Integrating AI into Evaluation: From Causal Evidence to Real-Time Adaptive Policymaking
Table ronde | En ligne
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Organisé par:
Department of Community Development (DCD)
À propos de l'événement
Artificial intelligence (AI) is rapidly reshaping the tools available to evaluators and policy makers, yet its integration into the decision-making theatre remains largely unexplored. Grounded in the experience of the Department of Community Development (DCD), the social sector regulator in Abu Dhabi, this panel examines how AI-driven approaches can strengthen evidence-informed policymaking, amplify the voices of the communities DCD serves, and ultimately improve the lives of the local population and its most vulnerable groups. Drawing on DCD’s mandate as social sector regulator, it reflects on how evaluation augmented by AI can provide learning across complex systems, support inclusive and real-time decision-making, and ensure that the design and adaptation of social programs is informed by the needs of local communities.
The panel explores the integration of high-dimensional and multi-dimensional administrative data with targeted survey data to generate real-time insights and foresights. Through the linkage of individual-level and community-level wellbeing data with administrative records, the panel will discuss how adaptive, responsive policymaking can make use of real-time data on the behaviour of program beneficiaries as well as rich survey data from the lived experiences of people and communities, ensuring that diverse voices inform public decisions in a timely manner.
The panel will also examine the use of AI applications for identifying and modelling specific policies, as well as their driving factors and key social outcomes, such as employment or family stability. This approach also allows for scenario testing and policy simulation.
Finally, the panel will also address how AI-driven impact predictions, built upon systematically rationalised and categorised data assets (e.g., administrative records, evaluation reports, policy briefs, as well as data files) can directly inform the design of new social sector strategies and policies. It outlines the process of structuring and classifying diverse data sources to enable predictive analytical modelling, and demonstrates how the resulting foresights can guide evidence-informed strategy development.
Collectively, the panel argues that the responsible integration of AI into monitoring and evaluation practice represents an innovation that can enable more responsive, evidence-grounded, and people-centred policymaking, ultimately strengthening social impact.
The evaluation field faces a dual challenge. On the one hand, traditional ex-post methods, whilst rigorous, often deliver findings too late to inform strategic policymaking. On the other hand, AI offers powerful analytical capabilities that risk undermining causal rigour if deployed without careful governance. This panel addresses this challenge by presenting concrete, complementary approaches to AI-evaluation integration developed within a real policy context, offering original methodological contributions to the evaluation community. Its objective is to discuss innovative methods for integrating diverse data sources for real-time policy learning, whilst ensuring that analytical rigour and evaluation standards are maintained.
The panel explores the integration of high-dimensional and multi-dimensional administrative data with targeted survey data to generate real-time insights and foresights. Through the linkage of individual-level and community-level wellbeing data with administrative records, the panel will discuss how adaptive, responsive policymaking can make use of real-time data on the behaviour of program beneficiaries as well as rich survey data from the lived experiences of people and communities, ensuring that diverse voices inform public decisions in a timely manner.
The panel will also examine the use of AI applications for identifying and modelling specific policies, as well as their driving factors and key social outcomes, such as employment or family stability. This approach also allows for scenario testing and policy simulation.
Finally, the panel will also address how AI-driven impact predictions, built upon systematically rationalised and categorised data assets (e.g., administrative records, evaluation reports, policy briefs, as well as data files) can directly inform the design of new social sector strategies and policies. It outlines the process of structuring and classifying diverse data sources to enable predictive analytical modelling, and demonstrates how the resulting foresights can guide evidence-informed strategy development.
Collectively, the panel argues that the responsible integration of AI into monitoring and evaluation practice represents an innovation that can enable more responsive, evidence-grounded, and people-centred policymaking, ultimately strengthening social impact.
The evaluation field faces a dual challenge. On the one hand, traditional ex-post methods, whilst rigorous, often deliver findings too late to inform strategic policymaking. On the other hand, AI offers powerful analytical capabilities that risk undermining causal rigour if deployed without careful governance. This panel addresses this challenge by presenting concrete, complementary approaches to AI-evaluation integration developed within a real policy context, offering original methodological contributions to the evaluation community. Its objective is to discuss innovative methods for integrating diverse data sources for real-time policy learning, whilst ensuring that analytical rigour and evaluation standards are maintained.
Conférenciers
| Nom | Titre | Biography |
|---|---|---|
| Asma Al Rashdi | Mrs | Asma is an expert in computer science, statistics, and social research. She is currently serving as the Executive Director of the Social Monitoring and Impact Sector at DCD. |
| Michael Joseph | Mr | Michael is an impact evaluation specialist, currently serving as the Manager of the Social Impact Division at DCD. |
| Ahmad Al Rubaie | Mr | Ahmad is a data scientist, currently serving as the Manager of the AI Division at DCD. |
Moderators
| Nom | Titre | Biography |
|---|---|---|
| Michele Binci | Dr | Dr Michele Binci is a development economist and impact evaluation specialist. He serves as Advisor to the Chairman at DCD on social impact assessment. He is currently advancing the application of AI to evaluation and the estimation of impact. |
Résumé
The panel discussion explored how Artificial Intelligence (AI) can be meaningfully integrated into evaluation and estimation analysis to support better, faster, and more adaptive decision making.
The discussion focused on the opportunities created by AI, whilst also recognising the need for careful and purposeful integration. A key point emerging from the panel was that AI should add clear value to evaluation practice. Its contribution needs to be explicit, useful, and proportionate, rather than assumed. Panellists reflected on where AI can genuinely strengthen evaluation, for instance by increasing efficiency, supporting faster analysis, enabling the processing of complex or high dimensional data, and helping evaluators identify patterns that may be difficult to detect through traditional approaches alone.
At the same time, the panel emphasised that the use of AI must not come at the expense of evaluation quality. Robust design, credible causal evidence, careful interpretation, and methodological rigour remain essential. AI can enhance evaluation, but it does not replace the judgement, experience, and critical reasoning of the evaluator. The discussion highlighted the importance of keeping the human in the loop, ensuring that evaluators remain central to the design, validation, interpretation, and use of AI supported analysis.
Another important theme was the need to avoid black box AI. The panel stressed the value of traceability, transparency, and explainability, particularly when AI is used to inform policy decisions that may affect communities and vulnerable groups through social sector interventions. Participants discussed the importance of understanding how AI supported insights are generated, what assumptions underpin them, and how findings can be checked, challenged, and validated.
Audience questions added further depth to the discussion, particularly around when and how AI outputs should be validated. This prompted a useful exchange on the need for clear validation points throughout the evaluation process, from checking data inputs and assumptions, to reviewing model outputs, testing results against expert judgement, and assessing whether findings are credible, interpretable, and useful for decision making.
The panel also considered how AI could support more real time and adaptive policymaking. By improving the speed and scope of analysis, AI has the potential to help decision makers respond more quickly to emerging evidence, needs for ex-ante estimations, and complex social conditions. However, this potential can only be realised if AI is embedded within strong evaluation systems and clear governance arrangements.
Overall, the panel provided a positive and constructive discussion on the role of AI in evaluation. It highlighted important opportunities to strengthen evaluation and estimation analysis, particularly through greater efficiency, analytical capacity, and responsiveness. At the same time, it reinforced the need for quality, transparency, traceability, validation, and human judgement. AI should support evaluators and decision makers, not replace them, and its value should be judged by the extent to which it improves the quality, usefulness, and timeliness of evidence for social policy and practice.