Quantifying truth and authenticity in AI-assisted candidate evaluation: A multi-domain pilot analysis

TL;DR

This study analyzes AI-assisted candidate evaluations across multiple domains, showing that combining factual and linguistic authenticity measures enhances hiring efficiency and trust in AI systems.

Contribution

It introduces a multi-dimensional verification framework that assesses candidate truthfulness through both factual accuracy and linguistic authenticity in AI-assisted hiring.

Findings

90-95% reduction in screening time

Detection of linguistic patterns indicating AI-assisted responses

Enhanced trust in AI-mediated evaluation systems

Abstract

This paper presents a retrospective analysis of anonymized candidate-evaluation data collected during pilot hiring campaigns conducted through AlteraSF, an AI-native resume-verification platform. The system evaluates resume claims, generates context-sensitive verification questions, and measures performance along quantitative axes of factual validity and job fit, complemented by qualitative integrity detection. Across six job families and 1,700 applications, the platform achieved a 90-95% reduction in screening time and detected measurable linguistic patterns consistent with AI-assisted or copied responses. The analysis demonstrates that candidate truthfulness can be assessed not only through factual accuracy but also through patterns of linguistic authenticity. The results suggest that a multi-dimensional verification framework can improve both hiring efficiency and trust in AI-mediated evaluation systems.