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Using Data and AI for Social Good- A forum associated with data and artificial intelligence

News Update/Press Release

Using Data and AI for Social Good- A forum associated with data and artificial intelligence

News Update/Press Release

Using Data and AI for Social Good- A forum associated with data and artificial intelligence

 

Delhi, 27th December, 2021: Arthan, a social enterprise, organized the second forum in the series ‘Using Data and AI for Social Good’. The final forum in the series was held last week and encompassed a wider variety of themes associated with data and artificial intelligence (AI) such as implementing data and AI-based solutions at the NGO level and overcoming challenges. Various industry experts came together to discuss and delve into the impact of data and AI and how it can shape the social sector.

The forum delved deeper to encompass pertinent themes like the definition of “good” data, its collection and management, steps to be taken by organizations to make data more driven, the process and trials of building AI-based solutions, good and responsible practices to using data and AI and much more. A successful roundtable was organized earlier in the month of September which revolved around the uses of AI and challenges associated with its implementation.

Some experts who were a part of the forum included Prof. Jasjit Singh, Professor of Strategy (INSEAD), Gaurav Sharma, Lead Advisor-AI (GIZ India), Dolly Agarwal, Technology Consultant (Pratham Education Foundation), and Subhashish Bhadra, Principal (Omidyar Network India).  The discussion threw light on some crucial themes which included pioneering and low-cost practices that could be scaled to use AI and optimize the work at the ground level.

Speaking in the session about data and AI implementation, Akanksha Sharma, Global ESG Head (Sterlite Technologies Limited), said, “Not many organizations, especially those at the grassroots level, are equipped enough or market-ready for these solutions to be implemented and this creates a digital divide.”  The forum also highlighted the scalability perspective, the significance of the quality of data, and how to make data an enabler in assisting in solution-building processes. The private sector can play a big role in addressing this gap and facilitating digital inclusion.

Raghu Dharmaraju, President (ARTPARK) who has spent a lifetime in building innovative and impact-driven solutions, moderated the session. He asked some key questions around responsible practices to be undertaken for AI implementation, use-cases, and problems that could be solved using AI, understanding the definition of “good data”, challenges to bring data together, and to understand the concept of data as a public good.

The session then moved on to the multiple factors that need to be taken care of like the digital divide and other such considerations while building AI-based solutions. It highlighted how there is a need to have programs in regional Indian languages but most of the models are built-in foreign languages as well as challenges that need to be dealt with from an impact assessment lens. The discussion then took a deep dive into the reality of AI and the sort of use cases that are a good fit to solve the issue of the appropriate application of AI. 

The speakers also talked about ethical and responsible uses of data and AI and that there are a lot of moral questions around using data. For instance, were people aware of the collection of data in cases of personal data sets, do they have access to verify/rectify data or take back the data they provided?

In this respect, good governance and a grievance redressal system become crucial through which people can voice their grievances. AI and data protection and use cases in India are really important. It is imperative to open source the data and this can enable researchers to build solutions that can be helpful.

There is a need for ‘hand-holding’ between technologists, domain experts, and linguists to work on certain structures and we need SOPs in place, an AI development life-cycle to work towards a particular solution so as to minimize the bias and improve transparency.

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