tahtest

Tahtest is a framework for evaluating the reliability, transparency, and fairness of automated decision systems. It is intended to provide a consistent, audit-ready set of checks for models operating in high-stakes or policy-relevant environments. The term tahtest is a portmanteau of transparency audit and test, and it has been used in scholarly and industry discussions to refer to an end-to-end assessment process.

The framework typically comprises several components: an execution harness that runs models against standardized test suites, comprehensive audit logs to document inputs, outputs, and decisions, input-output pair analyses to identify unexpected behavior, and a suite of examination tests for fairness, robustness, and interpretability. Scenarios may cover classification, scoring, recommendation, and decision-making under uncertainty. Scoring is often done with a composite metric that aggregates reproducibility, explainability, bias indicators, and stability across perturbations.

In practice, tahtest guides the preparation of datasets, the replication of results, and the documentation of limitations and remediation steps. It is designed to be adaptable to different domains and risk levels, though it requires careful selection of datasets and clear governance to avoid misuse or overclaiming.

Limitations include dependency on the quality and representativeness of test data, potential computational cost, and the challenge of capturing all relevant risk factors. See also AI auditing, model cards, and algorithmic transparency.