Divy Thakkar’s research while affiliated with Google Inc. and other places

Generative AI models as a design material offer a dynamic operational scope with support for varied functionality while constraining the degree of freedom software teams have in working with pre-trained models. In this work, we investigate how collaborative software teams prototype generative AI applications by engaging in prompt engineering. Unlike end-users crafting their own prompts for bespoke tasks, teams prototyping applications need to carefully align the prompt instructions with human-centered values while at the same time ensuring they support diverse users and contexts. By conducting a design study with 39 practitioners, we identified a content-centric prototyping approach. In working with content as a design material, collaborative teams applied different strategies to design and evaluate the prompts. We also identified potential challenges and pitfalls due to the high sensitivity of generative models to prompts and overreliance on example content. Finally, we contrast content-centric prototyping with prior work on human-AI design and highlight considerations for collaborative teams working on generative AI applications.

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 Multi‐armed 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 ∼130K beneficiaries with saheli, and are on track to serve one 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.

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.

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.

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.

India accounts for 12% of maternal deaths and 16% of child deaths globally. Lack of access to preventive care information is a major contributing factor for these deaths, especially in low-income households. We work with ARMMAN, a non-profit based in India, to further the use of call-based information programs by early-on identifying women who might not engage with these programs that are proven to affect health parameters positively. We analyzed anonymized call-records of over 300,000 women registered in an awareness program created by ARMMAN that uses cellphone calls to regularly disseminate health related information. We built machine learning based models to predict the long term engagement pattern from call logs and beneficiaries' demographic information, and discuss the applicability of this method in the real world through a pilot validation. Through a randomized controlled trial, we show that using our model's predictions to make interventions boosts engagement metrics by 14.3%. We then formulate the intervention planning problem as restless multi-armed bandits (RMABs), and present preliminary results using this approach.

Machine learning has tremendous potential to provide targeted interventions in low-resource communities, however the availability of high-quality public health data is a significant challenge. In this work, we partner with field experts at an non-governmental organization (NGO) in India to define and test a data collection quality score for each health worker who collects data. This challenging unlabeled data problem is handled by building upon domain-expert's guidance to design a useful data representation that is then clustered to infer a data quality score. We also provide a more interpretable version of the score. These scores already provide for a measurement of data collection quality; in addition, we also predict the quality for future time steps and find our results to be very accurate. Our work was successfully field tested and is in the final stages of deployment in Rajasthan, India.