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On 28 September 2026, in observance of the International Day for Universal Access to Information, this Future of Evaluation dialogue discussed how evaluation can make evidence more accessible, understandable and actionable for citizens. Access to information is fundamental to transparency, accountability and meaningful participation. Yet, evaluation evidence tends to remain within technical reports, institutional platforms and specialist audiences, limiting its potential as a public good. The dialogue examined strategies for extending evidence to citizens, highlighting practical approaches across knowledge translation, storytelling, open data and artificial intelligence (AI) driven innovations. In case you missed the conversation, watch the recording. Seven takeaways from the dialogue Making evidence public is only the first step towards making it accessible. A report can be public and still out of reach for citizens. It may be written in technical language, available only online, or leave citizens unsure what the findings mean for them and what should change as a result. Access can be better judged by whether people can find evidence, understand it and act on it. The number of reports published says little about whether an evaluation reached its audience, while tracking who reached the evidence and which decisions it informed says more about the success of an evaluation. Accessibility depends on language, format and where evidence is placed. Most evaluation reports are out of reach for the communities served by the programmes. The reports can be long, dense with technical and statistical jargon, often published in one language, and posted on institutional websites that few citizens visit. Making evidence accessible means providing short summaries in plain language, translations into local languages and using formats like infographics, short videos, radio and audio. It also means going where people already gather, like community meetings, local radio, youth groups, or community councils. However, simplification needs care—over-simplifying evaluation results risks distorting the evidence, requiring evaluators to strike a thoughtful balance between accessibility and accuracy. Recognize communities as knowledge partners rather than data sources. Evidence is gathered from communities, analysed in evaluation offices, discussed at conferences and stored in reports. The evidence rarely returns to the communities who provided it. Involving communities in defining evaluation priorities, setting criteria, and interpreting findings leads to evidence that reflects local priorities, draws on contextual knowledge, and drives meaningful action. Build evaluation literacy across the ecosystem. Evidence cannot be utilized to its full extent when the people receiving it are not equipped to interpret it. Evaluation literacy, meaning the ability to read evidence critically, weigh and apply it to decision-making, should be developed across the full development ecosystem rather than within evaluation offices alone. This includes programme staff, government officials, civil society organizations, academia and think tanks. Local government deserves particular attention, since it is closest to communities and often the channel through which evidence reaches them. The barriers to accessibility are institutional and political, not only technical. Evaluation is often seen as technocratic, with questions, criteria and methods set by technical specialists and funders, leaving little room for the affected communities to shape any of it. Institutional discomfort with poor performance also restricts what gets shared, and political sensitivities can further narrow access. Multi-country and meta-level evaluations add a different barrier, surfacing national and regional findings that override local context and leaving people unable to recognize their own situation in the evaluation. There is no single approach to sharing evaluation evidence that fits every setting and audience. AI can bring evidence to more people, but it cannot replace evaluators' judgement. AI offers evaluation an opportunity to make evidence available at scale, such as condensing a hundred-page report into a one-page summary, translating it into local languages, and presenting the findings in whichever format an audience prefers, from audio to a visual scorecard. AI can also help draw lessons across large numbers of evaluations far faster than manual review. The risks arise when AI is used to analyse and interpret evidence without proper human oversight. Entering sensitive evaluation data into these tools also raises privacy concerns. AI can also reproduce epistemic bias, marginalize local languages and knowledge, and create data-governance risks. If not used appropriately, it may also shift control over evidence away from communities. AI should therefore remain an assistive tool, not an evaluator or decision-maker. Close the feedback loop, provide equitable access and safeguard youth in evaluation. The Youth in Evaluation champion from UNICEF highlighted the organization's experience in engaging young people in evaluation processes. While young people are often consulted in evaluations through advisory groups or digital polling, they rarely receive updates on how their contributions were used. Meaningful youth engagement requires clear purpose and defined feedback loops. Furthermore, while digital channels expand reach, they risk excluding youth without access to the internet and digital platforms. Finally, in some contexts, contributing to an evaluation can expose young people to risk, so their protection should be built into the evaluation design. The Eval4Action Future of Evaluation dialogues are a series of forward-looking discussions that explore innovative and adaptive approaches to evaluation. Designed to make evaluation more influential in a rapidly changing and complex world, these dialogues bring together a diverse range of voices—from experts to young evaluators—to share knowledge and highlight ways to future-proof the field of evaluation. Each monthly dialogue is aligned with an international action day, ensuring the conversations are timely and relevant to a global discourse. This was the third dialogue of Season 2. The fourth dialogue will take place on 15 October 2026 in observance of the International Day for the Eradication of Poverty. Register. This article was written with AI support with human authors in the lead.

By Nea-Mari Heinonen Ministry for Foreign Affairs of Finland Artificial intelligence (AI) is increasingly presented as a solution to a wide range of organisational challenges in public administrations and multilateral organisations. Across sectors, there is growing pressure to adopt AI, often accompanied by assumptions that it will automatically improve efficiency. For the evaluation community, this raises new questions, such as: How do we prevent the emergence of a Wild West of AI use that can lead to pseudo-evaluation and pseudo-evidence? Figuratively, ‘the Wild West’ refers to “something that is chaotic and uncontrolled especially due to a lack of regulation or oversight”. Using this metaphor, this blog raises four key concerns. Coincidentally, around the time of publication, others have also found the metaphor apt for describing AI-related challenges. There is no such thing as "The AI" Firstly, ”AI” is often discussed as though it were a single technology or autonomous actor, despite encompassing a diverse range of human-designed tools, methods and techniques. In many discussions, AI has become shorthand for generative AI, and for specific chat-based tools such as Copilot or ChatGPT. This lack of precision creates conceptual confusion. Different people understand AI differently, depending on the technologies available in their organisation and their own level of familiarity with them. When "AI" is used as a catch-all term, there is a risk of creating a Wild West of concepts in which people are discussing different things while believing they are talking about the same thing. This can obscure which tools are appropriate for specific tasks and data types, potentially leading to a Wild West of conceptual confusion. The evaluation community needs to become more specific about the technologies that are being discussed and the purposes for which they are used. Who decides what appropriate use looks like? The second concern relates to decision-making in organisations: How to avoid hasty decisions about AI use and manage unrealistic or misinformed expectations? Who decides what appropriate use is, and will the subject-matter experts be involved in such decisions? Many evaluation experts can envisage a situation where someone with limited evaluation knowledge proposes an apparently simple solution: "Why not ask a chatbot whether the project was successful?" While well-intentioned, such suggestions underestimate the nature of evaluation. At present, many decisions about AI use are made at the level of individual users. People experiment with tools as part of their personal AI uptake, often without guidance, standards or oversight. At the same time, senior decision-makers in different organisations face pressure to increase efficiency. This can result in a fragmented environment in which AI application emerges through ad hoc decisions. Without appropriate governance, this creates a real risk of producing what might be called a Wild West of pseudo-evaluation: activities that resemble evaluation on the surface but lack the methodological, quality and other requirements to meet evaluation standards and principles. The cart is being put before the horse when AI tools are increasingly used to analyse information as method choices rather than purposeful complementary tools. As organisations move towards agentic AI systems, this challenge becomes even more complex. The evaluation community should not take the demand for rigorous evaluations for granted. When accepted uses collide Thirdly, even if evaluation professionals are successfully engaged to define appropriate uses of AI, another problem remains. Different organisations reach different conclusions about what is acceptable use. Internal regulations, policies and risk tolerances vary considerably. A practice considered appropriate by one organisation may be prohibited by another – for example, AI-assisted analysis of interview transcripts. This creates practical challenges for evaluators, commissioners, and informants, potentially leading to a Wild West of accepted use. Informed consent will not solve this multi-layered problem alone, assuming all informants are able to make informed choices about such highly technical matters. Informed rejection, on the other hand, can lead to unequal representation in the data. Navigating the rules becomes increasingly difficult, and will require ongoing dialogue, transparency and collaboration across organisations. It will also require willingness to revert to traditional evaluation methods when necessary to ensure adequate representation. Selection and differing uses generate pseudo-evidence Perhaps the greatest risk concerns the long-term quality of evidence itself. Much of the public debate around AI has focused on hallucinations and bias. These are important concerns, but equally important is the issue of selection. When tools such as Copilot or Claude summarise information, they make choices about what to highlight, prioritise and omit. Many users assume these processes are objective and systematic. In reality, it is not possible to know exactly how information is selected, nor does the same task produce identical outputs over time. Most users understand that AI-generated outputs should not be accepted uncritically but many overlook a subtler issue. Summaries can be factually correct while emphasising different aspects of the source material. A small experiment using three different large language models (LLMs) to summarise evaluation findings showed that all outputs were factually correct, yet they differed in formulation, emphasis, presentation and balance of content. One also generated recommendations, although recommendations were not part of the materials. Summarising evaluation findings passages with different LLMs Description: A set of 11 text passages on evaluation findings were extracted and tested using three different LLMs. Prompt: Summarise the content of the document in about 100 words. The results were cross-checked against the original passages. All were factually correct but differed in what became emphasized through selection and choice of words, framing, level of detail, and in balance of substantive elements, detectable through manual color-coding. One LLM also generated recommendations to maximise impact (in its opinion), although not requested nor was such content included in the original materials. Bold added by author to highlight some such features. “[Country] is leveraging high‑level advocacy, strategic partnerships with tech giants and targeted financing to embed gender equality in global digital‑development policies and projects. While it has achieved notable policy wins (e.g., gender clause in the [name of compact]) and piloted innovative solutions that benefit women and vulnerable groups, inclusion outcomes remain uneven—especially for low‑income, women‑led micro‑enterprises. The main challenges are rigid financing tools, limited visibility of [country’s] solutions, and insufficient grassroots digital literacy. To maximise impact, [country] needs more flexible, harmonised funding mechanisms, stronger SME engagement, and intensified capacity‑building for the poorest and most marginalized users.” (LLM1) “[Country] has strongly promoted inclusion, gender equality, and digital development through multilateral influence and bilateral projects. Its leadership in the [name of campaign] and co-leading of the [name of coalition] helped address the digital gender divide and online safety. [Country] contributed to shaping the [name of compact] and supported digital public goods, AI governance, and connectivity. Bilateral initiatives, such as digital land administration in [country] and innovation ecosystem support in Africa and [country], showed significant impact. However, challenges persist, including mixed inclusion outcomes, limited benefits for disadvantaged groups, difficult SME mobilisation, underfunded instruments, and reduced visibility in multilateral programmes.” (LLM2) “[Country] has played a leading role in promoting inclusive digital development, particularly through multilateral engagement. It has influenced global processes such as the [name of compact] and co-led the [name of coalition], advancing gender equality, digital inclusion, and online safety. Bilateral initiatives, including digital land registries in [country], have supported women’s rights and access to finance. However, inclusion outcomes remain mixed, especially in entrepreneurship and job creation, with limited benefits for the most vulnerable groups. Challenges include underfinanced multilateral instruments, reduced visibility, limited SME engagement, and persistent global digital divides in connectivity, affordability, electricity access, and digital skills.” (LLM3) Legend [blue=description] [green=positive results] [yellow=mixed results] [red=challenges] [purple=LLM-generated recommendations] Analysis and summarisation are inherently processes of selection. Evaluators make such choices deliberately, guided by purpose, context and methodological judgement. AI systems also make selections, but often through processes that remain opaque to users. The effects are multiplied by the various types of accepted uses of AI. The danger is not necessarily just incorrect information but the gradual erosion of nuance, context and complexity. As AI-generated summaries are used to create further summaries, over time, there is a risk creating a chain of "summaries of summaries" in which essential details are lost, causing the fourth concern, a Wild West of pseudo-evidence, through selection and the cumulative loss of context and meaning over time. This bears profound consequences, altering the very nature of evidence. Towards thoughtful application The use of AI technologies in evaluation raises important questions regarding their intended purposes, the boundaries of acceptable applications, and the implications of differing interpretations across organisations. While definitive answers may not yet exist, it is essential that the evaluation community begins to engage with these issues. The evaluation community has much to gain from engaging with various applications of AI. As initiatives such as OpenEval.fi demonstrate, there are genuine opportunities to support evaluative work in new ways. However, the future will not be determined by the technology alone but also by the human ability to govern its use. The key question is whether evaluation professionals will have an active role in defining appropriate uses of AI within various organisations. The evaluation community must also continue to raise awareness of "what evaluation is". AI applications may increasingly outperform humans at processing data. However, evaluation entails much more: interpreting significance, meaning and value; and answering evaluation questions that are complex, contested, value-laden, influenced by context, and open to multiple interpretations. The ultimate challenge is not whether AI should be used in evaluation, but how it can be thoughtfully employed in ways that strengthen the quality, credibility and integrity of evidence on which decisions are based. Failure risks entering a Wild West of pseudo-evaluation and pseudo-evidence due to unthoughtful application of AI. Success can help establish AI as a valuable tool in service of rigorous evaluation, rather than a poor substitute for it. Nea-Mari Heinonen (MA, MSSc) is the Lead Evaluation Specialist and Deputy Director of the Development Evaluation Unit of the Ministry for Foreign Affairs of Finland. With 20 years in international development, she has piloted AI and data science approaches and is currently pursuing a PhD in social data science at the University of Helsinki. Connect with Nea-Mari on LinkedIn. The views expressed in this blog post are those of the author and do not necessarily reflect the views of her affiliated organisations. Acknowledgement of the use of AI: Copilot was used to create the first draft of this blog post based on the author’s original panel speeches prepared for gLocal, June 2026. All content is original by the author. Disclaimer: The content of the blog is the responsibility of the author(s) and does not necessarily reflect the views of Eval4Action co-leaders and partners.

The Manifesto sign it! A call to action to meaningfully engage youth and young evaluators in evaluation We, the members of the global evaluation community, including governments, parliamentarians, Voluntary Organizations for Professional Evaluation (VOPEs), United Nations agencies, evaluators, academia, private sector, and the development sector, Recognize that young and emerging evaluators are actively contributing to shaping the global evaluation culture, and are multiplying the transformational power of evaluation to build a sustainable, inclusive, and equitable world framed by social justice and equity; Accept our role and responsibility in nurturing and supporting the meaningful engagement of youth and young and emerging evaluators in evaluation processes and in evaluation advocacy, in order to support the achievement of the Sustainable Development Goals; Commit to undertake strategic and concerted efforts to build professional capacities of young and emerging evaluators; Commit to advocate for and as well as to enhance real and meaningful engagement of youth in evaluation, focusing on the diversity of youth, by collaborating with youth in the design, development and use of the evaluation. This includes engaging youth and young and emerging evaluators in all phases of the evaluation, including as: Key informants , to provide perspectives and evidence during the evaluation data collection, with a special focus on underrepresented youth groups Co-evaluators, who conduct the evaluation together with senior evaluation team members Advisors, who contribute insights to key deliverables of the evaluation process Decision-makers, who co-lead and co-guide the evaluation process together with the commissioners and/or managers of the evaluation process Advocates for use of the evaluation Acknowledge that meaningful engagement of youth and young and emerging evaluators in evaluation is an ongoing process, and not a one-time effort, which requires reflection on the process and using lessons for action and change. We/I commit to be a champion for youth in evaluation! Sign the manifesto Sign spanish

day 3 day 4 day 5 full programme day 1 | 24 April 2023 day 2 career development Further information and registration details for all sessions will be available shortly 1.00 am GMT 9.00 am Mongolia Voice Advocacy for the Youth in Evaluation manifesto and adoption of standards (Day 1 of 5) Mongolian Evaluation Association Hybrid Mongolian and English Mongolia 9.00 am GMT 10.00 am West Africa Voice Youth participation in the new age: What does it take? (Day 1 of 3) Simba Ghana In person Ghana English and Dagbani 12.00 pm GMT Voice Championing youth integration in evaluation (Day 1 of 2) Young and Emerging Evaluators in Nigeria Nigeria English Hybrid Register 1.00 pm GMT 9.00 am ET Global inauguration of Youth in Evaluation Week Eval4Action campaign and partners Voice Virtua l Global English Register day 2

The Future of Evaluation dialogue brings together leading evaluation experts and leaders, young and emerging evaluators and new voices in the evaluation community and beyond, to explore how innovative approaches and adaptations can make evaluation more influential given the evolving global landscape. Thursday, 15 October 2026 8:00 am - 9:15 am ET Register How can evaluation drive more sustainable solutions to end poverty? Ending poverty requires more than short-term gains in income and living conditions. It requires sustainable solutions that address the root causes of poverty, help people escape it for good, and protect them from falling back. Evaluation can play an important role in this effort by identifying what works in poverty reduction, for whom, under what conditions, why, and whether the gains last. This Future of Evaluation dialogue will examine how evaluation can generate evidence that informs more sustainable solutions to end poverty in all its dimensions. It will also explore how evaluators can better address the structural drivers of poverty, partner with affected communities to generate evidence, and drive more inclusive, resilient solutions. The dialogue will consider how evaluation can better respond to climate change, economic shocks, and rapid technological change. Ultimately, it will ask how evaluation can move beyond measuring progress to informing the actions that end poverty and improve people's lives. Moderator Khalil Bitar is President of the International Organization for Cooperation in Evaluation (IOCE), Co-Chair of EvalPartners, and co-founder and co-lead of Eval4Action. He steered the launch of the Global Evaluation Agenda 2.0 and has led or contributed to over 100 evaluations in 20+ countries, specializing in fragile and conflict-affected settings, where extreme poverty is increasingly concentrated. He also founded the Palestinian Evaluation Association, co-founded and co-chaired the EvalYouth Global Network, and is a founding trustee of the International Evaluation Academy. Panelists Dr. Claudia Maldonado serves as the Director-General for Evaluation and Poverty Measurement at Mexico’s National Institute of Statistics and Geography (INEGI). She served as Academic Councillor at the National Council for the Evaluation of Social Development Policy (CONEVAL) during the 2020–2024 period and previously served as Director of the CLEAR Latin America (2011–2016). She has served as a member of UNICEF and UNDP’s International Panel of Expert Evaluation Advisers, the advisory group of the Global Evaluation Initiative (GEI), and the International Program for Development Evaluation Training (IPDET). Dr. Vidya Diwakar is an Senior Research Fellow at Institute of Development Studies (IDS) and Deputy Director of the Chronic Poverty Advisory Network. She leads mixed-methods research and evaluations on poverty dynamics, resilience, intersecting crises and multi-sectoral approaches to poverty reduction across regions of sub-Saharan Africa and South and Southeast Asia. Houqi Hong is Principal Evaluation Specialist at the Asian Development Bank (ADB). He has over 20 years of experience in evaluation and results measurement in international development and has led or contributed to more than three dozen evaluations at ADB and the World Bank. He holds advanced degrees in economics and management from Tsinghua University and Georgetown University. Noelle Uloko is Director of Operations at Cloneshouse, with over 10 years’ experience in MEL business development, project delivery, partnerships, training, youth development, and institutional strengthening. Through the Cloneshouse Internship Programme and other youth initiatives, she has helped young people build practical skills, access professional pathways, and participate meaningfully in development. Future of Evaluation dialogue