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Key Points • The authors identified four archetypes describing philanthropic funders’ approach to AI: The Curious, The Doers, The Dreamers, The Skeptics. • The authors did not find major differences across foundations based on their geographic location, though there were differences found based on their mission and values. Feminist and social justice funders in the Global South demonstrated more skepticism than others. • Even in seemingly benign or straightforward applications, AI systems...
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Large publicly funded programmes of research continue to receive increased investment as interventions aiming to produce impact for the world’s poorest and most marginalized populations. At this intersection of research and development, research is expected to contribute to complex processes of societal change. Embracing a co-produced view of impact as emerging along uncertain causal pathways often without predefined outcomes calls for innovation in the use of complexity-aware approaches to...
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Large language models (LLMs) are a type of generative artificial intelligence (AI) designed to produce text-based content. LLMs use deep learning techniques and massively large data sets to understand, summarize, generate, and predict new text. LLMs caught the public eye in early 2023 when ChatGPT (the first consumer facing LLM) was released. LLM technologies are driven by recent advances in deep-learning AI techniques, where language models are trained on extremely large text data from the...
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This paper probes trends in the use of big data by a community of early adopters working in monitoring, evaluation, research, and learning (MERL) in the development and humanitarian sectors. Qualitative analysis was conducted on data from MERL Tech conference records and key informant interviews. Findings indicate that MERL practitioners are in a fragmented, experimental phase, with use and application of big data varying widely, accompanied by shifting terminologies. We take an in-depth...
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Achieving impact through research for development programmes (R4D) requires engagement with diverse stakeholders across the research, development and policy divides. Understanding how such programmes support the emergence of outcomes, therefore, requires a focus on the relational aspects of engagement and collaboration. Increasingly, evaluation of large research collaborations is employing social network analysis (SNA), making use of its relational view of causation. In this paper, we use...
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