Generative AI is rapidly diffusing worldwide, yet access remains uneven. In informal settlements, barriers of cost, literacy and connectivity can exclude residents from AI-enabled self-expression. This paper presents Street Scenes: a public appliance for walk-up interaction with generative AI video in Dharavi, Mumbai. Inspired by the “Hole in the Wall” computers and previous Dharavi speech deployments, the system lets passers-by capture phone images, add voice-, button- and dial-based prompts, and generate short videos to view and leave locally. We report on ideation workshops, two Wizard-of-Oz prototypes and a 13-day in-situ deployment across Dharavi street locations. Findings show residents appropriating AI for play, self-presentation, small business promotion and community messaging, while also raising concerns about privacy, trust and misuse. We contribute: (1) a model for public AI appliances; (2) empirical insights into community engagement with generative AI; and, (3) design lessons for accessible, equitable and community-governed AI systems.

We report on community-centered, collaborative research that weaves together HCI, natural language processing, linguistic, and design insights to develop spoken language technologies for unwritten languages. Across three visits to a Banjara farming community in India, we use participatory, technical, and creative methods to engage community members, collect spoken language photo annotations, and develop an information retrieval (IR) system. Drawing on orality theory, we interrogate assumptions and biases of current speech interfaces and create a simple application that leverages our IR system to match fluidly spoken queries with recorded annotations and surface corresponding photos. In-situ evaluations show how our novel approach returns reliable results and inspired the co-creation of media retrieval use-cases that are more appropriate in oral contexts. The very low (< 4h) spoken data requirements makes our approach adaptable to other contexts where languages are unwritten or have no digital language resources available.

What do pedestrian crossings, ATMs, elevators and ticket machines have in common? These are just a few of the ubiquitous yet essential elements of public-space infrastructure that rely on physical buttons or touchscreens; common interactions that, until recently, were considered perfectly safe to perform. This work investigates how we might integrate touchless technologies into public-space infrastructure in order to minimise physical interaction with shared devices in light of the ongoing COVID-19 pandemic. Drawing on an ethnographic exploration into how public utilities are being used, adapted or avoided, we developed and evaluated a suite of technology probes that can be either retrofitted into, or replace, these services. In-situ community deployments of our probes demonstrate strong uptake and provide insight into how hands-free technologies can be adapted and utilised for the public domain; and, in turn, used to inform the future of walk-up-and use public technologies.

Automatic Speech Recognition (ASR) researchers are turning their attention towards supporting low-resource languages, such as isiXhosa or Marathi, with only limited training resources. We report and reflect on collaborative research across ASR & HCI to situate ASR-enabled technologies to suit the needs and functions of two communities of low-resource language speakers, on the outskirts of Cape Town, South Africa and in Mumbai, India. We build on longstanding community partnerships and draw on linguistics, media studies and HCI scholarship to guide our research. We demonstrate diverse design methods to: remotely engage participants; collect speech data to test ASR models; and ultimately field-test models with users. Reflecting on the research, we identify opportunities, challenges, and use-cases of ASR, in particular to support pervasive use of WhatsApp voice messaging. Finally, we uncover implications for collaborations across ASR & HCI that advance important discussions at CHI surrounding data, ethics, and AI.

Many of us daily encounter shadow and reflected light patterns alongside macro-level changes in ambient light levels. These are caused by elements—opaque objects, glass, mirrors, even clouds—in our environment interfacing with sunlight or artificial indoor lighting. Inspired by these phenomena, we explored ways of creating digitally-supported displays that use light, shade and reflection for output and harness the energy they need to operate from the sun or indoor ambient light. Through a set of design workshops we developed exemplar devices: SolarPix, ShadMo and GlowBoard. We detail their function and implementation, as well as evidencing their technical viability. The designs were informed by material understandings from the Global North and Global South and demonstrated in a cross-cultural workshop run in parallel in India and South Africa where community co-designers reflected on their uses and value given lived experience of their communication practices and unreliable energy networks.

Working with emergent users in two of Mumbai’s slums, we explored the value and uses of photovoltaic (PV) self-powering digital materials. Through a series of co-design workshops, a diary study and responses by artists and craftspeople, we developed the PV-Pix concept for inter-home connections. Each PV-Pix element consists of a deformable energy harvesting material that, when actuated by a person in one home, changes its physical state both there and in a connected home. To explore the concept we considered two forms of PV-Pix: one uses rigid materials and the other flexible ones. We deployed two low-fidelity prototypes, each constructed of a grid of one PV-Pix type, in four slum homes over a four week period to further understand the usability and uses of the materials, eliciting interesting inter-family communication practices. Encouraged by these results we report on a first-step towards working prototypes and demonstrate the technical viability of the approach.

There is widespread concern over the ways speech assistant providers currently use humans to listen to users' queries without their knowledge. We report two iterations of the TalkBack smart speaker, which transparently combines machine and human assistance. In the first, we created a prototype to investigate whether people would choose to forward their questions to a human answerer if the machine was unable to help. Longitudinal deployment revealed that most users would do so when given the explicit choice. In the second iteration we extended the prototype to draw upon spoken answers from previous deployments, combining machine efficiency with human richness. Deployment of this second iteration shows that this corpus can help provide relevant, human-created instant responses. We distil lessons learned for those developing conversational agents or other AI-infused systems about how to appropriately enlist human-in-the-loop information services to benefit users, task workers and system performance.

Mobile and ubiquitous computing researchers have long envisioned future worlds for users in developed regions. Steered by such visions, they have innovated devices and services exploring the value of alternative propositions with and for individuals, groups and communities. Meanwhile, such radical and long-term explorations are uncommon for what have been termed emergent users; users, that is, for whom advanced technologies are just within grasp. Rather, a driving assumption is that today’s high-end mobile technologies will “trickle down” to these user groups in due course. In this paper, we open the debate about what mobile technologies might be like if emergent users were directly involved in creating their visions for the future 5–10 years from now. To do this, we report on a set of envisioning workshops in India, South Africa and Kenya that provide a roadmap for valued, effective devices and services for these regions in the next decade.