Affective Trash Bin Signage to Promote Waste Segregation

Dy, Arvidas Kio; Lazo, Margarita; Santos, Andreana Gabrielle; Seva, Rosemary R.

doi:10.1007/978-3-030-74614-8_3

Cited by 1 publication

(1 citation statement)

References 23 publications

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“…In other words, the idea is to identify a specific design element that can potentially engender a particular emotion that will lead to a specific change in the target behavior. This concept has been used in a variety of domains such as in the user interface of children's e-learn to create positive attachment [61]; computer game design to enhance enjoyment and happiness [62]; eco-labels to promote positive affect which triggers purchase intention [63]; social robots to enhance positive perception and preference [35]; and trash bin signages to encourage waste segregation [64] to name a few.…”

Section: Emotional or Affective Designmentioning

confidence: 99%

Affective Design Analysis of Explainable Artificial Intelligence (XAI): A User-Centric Perspective

Bernardo

Seva

2023

Informatics

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Explainable Artificial Intelligence (XAI) has successfully solved the black box paradox of Artificial Intelligence (AI). By providing human-level insights on AI, it allowed users to understand its inner workings even with limited knowledge of the machine learning algorithms it uses. As a result, the field grew, and development flourished. However, concerns have been expressed that the techniques are limited in terms of to whom they are applicable and how their effect can be leveraged. Currently, most XAI techniques have been designed by developers. Though needed and valuable, XAI is more critical for an end-user, considering transparency cleaves on trust and adoption. This study aims to understand and conceptualize an end-user-centric XAI to fill in the lack of end-user understanding. Considering recent findings of related studies, this study focuses on design conceptualization and affective analysis. Data from 202 participants were collected from an online survey to identify the vital XAI design components and testbed experimentation to explore the affective and trust change per design configuration. The results show that affective is a viable trust calibration route for XAI. In terms of design, explanation form, communication style, and presence of supplementary information are the components users look for in an effective XAI. Lastly, anxiety about AI, incidental emotion, perceived AI reliability, and experience using the system are significant moderators of the trust calibration process for an end-user.

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