Austin Kenneth White
Machine-Generated Language Evaluation & Source-Based Analysis

Machine-generated language under examination
I review machine-generated language for the places where fluent, plausible output stops being fully faithful to a source or to the user’s
actual reasoning problem. The work goes beyond fact-checking. It examines shifts in certainty, scope, attribution, sequence, agency, evidentiary status,

legal conditions, and interpretation — especially when an output appears competent enough that those changes are easy to miss.

Why this matters

The most consequential failures are not always obvious fabrications. A summary can preserve the names, dates, topics, and broad

conclusion while becoming more categorical, more settled, or more authoritative than its source.