Air pollution poses significant health, social, and economic risks, necessitating effective communication to promote awareness and behavioural adaptation. This study explores how combining behavioural and science communication strategies shapes stakeholder participation in co-designing air quality (AQ) communication technologies and how stakeholders engage with, represent and negotiate environmental data and issues. Data was collected across 11 workshops involving 169 participants of different ages, combining education, storytelling, user testing and participatory design to foster learning and dialogue. Findings highlight that AQ communication is entangled with questions of data usability, trust and environmental literacy. Participants emphasised the importance of audience-specific AQ messaging, integrating AQ technical data with comprehensible visualisations, and combining persuasive communication with evidence-based information. Tensions emerged between individual adaptive behaviours and structural barriers, reinforcing the socio-ecological complexity of AQ communication. Challenges included involving underrepresented groups and assessing long-term impacts of interventions. The findings offer insights into how public engagement can be utilised to inform the design of an AQ communication technology to support protective and sustainable behaviours. Future research should explore ways to enhance inclusivity, refine message design for different user groups, and assess the impact of participatory AQ communication initiatives.

Deepfake technology is often used to create non-consensual synthetic intimate imagery (NSII), mainly of celebrity women. Through Critical Discursive Psychological analysis, we ask; i) how celebrities construct being targeted by deepfakes and ii) how they navigate infrastructural and social obstacles when seeking recourse. In this paper, we adopt Baumer’s concept of “Usees” (stakeholders who are non-consenting, unaware and directly targeted by technology), to understand public statements made by eight celebrity women and one non-binary individual targeted with NSII. Celebrities describe harms of being non-consensually targeted by deepfakes and they describe various infrastructural/social factors) which hinder activism and recourse. This work has implications in recognizing the roles of infrastructures underlying deepfake abuse and the potential of human-computer interaction to improve existing recourses for NSII. We also contribute to understanding false victim-blaming rhetoric about deepfake abuse. Future work should involve interventions which challenge values and false beliefs which motivate NSII creation/dissemination.

Much early research on deepfakes has focused on speculating about potential future harms of the technology. Everyday uses and experiences of existing deepfake content are, surprisingly, comparatively understudied. For example, there is little research that seeks to understand how people encounter and respond to deepfake content in today's entertainment media. In this study, we conducted a thematic analysis of user's comments made in response to a Kendrick Lamar YouTube music video that featured deepfake content. In total, we analysed 1467 comments. Our findings are described by six themes; advancing realism of deepfakes, seeing is not believing, predicting dystopian threats, enhancing the experience of the song, strong emotional reactions, and 'a toy for play'. People generally enjoyed the creativity and emotional impact that deepfakes brought to the video but those who were unaware of deepfake technology were left confused and doubting themselves. This study offers a naturalistic view of how deepfakes are encountered in everyday life.

This article presents a qualitative driving simulator study designed to understand the experience of giving up control to automated processes in semiautonomous driving systems. The study employed an experience prototyping methodology, with 12 drivers (4 female) completing 2 sessions in a high-fidelity driving simulator. Condition A simulated a normally functioning car, while Condition B simulated a semiautonomous system that monitors driver behavior and takes evasive action when danger is detected. The simulator experience was used to ground wider discussion of automation and the experience of driving, which was explored through a semistructured interview. Results identify design challenges for autonomous driving systems; the loss of user agency and confidence, and handling the change between manual and automated control. Opportunities were identified; in augmenting rather than removing human abilities, and in providing new learning opportunities for drivers.