Ryan Watkins

Faruqe, F., Medsker, L., Watkins, R. (2023). ATIAS: A Model for Understanding Intentions to Use AI Technology. In: Daimi, K., Alsadoon, A., Coelho, L. (eds) Cutting Edge Applications of Computational Intelligence Tools and Techniques. Studies in Computational Intelligence, vol 1118. Springer, Cham.

Abstract: The interdisciplinary quantitative research method presented in this chapter is used to investigate people’s trust in, and intention to use, AI systems. ATIAS (AI Trust and Intention to use AI Systems) is a hybrid model that combines AI ethics variables with technology acceptance model (TAM) variables. The approach is appropriate for surveys of large populations of consumers and other decision-makers to collect data on their levels of trust in AI and their intentions to choose and use AI systems. In this chapter, ATIAS is applied to the healthcare domain, where AI is increasingly being used. ATIAS is used to examine the impact of known technology acceptance factors and AI ethical factors on users’ trust in and positive attitudes toward AI. The method uses Partial Least Squares Structural Equation Modeling (PLS-SEM) as the data analysis method. ATIAS addresses the gap in the current research on human trust in AI systems, which tends to focus on either ethical factors or technology acceptance factors. By combining both types of factors in a hybrid model, the approach aims to provide a more comprehensive understanding of why people use AI systems. ATIAS may prove valuable for policymakers, AI system designers, and healthcare providers who need to understand the factors that influence users’ trust in AI systems. By identifying the factors that are most important in shaping users’ attitudes toward AI, the method may inform the development of more effective AI systems that are trusted and accepted by users.

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Watkins, R., Human, S. (2022). Needs-aware artificial intelligence: AI that ‘serves [human] needs’. AI and Ethics.

Abstract: By defining the current limits (and thereby the frontiers), many boundaries are shaping, and will continue to shape, the future of Artificial Intelligence (AI). We push on these boundaries to make further progress into what were yesterday’s frontiers. They are both pliable and resilient—always creating new boundaries of what AI can (or should) achieve. Among these are technical boundaries (such as processing capacity), psychological boundaries (such as human trust in AI systems), ethical boundaries (such as with AI weapons), and conceptual boundaries (such as the AI people can imagine). It is within these boundaries that we find the construct of needs and the limitations that our current concept of need places on the future AI.

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Watkins, R. (2023). Guidance for researchers and peer‑reviewers on the ethical use of Large Language Models (LLMs) in scientific research workflows. AI and Ethics.

Abstract: For researchers interested in exploring the exciting applications of Large Language Models (LLMs) in their scientific investigations, there is currently limited guidance and few norms for them to consult. Similarly, those providing peer-reviews on research articles where LLMs were used are without conventions or standards to apply or guidelines to follow. This situation is understandable given the rapid and recent development of LLMs that are capable of valuable contributions to research workflows (such as OpenAI’s ChatGPT). Nevertheless, now is the time to begin the development of norms, conventions, and standards that can be applied by researchers and peer-reviewers. By applying the principles of Artificial Intelligence (AI) ethics, we can better ensure that the use of LLMs in scientific research aligns with ethical principles and best practices. This editorial hopes to inspire further dialogue and research in this crucial area of scientific investigation.

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