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The application and use cases for conversational agents (CAs) are versatile. Smart speakers such as Alexa and Google Home are used in smart home environments, digital agents are integrated into car systems and chatbots are increasingly used in customer service processes. However, human–computer interaction researchers identify and investigate a wide-ranging variety of aspects impeding the usage of CAs by end-users. In general, impediments differ depending on use case contexts, user group characteristics and the CA's technological infrastructure. Hence, it is difficult and often ambiguous for designers and developers to generate an appropriate awareness about aspects impeding CA usage. We address this problem, by conducting a systematic review of 65 publications surveying impeding aspects of the usage of CAs. [ABSTRACT FROM AUTHOR]

There is increasing use of machine learning for the health financing functions (revenue raising, pooling and purchasing), yet evidence lacks for its effects on the universal health coverage (UHC) objectives. This paper provides a synopsis of the use cases of machine learning and their potential benefits and risks. The assessment reveals that the various use cases of machine learning for health financing have the potential to affect all the UHC intermediate objectives--the equitable distribution of resources (both positively and negatively); efficiency (primarily positively); and transparency (both positively and negatively). There are also both positive and negative effects on all three UHC final goals, that is, utilization of health services in line with need, financial protection and quality care. When the use of machine learning facilitates or simplifies health financing tasks that are counterproductive to UHC objectives, there are various risks--for instance risk selection, cost reductions at the expense of quality care, reduced financial protection or over-surveillance. Whether the effects of using machine learning are positive or negative depends on how and for which purpose the technology is applied. Therefore, specific health financing guidance and regulations, particularly for (voluntary) health insurance, are needed. To inform the development of specific health financing guidance and regulation, we propose several key policy and research questions. To gain a better understanding of how machine learning affects health financing for UHC objectives, more systematic and rigorous research should accompany the application of machine learning. Alors que l'apprentissage machine connait un usage croissant pour les fonctions de financement de la sante (collecte de revenus, mise en commun et achat), les preuves manquent quant a ses effets sur les objectifs de la couverture sanitaire universelle (CSU). Ce document presente une synthese des cas d'utilisation de l'apprentissage machine et de leurs avantages et risques potentiels. [(evaluation revele que les differents cas d'utilisation de l'apprentissage machine pour le financement de la sante sont susceptibles d'affecter tous les objectifs intermediaires de la CSU: la distribution equitable des ressources (a la fois positivement et negativement), l'efficacite (principalement positivement) et la transparence (a la fois positivement et negativement). Il existe egalement des effets positifs et negatifs sur les trois objectifs finaux de la CSU, a savoir l'utilisation des services de sante en fonction des besoins, la protection financiere et la qualite des soins. Lorsque l'utilisation de l'apprentissage machine facilite ou simplifie des taches de financement de la sante qui vont a lencontre des objectifs de la CSU, differents risques se font jour, comme la selection des risques, la reduction des couts au detriment de la qualite des soins, la reduction de la protection financiere ou la surveillance excessive. Les effets positifs ou negatifs de l'utilisation de l'apprentissage machine dependent de la maniere dont la technologie est appliquee et de l'objectif poursuivi. C'est pourquoi s'imposent des orientations et des reglementations specifiques en matiere de financement de la sante, en particulier pour l'assurance maladie (volontaire). Afin d'eclairer l'elaboration de telles orientations et reglementations, nous proposons plusieurs questions cles en matiere de politique et de recherche. Pour mieux comprendre la facon dont l'apprentissage machine affecte le financement de la sante dans le cadre des objectifs de la CSU, une recherche plus systematique et plus rigoureuse devrait accompagner la mise en oeuvre de l'apprentissage machine. Aunque el uso del aprendizaje automatico para las funciones de financiacion sanitaria (recaudacion de ingresos, mancomunacion y compra) es cada vez mayor, no hay evidencias de sus efectos sobre los objetivos de la cobertura sanitaria universal (CSU). Este documento ofrece una sinopsis de los casos de uso del aprendizaje automatico y sus posibles beneficios y riesgos. La evaluacion revela que los diversos casos de uso del aprendizaje automatico para la financiacion sanitaria tienen el potencial de afectar a todos los objetivos intermedios de la CSU: la distribucion equitativa de los recursos (tanto positiva como negativamente), la eficiencia (principalmente positiva) y la transparencia (tanto positiva como negativamente). Tambien hay efectos positivos y negativos en los tres objetivos finales de la CSU, es decir, la utilizacion de los servicios sanitarios en funcion de las necesidades, la proteccion financiera y la atencion de calidad. El uso del aprendizaje automatico para facilitar o simplificar tareas de financiacion sanitaria contraproducentes para los objetivos de la CSU plantea diversos riesgos, como la seleccion de riesgos, la reduccion de costes a expensas de la calidad de la atencion, la disminucion de la proteccion financiera o el exceso de vigilancia. El caracter positivo o negativo de los efectos del aprendizaje automatico depende de como y con que fin se aplique la tecnologia. Por lo tanto, se necesitan directrices y reglamentos especificos para la financiacion sanitaria, en particular para los seguros de salud (voluntarios). Proponemos varias preguntas clave en materia de politica e investigacion para contribuir a la elaboracion de directrices y reglamentos especificos sobre financiacion sanitaria. A fin de comprender mejor como afecta el aprendizaje automatico al logro de los objetivos de la CSU en el ambito de la financiacion sanitaria, la aplicacion del aprendizaje automatico deberia ir acompanada de una investigacion mas sistematica y rigurosa., Introduction Over the past 10 years, the number of publications on artificial intelligence and machine learning related to health financing tasks has markedly increased, (1) in line with the trend [...]

AI adoption is accelerating rapidly, and security is racing to keep up with the changes it introduces. While AI can transform employee productivity and workplace efficiency, it also amplifies existing [...]

As I write this, Hurricane Milton is hurtling towards Tampa, Florida. With no time to catch their breath after Hurricane Helene, those in the path of the storm find themselves [...]

In a first, a team led by researchers at Harvard Medical School has mapped specific word meanings to particular brain cells and patterns. Their findings show how single-neuron recordings can [...]

The introduction of Large Language Models (LLM) to the chatbot landscape has opened intriguing possibilities for academic libraries to offer more responsive and institutionally contextualized support to users, especially outside of regular service hours. While a few academic libraries currently employ AI-based chatbots on their websites, this service has not yet become the norm and there are no best practices in place for how academic libraries should launch, train, and assess the usefulness of a chatbot. In summer 2023, staff from the University of Delaware's Morris Library information technology (IT) and reference departments came together in a unique partnership to pilot a low-cost AI-powered chatbot called UDStax. The goals of the pilot were to learn more about the campus community's interest in engaging with this tool and to better understand the labor required on the staff side to maintain the bot. After researching six different options, the team selected Chatbase, a subscription-model product based on ChatGPT 3.5 that provides user-friendly training methods for an AI model using website URLs and uploaded source material. Chatbase removed the need to utilize the OpenAI API directly to code processes for submitting information to the AI engine to train the model, cutting down the amount of work for library information technology and making it possible to leverage the expertise of reference librarians and other public-facing staff, including student workers, to distribute the work of developing, refining, and reviewing training materials. This article will discuss the development of prompts, leveraging of existing data sources for training materials, and workflows involved in the pilot. It will argue that, when implementing AI-based tools in the academic library, involving staff from across the organization is essential to ensure buy-in and success. Although chatbots are designed to hide the effort of the people behind them, that labor is substantial and needs to be recognized., INTRODUCTION The University of Delaware is an R1 research-intensive institution with an enrollment of approximately 25,000 students. The library was an early adopter of technology, with email, web-form based, and [...]

Frontiers TECHNOLOGY ADOPTION When Generative AI Meets Product Development From ideation to user testing, large language models are allowing companies to explore more ideas and iterate faster. By Tucker J. [...]

Claims analysis and risk management is key to avoiding fraud and managing risk in the life insurance industry. Though visualization plays a fundamental role in supporting analysis tasks in the business domain, exploring user behaviour remains a challenging task. The prevalence of natural language interactions enhanced with data visualization has become quite the norm. With the increasing demand for visualization tools and varying levels of user expertise, the use of a natural language interface (NLI) is common. However, the design of visual analytics tools enhanced by NLIs for risk management and claim analysis requires thorough task analysis and domain expertise. In this work, we investigate an alternative approach through a natural language interaction-based interactive visualization such as a chart, pie chart, or histogram, which can be used for insurance claim analysis and risk management. We design a new visual analytics solution named InsCRMVis which is enhanced by NLIs. We present an expert evaluation of InsCRMVis based on the Task Load Index questionnaire which is currently the standard metrics measure to investigate its usability (effectiveness, efficiency, satisfaction) to support human task performance. To evaluate its performance, we performed a user study of ten experts which suggests that InsCRMVis can provide better insights and assistance to insurance managers in reducing loss and guiding changes to insurance premium policies. Furthermore, we provide a concise set of guidelines that can be used when visualizing risk to avoid dangers in the insurance domain. We discuss the challenges associated with the use of a visualization system in the insurance industry, focusing on aspects related to visualization research. [ABSTRACT FROM AUTHOR]