Divy Thakkar
I am an HCI researcher with a focus on Human-AI interactions in low-resource communities. My prior research has examined AI for Social Good for marginalised communities, Future of Work, Algorithmic Fairness of education/ job Platforms and the inequitable computing scenario in India.
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Public Health Calls for/with AI: An Ethnographic Perspective
Azra Ismail
Neha Kumar
Neha Madhiwalla
ACM Conference On Computer-Supported Cooperative Work And Social Computing (2023)
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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 largescale 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.
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Deployed SAHELI: Field Optimization of Intelligent RMAB for Maternal and Child Care
Shresth Verma
Aditya S. Mate
Paritosh Verma
Sruthi Gorantala
Neha Madhiwalla
Aparna Hegde
Manish Jain
Innovative Applications of Artificial Intelligence (IAAI) (2023) (to appear)
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Underserved communities face critical health challenges due to lack of access to timely and reliable information. Non-governmental 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 ∼ 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
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Facilitating Human-Wildlife Cohabitation through Conflict Prediction
Susobhan Ghosh
Pradeep Varakantham
Aniket Bhatkhande
Tamanna Ahmad
Anish Andheria
Wenjun Li
IAAI Technical Track on Emerging Applications of AI (2022)
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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.
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When is ML data good?: Valuing in Public Health Datafication
Azra Ismail
Pratyush Kumar
Alex Hanna
Nithya Sambasivan
Neha Kumar
CHI 2022 (2022) (to appear)
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Data-driven approaches that form the foundation of advancements in artificial intelligence (AI) and machine learning (ML) are powered in large part by human infrastructures that enable the collection of large datasets. We examine the movement of data through multiple stages of data collection in the context of public health in India, where the data workers include frontline health workers, data stewards, and AI/ML developers. We conducted interviews with these stakeholders to understand how they value data differently at each stage, how data are worked upon to attain this value, as well as the challenges that arise in the process. Our work uncovers the tensions in valuing across stakeholders, and lays out implications for work of ML datasets. We discuss how these tensions arise and how they might be addressed, and the need for better transparency and accountability as data is transformed from one stage to the next.
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Measuring Data Collection Diligence for Community Healthcare
Ramesha Karunasena
Md Sarfrazul Ambiya
Arunesh Sinha
Ruchit Nagar
Dhyanesh Narayanan
ACM conference on Equity and Access in Algorithms, Mechanisms, and Optimization 2021 (2021)
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Data analytics has tremendous potential to provide targeted benefit in low-resource communities, however the availability of highquality public health data is a significant challenge in developing countries primarily due to non-diligent data collection by community health workers (CHWs). Our use of the word non-diligence here is to emphasize that poor data collection is often not a deliberate
action by CHW but arises due to a myriad of factors, sometime beyond the control of the CHW. In this work, we define and test a data collection diligence score. This challenging unlabeled data problem is handled by building upon domain expert’s guidance to design a useful data representation of the raw data, using which we design a simple and natural score. An important aspect of the
score is relative scoring of the CHWs, which implicitly takes into account the context of the local area. The data is also clustered and interpreting these clusters provides a natural explanation of the past behavior of each data collector. We further predict the diligence score for future time steps. Our framework has been validated on the ground using observations by the field monitors of our partner
NGO in India. Beyond the successful field test, our work is in the final stages of deployment in the state of Rajasthan, India. This system will be helpful in providing non-punitive intervention and necessary guidance to encourage CHWs.
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Vocational technicians are a critical labour force transitioning from high school education to skilled technicians, specialising in fields like data entry operations, electrical wiring, and welding. An estimated 1.5 million students are enrolled in over 13,000 vocational training institutes in India, and the national government has further plans to train 500 million Indians by 2022 [3]. India, like many countries of the Global South, has a large youth demographic dividend--over half the population is below 25 years of age. Workers enter the vocational trade to fulfil their economic and social aspirations, often coming from oppressed caste and class backgrounds. Despite the heavy investment and growth in the vocational labour force, impacts of automation and economic downturn may likely significantly affect these technicians, due to the predictable, repetitive, and frequently mechanistic nature of their jobs. Across the Global South, entire industries built around rule-based jobs like call centres, technology outsourcing, and low-level factory jobs could face the risk of job destabilisation from automation and global catastrophes.
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Towards an AI-Powered Future that Works for Vocational Workers
Neha Kumar
Nithya Sambasivan
Conference on Human Factors in Computing Systems, Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (2020)
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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
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The Unexpected Entry and Exodus of Women in Computing and HCI in India
Nithya Sambasivan*
Purva Yardi
Pratap Kalenahalli Sudarshan
Kentaro Toyama
SIGCHI, ACM (2018)
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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, almost 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, from undergraduates to senior scientists. We find that cultural norms exert a powerful force on the representation of women in the tech sector, which is expressed in India as a societal whiplash in which women are encouraged to go into computing as students, but then expected to exit soon after they enter the tech workforce. Specifically, 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.
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