Behavioral Validity Checks for ML‑Based "Coding" (www.gojiberries.io)

Researchers propose a practical behavioral-audit framework to test construct validity of ML and LLM "coders" used in social-science content analysis. Rather than rely solely on held-out accuracy or inter-annotator agreement, they recommend theory‑driven counterfactual edits to inputs and measure model behavior: invariance edits that should not change labels and directional edits that should. From these checks they define three summary metrics—Invariance Violation Rate (IVR, lower is better), Directional Sensitivity Rate (DSR, higher is better), and Causal‑Proxy Gap (CPG, performance drop when masking construct spans versus nuisance spans)—and suggest slicing by source/time/length/identity terms. They also pair these with codebook‑compliance guardrails and a Prompt‑Injection Violation Rate (PIVR) for LLMs to catch format or hidden-instruction failures. The paper gives a compact audit protocol (predeclare behaviors, build counterfactual pairs, test on correct and error cases, report IVR/DSR/CPG) and concrete remedies: tighten codebooks/prompts, augment training/few‑shot pools with the counterfactuals, and add light invariance penalties. A small experiment on SST-2 and IMDB shows real-world impact: some popular sentiment models suffer up to a 14% accuracy drop and consistency scores falling under ~65% on negation and spurious-perturbation tests. The authors stress these checks provide supportive—not definitive—evidence of construct validity and must be diverse, theory‑grounded, and combined with human review.