Precision Prototyping: Leveraging AI for Intelligent Site Selection and Rapid Model Iteration in Bangladesh
Webinar (em inglês) | Online
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Organizado por:
International Development Enterprises (iDE)
Sobre o evento
How can AI move project evaluations from "hindsight" to "foresight"? This session presents a practical case study from the Oporajita project in Bangladesh, highlighting the use of AI to pre-select intervention sites and rapidly prototype collective business models. We explore how AI-driven prototyping allows teams to test enterprise ideas and viability before full-scale implementation. Attendees will learn practical ways to integrate AI into MERL workflows to increase precision, reduce costs, and ensure human-centric design remains at the heart of digital innovation.
Orador/a
| Nome | Título | Biography |
|---|---|---|
| Fatima Shehata | Senior Design Strategist and Experimentation Lead | Fatima is a social research and service design specialist with 10+ years of experience across 15+ countries. She delivers evidence-based strategies in Public Health, Resilience, Infrastructure, and Nutrition for the public, private, and NGO sectors to drive impactful global outcomes. |
| Sara Hanan Chowdhury | Senior Officer - Monitoring, Evaluation, Research & Learning (MERL) | Sara is a MERL professional at iDE Bangladesh with over five years of experience in development. Her expertise lies in mixed-methods research and evaluation, with a focus on qualitative approaches, participatory data collection, and analysis to inform program outcomes and decisions. |
Moderators
| Nome | Título | Biography |
|---|---|---|
| Abid ul Huque | Manager - Monitoring, Evaluation, Research & Learning (MERL) | Abid is currently leading the MERL unit at iDE Bangladesh, with over 6 years of experience in designing and implementing MERL frameworks and leading research to support evidence-based decision-making for market strategies in agriculture, nutrition, and resilience-building programs. |
Resumo
- AI cannot replace our traditional M&E processes, but it can make them more efficient and targeted, and help us turn data into local action.
- Avoid using AI to analyze raw field data. Instead, use collaborative synthesis workshops and expert interviews to preserve the nuanced local voices and human judgment that AI summaries often miss.
- When using AI to generate visuals, practitioners should use specific prompting to remove inherent biases, such as religious or status markers.
- AI is best used for repetitive or preparatory tasks—such as summarizing interviews, translating guidelines, or generating visual prototypes—allowing researchers to focus on the creative work of being human.
- Establish AI Responsible Use Policies: Organizations should work closely with IT teams to develop formal policies that define how AI can be used and ensure that models are not being trained on sensitive participant data.
- Adopt Dynamic Informed Consent: Researchers should move toward a model of "dynamic" consent, where participants are informed specifically about AI use and are given the right to be forgotten or to withdraw their consent in the future if perceived risks change.
- Maintain Human Verification: Every AI output—whether a map, a translation, or a generated image—must undergo human validation to ensure it aligns with the local context and preserves the dignity of the participants.