Underserved communities face critical health challenges due to lack of access to timely and reliable information. Nongovernmental organizations are leveraging the widespread use of cellphones to combat these healthcare challenges and spread preventative awareness. The health workers at these organizations reach out individually to beneficiaries; however such programs still suffer from declining engagement. We have deployed SAHELI, a system to efficiently utilize the limited availability of health workers for improving maternal and child health in India. SAHELI uses the Restless Multiarmed Bandit (RMAB) framework to identify beneficiaries for outreach. It is the first deployed application for RMABs in public health, and is already in continuous use by our partner NGO, ARMMAN. We have already reached ~100K beneficiaries with SAHELI, and are on track to serve 1 million beneficiaries by the end of 2023. This scale and impact has been achieved through multiple innovations in the RMAB model and its development, in preparation of real world data, and in deployment practices; and through careful consideration of responsible AI practices. Specifically, in this paper, we describe our approach to learn from past data to improve the performance of SAHELI’s RMAB model, the real-world challenges faced during deployment and adoption of SAHELI, and the end-to-end pipeline.

Artificial Intelligence (AI) based technologies are increasingly being integrated into public sector programs to help with decision-support and effective distribution of constrained resources. The field of Computer Supported Cooperative Work (CSCW) has begun to examine how the resultant sociotechnical systems may be designed appropriately when targeting underserved populations. We present an ethnographic study of a large-scale real-world integration of an AI system for resource allocation in a call-based maternal and child health program in India. Our findings uncover complexities around determining who benefits from the intervention, how the human-AI collaboration is managed, when intervention must take place in alignment with various priorities, and why the AI is sought, for what purpose. Our paper offers takeaways for human-centered AI integration in public health, drawing attention to the work done by the AI as actor, the work of configuring the human-AI partnership with multiple diverse stakeholders, and the work of aligning program goals for design and implementation through continual dialogue across stakeholders.

With increasing world population and expanded use of forests as cohabited regions, interactions and conflicts with wildlife are increasing, leading to large scale loss of lives (animal and human) and livelihoods (economic). While community knowledge is valuable, forest officials and conservation organisations can greatly benefit from predictive analysis of human-wildlife conflict, leading to targeted interventions that can potentially help save lives and livelihoods. However, the problem of prediction is a complex socio-technical problem in the context of limited data in low-resource regions. Identifying the right features to make accurate predictions of conflicts at the required spatial granularity using a sparse conflict training dataset is the key challenge that we address in this paper. Specifically, we do an illustrative case study on human-wildlife conflicts in the Bramhapuri Forest Division in Chandrapur, Maharashtra, India. Most existing work has considered human wildlife conflicts in protected areas and to the best of our knowledge, this is the first effort at prediction of human-wildlife conflicts in unprotected areas and using those predictions for deploying interventions on the ground.

Data-driven approaches that form the foundation of advancements in machine learning (ML) are powered in large part by human infrastructures that enable the collection of large datasets. We study the movement of data through multiple stages of data processing in the context of public health in India, examining the data work performed by frontline health workers, data stewards, and ML developers. We conducted interviews with these stakeholders to understand their varied perspectives on valuing data across stages, working with data to attain this value, and challenges arising throughout. We discuss the tensions in valuing and how they might be addressed, as we emphasize the need for improved transparency and accountability when data are transformed from one stage of processing to the next.

The future of work is speculated to undergo profound change with increased automation. Predictable jobs are projected to face high susceptibility to technological developments. Many economies in Global South are built around outsourcing and manual labour, facing a risk of job insecurity. In this paper, we examine the perceptions and practices around automated futures of work among a population that is highly vulnerable to algorithms and robots entering rule-based and manual domains: vocational technicians. We present results from participatory action research with 38 vocational technician students of low socio-economic status in Bangalore, India. Our findings show that technicians were unfamiliar with the growth of automation, but upon learning about it, articulated an emic vision for a future of work in-line with their value systems. Participants felt excluded by current technological platforms for skilling and job-seeking. We present opportunities for technology industry and policy makers to build a future of work for vulnerable communities.

In India, women represent 45% of total computer science enrollment in universities, almost three times the rate in the United States, where it is 17%. At the same time, women make up an estimated 25-30% of the HCI community in India, half the rate in the U.S. We investigate the complexities of these surprising phenomena through qualitative research of Indian computer science and human-computer interaction researchers and professionals at various life stages. We find among other things that Indian familial norms play a significant role in pressuring young women into computing as a field; that familial pressures and workplace discrimination then cause a precipitous exit of women from computing at the onset of marriage; and that HCI occupies an interstitial space between art and technology that affects women's careers. Our findings underscore the societal influence on women's representation in the tech sector and invite further participation by the HCI community in related questions.