Expanding the Role of Trust in the Experience of Algorithmic Journalism: User Sensemaking of Algorithmic Heuristics in Korean Users

© 2020 Informa UK Limited, trading as Taylor & Francis Group. Algorithmic journalism (AJ) has become widely popular, emerging in mainstream trends. Despite this surging popularity, little is known about the ways through which readers understand and actualize the potential for trust or affordances in AJ. The goal of the study is to highlight principles of algorithmic process in AJ and the processes these principles are perceived, appreciated and acted upon by AJ users. The idea of algorithmic trust is proposed as a new form of digital affordance in algorithm-driven news services. It identifies key issues of AJ and conceptualizes such issues in reference to algorithmic trust by analyzing how they influence reader satisfaction and adoption of AJ. A multi-mixed mixed method integrating interpretive methods and empirical survey was used for Korean users. Algorithmic affordances offer a useful standpoint on the conceptualization of algorithmic trust. Cognitive processes and heuristic mechanisms provide better foundations for algorithm design and development and a stronger basis for design of sensemaking AJ. Based on the study, a theoretical model is proposed to define algorithmic trust in the context of AJ

The perception of humanness in conversational journalism: An algorithmic information-processing perspective

How much do anthropomorphisms influence the perception of users about whether they are conversing with a human or an algorithm in a chatbot environment? We develop a cognitive model using the constructs of anthropomorphism and explainability to explain user experiences with conversational journalism (CJ) in the context of chatbot news. We examine how users perceive anthropomorphic and explanatory cues, and how these stimuli influence user perception of and attitudes toward CJ. Anthropomorphic explanations of why and how certain items are recommended afford users a sense of humanness, which then affects trust and emotional assurance. Perceived humanness triggers a two-step flow of interaction by defining the baseline to make a judgment about the qualities of CJ and by affording the capacity to interact with chatbots concerning their intention to interact with chatbots. We develop practical implications relevant to chatbots and ascertain the significance of humanness as a social cue in CJ. We offer a theoretical lens through which to characterize humanness as a key mechanism of human–artificial intelligence (AI) interaction, of which the eventual goal is humans perceive AI as human beings. Our results help to better understand human–chatbot interaction in CJ by illustrating how humans interact with chatbots and explaining why humans accept the way of CJ

A living lab as socio-technical ecosystem: Evaluating the Korean living lab of internet of things

Living Lab approaches, as one of socio-technical approaches, are effective strategy for user-driven technology development. The recent development of the Internet of Things (loT) and its various technologies appear to be promising possibilities to adopt Living Lab innovation into community domains. Using Living Lab frame, this study examines the developmental processes of the IoT from a multi-level analysis: a micro approach of user acceptance and experience of IoT services; a meso approach of socio-technical evaluation of selected site; and a macro approach of regulation and strategies on IoT. Through the multi-level approach, it conceptualizes a Living Lab process to create, test, and adept a social IoT environment. The findings should guide governments\u27 promotion of IoT services to increase user acceptance by enhancing usability and benefits and ensuring sustainability. The findings also provide guidelines, strategies, and best practices for practitioners to integrate IoT into communities and society effectively. The insights help to conceptualize how the IoT can be situated and contextualized within human-centered contexts. The results of this study show that creating IoT innovations require prudent coordination of different stakeholders and roles across innovation cycle. Particularly user-centered approach warrants a new innovative way to structure and facilitate user involvement within the context of Living Labs for IoT

Does augmented reality augment user affordance? The effect of technological characteristics on game behaviour

This study conceptualises a framework of technological affordances for augmented reality games (ARG) relative to the affordances of internalised and embodied experiences by users. This study examines players’ affordances and investigate how they influence user experience in ARG. It explores how affordances are perceived and enacted by users in an augmented environment to maximise user experience of ARG. A multimethod research approach was utilised that integrated ethnographic and statistical methods. Qualitative study confirmed the general structure of affordance framework, while also revealing relational structures with other variables to explore. Based on the affordance factors identified from the ethnographic methods, a survey questionnaire was created to map and investigate the effects of affordance on the user’s cognitive processes and the influence of affordance on the gameplay process. The results show that technological properties of the ARG system affect opportunities for action available in the environment (affordances). The heuristic role of immersion and presence affordances through underlying cues appear to trigger a player’s sensory representations of affective affordances

How do technological properties influence user affordance of wearable technologies?

© John Benjamins Publishing Company. The Internet of things (IoT) 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\u27 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\u27 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

How do users experience the interaction with an immersive screen?

© 2018 Ultra-high definition television (UHDTV) is designed using a variety of immersive technologies that are believed to promote user engagement. This study examines the immersion feature of UHDTV by focusing on user experience (UX) to determine whether there is a favorable response by users and whether immersion improves their viewing experience. Further, it examines the relationship between UX and the quality perception of UHDTV to develop a conceptual model for users\u27 quality of experience (QoE) with respect to UHDTV. It uses multiple methods combining experiment (to measure the effect of the immersive experience) and survey (to model the UHDTV UX). The results imply an optimum level of immersion as well as the need for user-based quality measurement. The results confirm the existing models of predicting user intentions with digital technologies and have potential to provide new insights to the research of immersive technologies

User Perceptions of Algorithmic Decisions in the Personalized AI System: Perceptual Evaluation of Fairness, Accountability, Transparency, and Explainability

© 2020 Broadcast Education Association. With the growing presence of algorithms and their far-reaching effects, artificial intelligence (AI) will be mainstream trends any time soon. Despite this surging popularity, little is known about the processes through which people perceive and make a sense of trust through algorithmic characteristics in a personalized algorithm system. This study examines the extent to which trust can be linked to how perceptions of automated personalization by AI and the processes of such perceptions influence user heuristic and systematic processes. It examines how fair, accountable, transparent, and interpretable people perceive the use of algorithmic recommendations by digital platforms. When users perceive that the algorithm is fairer, more accountable, transparent, and explainable, they see it as more trustworthy and useful. This demonstrates that trust is of particular value to users and further implies the heuristic roles of algorithmic characteristics in terms of their underlying links to trust and subsequent attitudes toward algorithmic decisions. The processes offer a useful perspective on the conceptualization of AI experience and interaction. User cognitive processes identified provide solid foundations for algorithm design and development and a stronger basis for the design of sensemaking AI services

The actualization of meta affordances: Conceptualizing affordance actualization in the metaverse games

Drawing on the theory of affordance, we conceptualize affordance actualization for the metaverse games (MG) relative to the affordances of internalized and embodied experiences by users. Focusing on MG players\u27 affordances, we examine how they affect the user experience by exploring how affordances are realized and enacted in an extended environment. Based on mixed methods of empirical analysis, we identify relevant affordances, theorize affordance actualization, and characterize the duality of affordance in the metaverse. A heuristic process of immersion and selection of affordances through underlying cues together actualize a player\u27s sensory representations of affective affordances. By identifying how extended reality mediates interactions with users, we contribute to prescriptive knowledge in the form of theoretical considerations and practical implications intended for academics and practitioners working in the context of the extended environment. We propose that affordance actualization helps to theorize the duality of affordance in the metaverse that users shape their metaverse based on their actualized affordance, and at the same time, the metaverse becomes a part of the structure shaping and constraining user actions

The effects of explainability and causability on perception, trust, and acceptance: Implications for explainable AI

© 2020 Artificial intelligence and algorithmic decision-making processes are increasingly criticized for their black-box nature. Explainable AI approaches to trace human-interpretable decision processes from algorithms have been explored. Yet, little is known about algorithmic explainability from a human factors’ perspective. From the perspective of user interpretability and understandability, this study examines the effect of explainability in AI on user trust and attitudes toward AI. It conceptualizes causability as an antecedent of explainability and as a key cue of an algorithm and examines them in relation to trust by testing how they affect user perceived performance of AI-driven services. The results show the dual roles of causability and explainability in terms of its underlying links to trust and subsequent user behaviors. Explanations of why certain news articles are recommended generate users trust whereas causability of to what extent they can understand the explanations affords users emotional confidence. Causability lends the justification for what and how should be explained as it determines the relative importance of the properties of explainability. The results have implications for the inclusion of causability and explanatory cues in AI systems, which help to increase trust and help users to assess the quality of explanations. Causable explainable AI will help people understand the decision-making process of AI algorithms by bringing transparency and accountability into AI systems

How do users interact with algorithm recommender systems? The interaction of users, algorithms, and performance

© 2020 Elsevier Ltd Although algorithms have been widely used to deliver useful applications and services, it is unclear how users actually experience and interact with algorithm-driven services. This ambiguity is even more troubling in news recommendation algorithms, where thorny issues are complicated. This study investigates the user experience and usability of algorithms by focusing on users\u27 cognitive process to understand how qualities/features are received and transformed into experiences and interaction. This work examines how users perceive and feel about issues in news recommendations and how they interact and engage with algorithm-recommended news. It proposes an algorithm experience model of news recommendation integrating the heuristic process of cognitive, affective, and behavioral factors. The underlying algorithm can affect in different ways the user\u27s perception and trust of the system. The heuristic affect occurs when users\u27 subjective feelings about transparency and accuracy act as a mental shortcut: users considered transparent and accurate systems convenient and useful. The mediating role of trust suggests that establishing algorithmic trust between users and NRS could enhance algorithm performance. The model illustrates the users\u27 cognitive processes of perceptual judgment as well as the motivation behind user behaviors. The results highlight a link between news recommendation systems and user interaction, providing a clearer conceptualization of user-centered development and the evaluation of algorithm-based services