Digital Health-Enabled Community-Centered Care: Scalable Model to Empower Future Community Health Workers Using Human-in-the-Loop Artificial Intelligence

Digital health–enabled community-centered care (D-CCC) represents a pioneering vision for the future of community-centered care. D-CCC aims to support and amplify the digital footprint of community health workers through a novel artificial intelligence–enabled closed-loop digital health platform designed for, and with, community health workers. By focusing digitalization at the level of the community health worker, D-CCC enables more timely, supported, and individualized community health worker–delivered interventions. D-CCC has the potential to move community-centered care into an expanded, digitally interconnected, and collaborative community-centered health and social care ecosystem of the future, grounded within a robust and digitally empowered community health workforce.</p

MMSS: A storytelling simulation software to mitigate misinformation on social media

This paper proposes a modular python implementation of a storytelling simulation. The software evaluates misinformation mitigation strategies over social media and visualizes the investigated scenarios’ potential outcomes. Our software integrates information diffusion and control models components. The control model mitigates users’ exposure to misinformation with social fairness awareness, while the diffusion model predicts the outcome from the control model. During the interaction of both models, a graph coloring algorithm traces the interaction within specific time intervals. Then, it generates meta-data to construct visuals of predicted near-future states of the social network to help support decision-making and evaluate proposed mitigation strategies.publishedVersionPaid open acces

How to Blend Journalistic Expertise with Artificial Intelligence for Research and Verifying News Stories

The use of AI technology can help to automate news verification workflows, while significantly innovating journalism practices. However, most existing systems are designed in isolation without interactive collaboration with journalists. ‘DMINR’ project aims to bring humans-at-the-center of AI loop for developing a powerful tool that is sympathetic to the way journalists work. In this paper, we attempt to understand how AI can shape journalists’ practices and, crucially, be shaped by them; we aim to design human-centred AI tool that works in synergy with journalists’ practices and strike a useful balance between human and machine intelligence. In this paper, we conducted a Co-design workshop to inform the design of the ‘DMINR’ system. Based on the findings, we outline the main challenges for designing AI systems in the context of journalism, that can serve as a resource for Human-AI interaction design

Semantic TrueLearn: Using Semantic Knowledge Graphs in Recommendation Systems

In informational recommenders, many challenges arise from the need to handle the semantic and hierarchical structure between knowledge areas. This work aims to advance towards building a state-aware educational recommendation system that incorporates semantic relatedness between knowledge topics, propagating latent information across semantically related topics. We introduce a novel learner model that exploits this semantic relatedness between knowledge components in learning resources using the Wikipedia link graph, with the aim to better predict learner engagement and latent knowledge in a lifelong learning scenario. In this sense, Semantic TrueLearn builds a humanly intuitive knowledge representation while leveraging Bayesian machine learning to improve the predictive performance of the educational engagement. Our experiments with a large dataset demonstrate that this new semantic version of TrueLearn algorithm achieves statistically significant improvements in terms of predictive performance with a simple extension that adds semantic awareness to the model