AI algorithms that determine what to annotate first — reducing cost, bias, and time while improving the quality of behavioral research data.
Data annotation is a bottleneck in behavioral science. Across cognitive science, psychology, linguistics, and related fields, researchers need large volumes of human-labeled data to train models, validate hypotheses, and build datasets — but annotation is slow, expensive, biased, and hard to staff.
The specific challenges are:
We develop specialized algorithms that address these problems in two ways:
Determine which data is most important to annotate first. Active learning approaches select the most informative samples, reducing the total volume of annotation required to reach a target accuracy.
Provide confidence metrics about label accuracy, automatically flagging data that requires additional human review. This minimizes the impact of annotator bias and disagreement.
Together, these algorithms minimize annotation workload while directing appropriate data to suitable annotators — matching data difficulty to annotator expertise.
This project feeds directly into the Global Human Language Optimization (GHLO) pipeline. Generating a new global human language requires large-scale validation of generated texts — ensuring the output is learnable, expressive, culturally neutral, and free of ambiguity. Data annotation is the mechanism for that validation, and efficient annotation is what makes it feasible at scale.
The algorithms developed here generalize beyond behavioral science to any domain that requires labeled data: medical imaging, legal document classification, sentiment analysis, and more. The core insight — that not all data needs to be annotated, and that the order of annotation matters — is domain-agnostic.