Quantifying truth and authenticity in AI-assisted candidate evaluation: A multi-domain pilot analysis
Year: 2025
Type: preprint
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.
Source: arXiv (Cornell University)
Topic: AI and HR Technologies
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W4415939038
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