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This practical guide from the UK Evaluation Society explores how evaluators can use AI tools responsibly and transparently across all stages of the evaluation process.
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Earlier this year we spoke to 102 leaders from across the globe to ask: Ten years from now, what will we regret not having done today? This framing turned out to be a powerful device for collective horizon scanning and foresight. Across every conversation there was a sense that this moment demands more from us all, along with a strong eagerness to explore what we can own and act on. More than that, these discussions surfaced direction, hope and a way forward. From CEOs, professors, PhDs,...
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This paper shares an insider's perspective on the spread of Thinking and Working Politically (TWP) across the US Agency for International Development (USAID), highlighting lessons learned along the way that are relevant even beyond the dissolution of the Agency in 2025.
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The CGIAR 2030 Research and Innovation Strategy commits organizational change with seven ways of working, including “Making the digital revolution central to our way of working”. In that context, Artificial Intelligence (AI), introduces both opportunities and risks to evaluation practice. Guided by the CGIAR-wide Evaluation Framework, integrating AI tools requires a governance approach to balance innovation with ethical responsibility, ensuring transparency, fairness, accountability, and...
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Today’s critical challenges are complex and require system level change. Issues like ending poverty, reversing biodiversity collapse, creating more equitable and sustainable food systems, or ending health inequalities, will not respond to neat siloed technical interventions. These are not problems we can tackle head on. Rather, we need to change the conditions (systems) that create these problems. Consequently, many of the most ambitious philanthropic foundations are adopting ‘systems...
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One of the main questions evaluators have when using AI is: "How can I know if it performs well?" To answer this question, we have developed a guidance note aimed at capturing what we have learned so far about integrating AI into evaluation and to offer a framework for further exploration. Since we began our experiments with Large Language Models (LLMs) in the Spring of 2023, we have made significant progress in using Generative Artificial Intelligence (GenAI) for processing and analyzing...
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Process tracing (PT) involves a detailed analysis of the processes that link interventions to outcomes. PT is particularly useful for evaluating interventions that are difficult to quantify, such as knowledge work or institution building. It involves creating a detailed causal theory, tracking the process theory of change (pToC) by examining the observable evidence, and learning general lessons from the cases studied. PT offers two main benefits to evaluators: first, it provides a clear way...
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Generative AI (GenAI) is evolving rapidly, and its use in violence against women research remains new and largely experimental. While researchers are beginning to explore GenAI’s potential, uncertainty around its risks, ethical challenges, and real-world application persists. Recognising the need for clear guidance, the Sexual Violence Research Initiative (SVRI) partnered with The MERL Tech Initiative (MTI) to develop this resource. This guide aims to provide background on GenAI, strengthen...
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