Austin Kenneth White
Machine-Generated Language Evaluation & Source-Based Analysis
Selected 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.
Selected analysis
Each analysis begins with observable generated language, identifies the consequential discrepancy, and then names the behavior. The emphasis is on what changed, how it changed, and what a downstream reader could misunderstand if the output were treated as reliable.