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Design
Published in Wanda Grimsgaard, Design and Strategy, 2023
In a design-driven project, the human-centred approach is usually an overarching intention, either explicitly or implicitly, in how the company, the designer and the developer naturally work and think. User-centricity is about developing solutions to problems by involving the human perspective in all stages of the problem-solving process. It is a process that starts with studying the people one will be designing for and ends with new solutions tailored to meet their needs. Empathy is a keyword which means establishing deep insight, understanding, and sympathy for and with the users. It involves talking to the relevant people, making participatory observations and focusing on producing solutions to problems rather than just documenting them. Here also lies the potential to ‘reach a higher level of empathy and emotional intelligence in order to see the real bigger picture, one that takes into consideration the environmental aspect13 and non-human players too’ (Bencini, 2021).
Flexible and Stretchable Devices for Human-Machine Interfaces
Published in Muhammad Mustafa Hussain, Nazek El-Atab, Handbook of Flexible and Stretchable Electronics, 2019
Irmandy Wicaksono, Canan Dagdeviren
Emotions play a vital role in our daily life as they enable us to express and understand each other’s feelings. They are represented by external physical expressions and internal mental processes that may be imperceptible to us. The ability to recognize human emotions and simulate empathy has become an important aspect in human-machine interaction systems, prompting the field of affective computing or artificial emotional intelligence (emotion AI) (Picard 1997). Recognizing emotions enables machines to adapt and react depending on the user’s behaviors, allowing a more natural and efficacious mutual relationship between human and computers. Multiple methods have been explored in the past years to monitor and classify human emotions. The most widely used approach involves the detection of facial expressions, speech, body gestures, and physiological signals (Castellano et al. 2008). Except for physiological monitoring, which uses wearable sensors, current approaches of emotion recognition mainly use an external camera in order to recognize facial gestures or microphone to process voice signals. As we have covered recent developments of flexible and stretchable devices for body gesture, speech, and facial expression recognition (Sections 3 through 5), in this section, we will mainly discuss the development of these devices for physiological sensing. The fact that individuals cannot easily control their physiological signals makes sensing them extremely useful, as manipulating these signals to hide our emotions is challenging.
What Makes People Feel Empathy for AI Chatbots? Assessing the Role of Competence and Warmth
Published in International Journal of Human–Computer Interaction, 2023
Previous studies have shown that empathy plays a role in increasing the quality and satisfaction of relationships between employees and consumers. The perceived empathy of AI-based services can also improve human–AI relationships. Advances in AI have enabled computers to gain the capability to express empathy by analyzing and reacting to user expressions (Adam et al., 2021). Users may perceive AI chatbot empathy when chatbots process users’ answers and provide appropriate responses to their inputs (Adam et al., 2021). Similarly, Bove (2019) mentioned that the technology used in service settings could help detect consumers’ needs and distress, consequently responding with empathy. Meanwhile, several scholars reported that empathy could be evoked when people feel warmth, compassion, and concern for others (Davis, 1983; Eisenberg & Lennon, 1983). Pelau et al. (2021) explored the mediation role of perceived empathy and interaction quality between the anthropomorphic characteristics of AI and acceptance intentions. Moreover, Belanche et al. (2021) empirically validated a new theoretical framework called the humanness (i.e., robots’ physical appearance, competence, warmth)–value (i.e., functional, social, monetary, emotional value)–loyalty model. Their results showed that both the perceived competence and warmth of robots positively affect emotional value; in particular, the influence of warmth on other values was not significant. Accordingly, we expect that perceived competence and warmth, as two robust dimensions of the social judgment of AI chatbots, will influence empathy. We posit: