SHORTS: Best practices for creating representative training datasets for M&E

Creating representative training datasets for M&E AI models requires deliberate strategies to mirror diverse program beneficiaries, outcomes, and contexts, avoiding skewed evaluations in development projects. These practices ensure models generalize across regions, demographics, and intervention types without amplifying inequities.quanthub+2

Data Collection Planning

Define M&E-specific objectives first, such as impact prediction for health or agriculture programs, then source data from surveys, administrative records, and qualitative reports representing all strata like urban/rural or gender groups. Use stratified sampling to proportionally capture minority classes, e.g., rare failure outcomes in logframes, ensuring 10-20% coverage of edge cases.deasylabs+1

Cleaning and Balancing

Remove duplicates, handle missing values via imputation aligned with M&E protocols (e.g., last observation carried forward for time-series monitoring), and balance classes with techniques like SMOTE for oversampling underrepresented beneficiaries. Verify distributions match real-world program demographics through statistical tests like chi-square.zaytrics+1

Augmentation and Validation

Apply domain-relevant augmentation, such as perturbing survey responses for cultural variations or simulating data shifts in multi-year evaluations, to boost dataset size by 20-50%. Split into 70/15/15 train/validation/test sets with random or stratified partitioning, then validate representativeness using metrics like Kolmogorov-Smirnov tests against population baselines.quanthub+1

Quality Assurance Practices

Involve diverse M&E stakeholders for labeling consistency, document metadata (e.g., collection date, source), and conduct bias audits pre-training. Iterate with cross-validation to confirm generalizability across deployment cycles.deasylabs+1