The first 100 days: how has COVID-19 affected poor and vulnerable groups in India?

In India, strict public health measures to suppress COVID-19 transmission and reduce burden have been rapidly adopted. Pandemic containment and confinement measures impact societies and economies; their costs and benefits must be assessed holistically. This study provides an evolving portrait of the health, economic and social consequences of the COVID-19 pandemic on vulnerable populations in India. Our analysis focuses on 100 days early in the pandemic from 13 March to 20 June 2020. We developed a conceptual framework based on the human right to health and the UN Sustainable Development Goals (SDGs). We analysed people's experiences recorded and shared via mobile phone on the voice platforms operated by the Gram Vaani COVID-19 response network, a service for rural and low-income populations now being deployed to support India's COVID-19 response. Quantitative and visual methods were used to summarize key features of the data and explore relationships between factors. In its first 100 days, the platform logged over 1.15 million phone calls, of which 793 350 (69%) were outbound calls related largely to health promotion in the context of COVID-19. Analysis of 6636 audio recordings by network users revealed struggles to secure the basic necessities of survival, including food (48%), cash (17%), transportation (10%) and employment or livelihoods (8%). Themes were mapped to shortfalls in 10 SDGs and their associated targets. Pre-existing development deficits and weak social safety nets are driving vulnerability during the COVID-19 crisis. For an effective pandemic response and recovery, these must be addressed through inclusive policy design and institutional reforms.

Experiences with the Introduction of AI-based Tools for Moderation Automation of Voice-based Participatory Media Forum.

Voice-based discussion forums where users can record audio messages which are then published for other users to listen and comment, are often moderated to ensure that the published audios are of good quality, relevant, and adhere to editorial guidelines of the forum. There is room for the introduction of AI-based tools in the moderation process, such as to identify and filter out blank or noisy audios, use speech recognition to transcribe the voice messages in text, and use natural language processing techniques to extract relevant metadata from the audio transcripts. We design such tools and deploy them within a social enterprise working in India that runs several voice-based discussion forums. We present our findings in terms of the time and cost-savings made through the introduction of these tools, and describe the feedback of the moderators towards the acceptability of AI-based automation in their workflow. Our work forms a case-study in the use of AI for automation of several routine tasks, and can be especially relevant for other researchers and practitioners involved with the use of voice-based technologies in developing regions of the world.