node mobility. In this study, an agent-based implementation of the susceptible-exposed-infected-recovered-vaccinated (SEIRV) mathematical model was employed for machine learning (ML) predictions. Using tools such as NetLogo's BehaviorSpace and Python, two epidemic synthetic datasets were generated and prepared for the application of several ML algorithms. Posed as a regression problem, the infected and recovered nodes were predicted, and the performance of these algorithms is compared using the error metrics of the train and test sets. The predictions performed well, with low error metrics and high R^2 values (0.997, 1.000, 0.999, 1.000), indicating an effective fit to the training set. The validation values were lower (0.992, 0.998, 0.971, and 0.999), as is typical when evaluating model performance on unseen data. Based on the recorded performances, support vector, linear, Lasso, Ridge, and ElasticNet regression were among the worst-performing algorithms, while Random Forest, XGBoost, Decision Trees, and k-nearest neighbors achieved the best results.