Quantifying truth and authenticity in AI-assisted candidate evaluation: A multi-domain pilot analysis
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.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
GA-GS: Generation-Assisted Gaussian Splatting for Static Scene Reconstruction
Reconstructing static 3D scene from monocular video with dynamic objects is important for numerous applications such as virtual reality and autonomous driving. Current approaches typically rely on background for static s…
Autonomous DrivingBetter Together: Quantifying the Benefits of AI-Assisted Recruitment
Artificial intelligence (AI) is increasingly used in recruitment, yet empirical evidence quantifying its impact on hiring efficiency and candidate selection remains limited. We randomly assign 37,000 applicants for a jun…
Decision MakingExploring validation metrics for offline model-based optimisation with diffusion models
In model-based optimisation (MBO) we are interested in using machine learning to design candidates that maximise some measure of reward with respect to a black box function called the (ground truth) oracle, which is expe…
DenoisingModel SelectionMORE: A Multilingual Document Parsing Benchmark and Evaluation
Multilingual documents encapsulate rich regional cultures, scientific discoveries, and historical records. Parsing this content into structured, machine-readable formats is critical for unlocking global knowledge. Howeve…
Digital Avatars: Framework Development and Their Evaluation
We present a novel prompting strategy for artificial intelligence driven digital avatars. To better quantify how our prompting strategy affects anthropomorphic features like humor, authenticity, and favorability we prese…
Language ModelingLanguage ModellingLarge Language Model