This paper examines the potential for localized adaptation, appropriation and re-imagination of AI for non-western cultural expression, using the Persian Gulf as a case. Using sociologist Howard Becker’s concept of 'art worlds' as a situated lens to evaluate generative AI, we set up an eight week experimentation and dialogue between artists, art historians and curators. Our project reveals how local art worlds 1) can appropriate AI tools to address contextual and cultural needs; 2) develop "hacks'' to adapt AI for culturally-specific capabilities; and 3) can be a site for imagining alternative technological trajectories. We thus showcase the importance of expanding the scope of AI evaluations to include the social dynamics AI operates in and its contexts of use. We also reflect on the power that local communities may have to interrupt AI with more culturally-relevant orientations and to offer visions for redesigning AI for non-Western creativity.
The rise of generative AI has brought a host of challenges for historically marginalized groups, including increased surveillance, AI-mediated racism, and algorithmic inequity. While stakeholders emphasize ethical and responsible AI that is safe, anti-discriminatory, and "protects human dignity", the centrality of anti-Blackness in the design, development, and deployment of AI systems coupled with race-evasive approaches to defining and advancing ethical, equitable, and ‘human-centered’ technologies have exacerbated racial oppression. We present three case studies of speculative technologies designed by Black youth in a college bridge, summer course that examine ethical and responsible AI in their everyday lives. From a bottom-up approach, we infringe upon this broader discourse to provide an initial grounding of responsible and ethical AI as well as discuss the criticality of Black, historically anchored, culturally-situated lenses to offer justice-oriented design principles that can guide the teaching, learning, and design of technology.
Generative text-to-image models are disrupting the lives of creative professionals. Specifically, illustrators are threatened by models that claim to extract and reproduce their style. Yet, research on style transfer has rarely focused on their perspectives. We provided four illustrators with a model fine-tuned to their style and conducted semi-structured interviews about the model’s successes, limitations, and potential uses. Evaluating their output, artists reported that style transfer successfully copies aesthetic fragments but is limited by content-style disentanglement and lacks the crucial emergent quality of their style. They also deemed the others’ copies more successful. Understanding the results of style transfer as “boundary objects,” we analyze how they can simultaneously be considered unsuccessful by artists and poised to replace their work by others. We connect our findings to critical HCI frameworks, demonstrating that style transfer, rather than merely a Creativity Support Tool, should also be understood as a supply chain optimization one.
Despite the importance of AI literacy for both children and adults, adults have been understudied. We developed short videos for adults that provided training on the basics of AI understanding, use, and evaluation. In an online experiment, 94 adults aged 30-49 were randomly assigned in a 1:2 ratio to view either short videos on AI history (control group) or AI literacy training videos (treatment group). The results showed that the intervention significantly improved people’s self-efficacy of AI use but not in AI understanding or evaluation. Interestingly, participants’ fears of AI bias, privacy violations, and job replacement increased after the training, although they remained below the midpoints. We argue that the heightened fear in the treatment group reflects a foundation for critical thinking skills, as it moves them closer to a more calibrated, moderate level of fear. Therefore, this study uniquely contributes by utilizing short-form experiential content to both educate and foster a more informed, critical interaction with AI technologies. The implications of designing AI literacy educational materials for adults were discussed.
The energy cost of developing and deploying Generative AI (GenAI) models has exploded with their mass adoption, as has the ensuing carbon emissions. The climate impact of this is currently unknown. In Human-Computer Interaction, GenAI models are rarely trained but often used. Based on detailed review of 282 papers, we estimate this footprint from energy consumption of the total use of GenAI for CHI 2024 research as between 10,769.63 and 10,925.12 kg CO2e — equal to driving a car for more than 100,000 km. We
show that in CHI research, GenAI is most often used for Prototyping, Evaluation & User studies, and that Data Collection and Fine-tuning models incurs the highest CO2st. We find that CHI submissions are unlikely to report GenAI use transparently, which makes precise calculations difficult. By measuring the usage of a subset of the papers on local hardware, we obtain estimations of the energy consumption and carbon footprint. Based on this evidence, we discuss and demonstrate ways to mitigate the issues of GenAI carbon footprint and lack of transparency.
Conversational artificial intelligence (CAI), which replicates human-to-human interaction as human-to-machine, is increasingly developed to address insufficient access to healthcare. In this paper, we use design fiction methods to speculate on ethical consequences of CAI that offers emotional support to complement or replace mental healthcare. Through a near-future news article about a fictional, failed CAI, we explore safety and privacy concerns associated with mismatches between what an emotional support CAI is advertised to do, what it technically can do, and how it is likely to be used. We pose the following questions to researchers, regulators, and developers: How might we jointly and effectively address the anticipatable safety and privacy risks that emotional support CAI pose, including formalizing ethical speculation processes? What streamlined and practically feasible measures can efficiently account for the most dangerous harms? How might differing stakeholder expectations about the CAI be bridged? Finally, in what scenarios is the decision not to design a CAI tool the most ethical or safest option? Content advisement: Contains discussion of disordered eating behaviors and intimate partner violence.
Play is pivotal in fostering the emotional, social, and cultural dimensions of urban spaces. While generative AI (GAI) potentially supports playful urban interaction, a balanced and critical approach to the design opportunities and challenges is needed. This work develops iWonder, an image-to-image GAI tool engaging fourteen designers in urban explorations to identify GAI's playful features and create design ideas. Fourteen citizens then evaluated these ideas, providing expectations and critical concerns from a bottom-up perspective. Our findings reveal the dynamic interplay between users, GAI, and urban contexts, highlighting GAI's potential to facilitate playful urban experiences through generative agency, meaningful unpredictability, social performativity, and the associated offensive qualities. We propose design considerations to address citizen concerns and the `tourist metaphor' to deepen our understanding of GAI's impacts, offering insights to enhance cities' socio-cultural fabric. Overall, this research contributes to the effort to harness GAI's capabilities for urban enrichment.