Can Large Language Models Reliably Code Qualitative Humanitarian Data? A Benchmark Study Against Human Expert Adjudication

2026-06-25 · Source: Machine Learning · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Social Sciences & Behavioral Studies · Depth: Intermediate, quick

Summary

A benchmark study compared 46 Large Language Models (LLMs) against a human Gold Standard for coding 150 high-fidelity synthetic humanitarian transcripts. The evaluation utilized Krippendorff's alpha, discrepancy analysis, and qualitative assessment across humanitarian-specific criteria like discrimination and complex needs. The findings indicate that multiple LLMs can achieve deductive coding reliability comparable to experienced human coders, particularly when structured prompts and reasoning-enabled configurations are employed. However, aggregate reliability metrics alone are insufficient for deployment decisions, as models showed variability in recognizing indirectly expressed needs, out-of-category needs, and protection-relevant concerns such as physical safety and discrimination. This suggests LLMs can expand analytical capacity but not replace human judgment.

Key takeaway

For humanitarian organizations considering LLMs for data analysis, you can integrate these models for deductive coding to scale analytical capacity. However, ensure human judgment remains central for interpreting nuanced accounts or sensitive protection-relevant concerns. Focus your tiered oversight on categories where miscoding would have significant programmatic consequences, and explore open-weights models on self-hosted infrastructure for stronger data governance.

Key insights

LLMs can reliably code humanitarian data with structured prompts, but human oversight remains crucial for nuanced cases.

Principles

• Structured prompts enhance LLM coding reliability.
• Reasoning-enabled LLMs improve performance.
• Aggregate reliability metrics are insufficient for deployment.

Method

A benchmark study compared 46 LLMs to a human Gold Standard using 150 synthetic humanitarian transcripts, evaluated via Krippendorff's alpha and discrepancy analysis.

In practice

• Use structured codebooks for LLM coding.
• Employ reasoning-enabled LLM configurations.
• Prioritize oversight for sensitive data categories.

Topics

• Humanitarian Data Analysis
• Large Language Models
• Qualitative Data Coding
• Benchmark Study
• Inter-rater Reliability
• Data Governance

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning.