Artificial intelligence (AI) applications can profoundly affect society. Recently, there has been extensive interest in studying how scientists design AI systems for general tasks. However, it remains an open question as to whether the AI systems developed in this way can work as expected in different regional contexts while simultaneously empowering local people. How can scientists co-create AI systems with local communities to address regional concerns? This article contributes new perspectives in this underexplored direction at the intersection of data science, AI, citizen science, and human-computer interaction. Through case studies, we discuss challenges in co-designing AI systems with local people, collecting and explaining community data using AI, and adapting AI systems to long-term social change. We also consolidate insights into bridging AI research and citizen needs, including evaluating the social impact of AI, curating community datasets for AI development, and building AI pipelines to explain data patterns to laypeople.

Empowering communities through citizen science.

The EmpathiCH Workshop aims to blend a diverse set of expertise to expand upon the nascent field of Empathy-Centric Design. Building on the discussions in previous editions of the workshop, this iteration invites contributions which scrutinize the use of empathy as a design principle in digital interfaces. We encourage inquiry in a number of research dimensions: examining the multifaceted nature of empathy; establishing both the requirements and shortcomings of empathy in design research; discussing key post-human stakeholders in digital interfaces (social groups, causes, digital avatars, artificial agents etc.); and expanding the scope of empathy research beyond preliminary perspective-taking. The workshop, structured as a combination of author panels, expert discussion, and interactive activities, provides the ideal venue to foster a critical discussion on the nature of the suitability of empathy in digital design, especially in the rapidly approaching context of its role in post-humanist HCI.

The use of Computer Vision, through a Perceptive Visual Urban Analytics (VUA) paradigm, has been proposed as a way for municipalities to more easily monitor their cities. However, prior studies fall short of actually investigating whether Perceptive VUA is ready for municipal use. In this paper we take a critical look at this paradigm by comparing key methods and evaluating them on usability and trustworthiness with municipal experts as well as Responsible AI and Computer Vision researchers. Based on on this evaluation we find that Perceptive VUA is not (yet) ready for municipal use as they do not incorporate domain knowledge and overly rely on spurious correlations. We conclude by providing recommendations for how to progress Perceptive VUA such that it may actually contribute to improving the liveability and quality of urban environments.

Digitally-supported participatory methods are often used in policy-making to develop inclusive policies by collecting and integrating citizen's opinions. However, these methods fail to capture the complexity and nuances in citizen's needs, i.e., citizens are generally unaware of other's needs, perspectives, and experiences. Consequently, policies developed with this underlying gap tend to overlook the alignment of multistakeholder perspectives, and design policies based on the optimization of high-level demographic features. In our contribution, we propose a method to enable citizens understand other's perspectives and calibrate their positions. First, we collected requirements and design principles to develop our approach by involving stakeholders and experts in policymaking in a series of workshops. Then, we conducted a crowdsourcing study with 420 participants to compare the effect of different text and images, on people's initial and final motivations and their willingness to change opinions. We observed that both influence participant's opinion change, however, the effect is more pronounced for textual modality. Finally, we discuss overarching implications of designing with empathy to mediate alignment of citizen's perspectives.