The exponential growth of algorithms has made establishing a trusted relationship between human and artificial intelligence increasingly important. Algorithm systems such as chatbots can play an important role in assessing a user’s credibility on algorithms. Unless users believe the chatbot’s information is credible, they are not likely to be willing to act on the recommendation. This study examines how literacy and user trust influence perceptions of chatbot information credibility. Results confirm that algorithmic literacy and users’ trust play a pivotal role (...) in how users form perceptions of the credibility of chatbot messages and recommendations. Insights on how user trust is related to credibility provide a useful perspective on the conceptualization of algorithmic credibility. Algorithmic information processing that has been identified provides better foundations for algorithm design and development and a stronger basis for the design of sense-making chatbot journalism. (shrink)

A number of artificial intelligence systems have been proposed to assist users in identifying the issues of algorithmic fairness and transparency. These AI systems use diverse bias detection methods from various perspectives, including exploratory cues, interpretable tools, and revealing algorithms. This study explains the design of AI systems by probing how users make sense of fairness and transparency as they are hypothetical in nature, with no specific ways for evaluation. Focusing on individual perceptions of fairness and transparency, this study examines (...) the roles of normative values in over-the-top platforms by empirically testing their effects on sensemaking processes. A mixed-method design incorporating both qualitative and quantitative approaches was used to discover user heuristics and to test the effects of such normative values on user acceptance. Collectively, a composite concept of transparent fairness emerged around user sensemaking processes and its formative roles regarding their underlying relations to perceived quality and credibility. From a sensemaking perspective, this study discusses the implications of transparent fairness in algorithmic media platforms by clarifying how and what should be done to make algorithmic media more trustable and reliable platforms. Based on the findings, a theoretical model is developed to define transparent fairness as an essential algorithmic attribute in the context of OTT platforms. (shrink)

This book serves as a guide to understanding the dynamics of AI in human contexts with a specific focus on the generation, sharing, and consumption of misinformation online. How do humans and AI interact? How is AI shaping our understanding of ourselves and our societies? What are the interaction mechanisms that govern how humans and algorithms contribute to misinformation online? And how do we bridge the gap between ethical considerations and practical realities to make responsible, reliable systems? Exploring these questions, (...) the book empowers humans to make AI design choices that allow them meaningful control over AI and the online sphere. Calling for an interdisciplinary approach toward human-misinformation algorithmic interaction that focuses on building methods and tools that robustly deal with complex psychological/social phenomena, the book offers a compelling insight into the future of AI-based society. (shrink)

The Internet of things affords people plenty of opportunities and a higher quality of life as well as drives a huge amount of data. By drawing on the concept of affordances, this study examines the user experience of personal informatics focusing on the technological and affective nature of affordance. A multi-mixed approach is used by combining qualitative methods and a quantitative survey. Results of the qualitative methods revealed a series of factors that related to the affordance of personal informatics, whereas (...) results of the user model confirmed a significant role for connectivity, control, and synchronicity affordance regarding their underlying link to other variables, namely, expectation, confirmation, and satisfaction. The experiments showed that users’ affordances are greatly influenced by personal traits with interactivity tendency. The findings imply the embodied cognition process of personal informatics in which technological qualities are shaped by users’ perception, traits, and context. The results establish a foundation for wearable technologies through a heuristic quality assessment tool from a user embodied cognitive process. They confirm the validity and utility of applying affordances to the design of IoT as a useful concept, as well as prove that the optimum mix of affordances is crucial to the success or failure of IoT design. (shrink)