From Field Data to Decisions: Building Trustworthy Digital MERL Workflows in Bangladesh’s Nutrition Program
Webinar | Online
-
Organized by:
International Development Enterprises (iDE)
About the Event
In complex development programs, large-scale data collection does not guarantee timely or actionable decisions. With growing use of AI in evaluation, the challenge is building trust in evidence without sidelining human judgment and over-reliance on automation. This session explores a practical case from Bangladesh’s Transforming Lives Through Nutrition (TLTN) program. We examine a field-to-dashboard MERL workflow that integrates digital data capture, structured quality assurance, service coverage mapping, and transparent governance to ensure data leads to trusted, human-led insights.
Speakers
| 名称 | 标题 | Biography |
|---|---|---|
| Poresh Moni Das | Technical Specialist - Monitoring, Evaluation, Research & Learning (MERL) | Poresh Moni Das is MERL Specialist at iDE Bangladesh, with over 7 years' experience in M&E systems in humanitarian, nutrition, and market systems development programming. In his role, Poresh helps turn field data into credible analysis, maps, and practical insights for stronger program decisions. |
Moderators
| 名称 | 标题 | 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. |
摘要
- The implementation of AI tools has transformed how the project team communicates, with faster feedback and evidence-based strategy.
- Trust is built across the entire system: A central message of the webinar is that a dashboard is only the visible layer of a monitoring, evaluation, research, and learning (MERL) system; trust must be established at every stage of the data pipeline.
- AI readiness is essentially MERL governance readiness. While AI can enhance efficiency, its role is limited. AI must not generate official figures without validation, use personal data in unsecured tools, or replace field verification and DQA processes. The final interpretation and responsibility for official reporting must always remain with humans.
- Set strict boundaries for AI integration: While AI can enhance efficiency, it must be used as an assistant rather than an authority. Teams must maintain a human-in-the-loop approach where staff review all AI-assisted outputs against field context.
- Prioritize data ethics: Secure personally identifiable information (PII) and use consent-based data minimization rules. When using tools like ChatGPT or Gemini, ensure settings are adjusted so that project data is not used to train the AI models.
- Environmental Awareness: Consider the environmental trade-off of utilizing power-intensive servers and processors for AI and digital workflows.