Evaluating AI Recruitment Sourcing Tools by Human Preference

• URL: http://arxiv.org/abs/2504.02463v1
• Date: Thu, 03 Apr 2025 10:33:43 GMT
• Title: Evaluating AI Recruitment Sourcing Tools by Human Preference
• Authors: Vladimir Slaykovskiy, Maksim Zvegintsev, Yury Sakhonchyk, Hrachik Ajamian,
• Abstract summary: This study introduces a benchmarking methodology designed to evaluate the performance of AI-driven recruitment sourcing tools.<n>We created and utilized a dataset to perform a comparative analysis of search results generated by leading AI-based solutions, LinkedIn Recruiter, and our proprietary system, Pearch.ai.<n>We found a strong alignment between AI-based evaluations and human judgments, highlighting the potential for advanced AI technologies to substantially enhance talent acquisition effectiveness.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This study introduces a benchmarking methodology designed to evaluate the performance of AI-driven recruitment sourcing tools. We created and utilized a dataset to perform a comparative analysis of search results generated by leading AI-based solutions, LinkedIn Recruiter, and our proprietary system, Pearch.ai. Human experts assessed the relevance of the returned candidates, and an Elo rating system was applied to quantitatively measure each tool's comparative performance. Our findings indicate that AI-driven recruitment sourcing tools consistently outperform LinkedIn Recruiter in candidate relevance, with Pearch.ai achieving the highest performance scores. Furthermore, we found a strong alignment between AI-based evaluations and human judgments, highlighting the potential for advanced AI technologies to substantially enhance talent acquisition effectiveness. Code and supporting data are publicly available at https://github.com/vslaykovsky/ai-sourcing-benchmark

Related papers

• Review, Refine, Repeat: Understanding Iterative Decoding of AI Agents with Dynamic Evaluation and Selection [71.92083784393418]
  Inference-time methods such as Best-of-N (BON) sampling offer a simple yet effective alternative to improve performance.<n>We propose Iterative Agent Decoding (IAD) which combines iterative refinement with dynamic candidate evaluation and selection guided by a verifier.
  arXiv  Detail & Related papers  (2025-04-02T17:40:47Z)
• General Scales Unlock AI Evaluation with Explanatory and Predictive Power [57.7995945974989]
  benchmarking has guided progress in AI, but it has offered limited explanatory and predictive power for general-purpose AI systems.<n>We introduce general scales for AI evaluation that can explain what common AI benchmarks really measure.<n>Our fully-automated methodology builds on 18 newly-crafted rubrics that place instance demands on general scales that do not saturate.
  arXiv  Detail & Related papers  (2025-03-09T01:13:56Z)
• CritiQ: Mining Data Quality Criteria from Human Preferences [70.35346554179036]
  We introduce CritiQ, a novel data selection method that automatically mines criteria from human preferences for data quality. CritiQ Flow employs a manager agent to evolve quality criteria and worker agents to make pairwise judgments. We demonstrate the effectiveness of our method in the code, math, and logic domains.
  arXiv  Detail & Related papers  (2025-02-26T16:33:41Z)
• Validity Arguments For Constructed Response Scoring Using Generative Artificial Intelligence Applications [0.0]
  generative AI is particularly appealing because it reduces the effort required for handcrafting features in traditional AI scoring. We compare the validity evidence needed in scoring systems using human ratings, feature-based natural language processing AI scoring engines, and generative AI.
  arXiv  Detail & Related papers  (2025-01-04T16:59:29Z)
• To Err Is AI! Debugging as an Intervention to Facilitate Appropriate Reliance on AI Systems [11.690126756498223]
  Vision for optimal human-AI collaboration requires 'appropriate reliance' of humans on AI systems. In practice, the performance disparity of machine learning models on out-of-distribution data makes dataset-specific performance feedback unreliable.
  arXiv  Detail & Related papers  (2024-09-22T09:43:27Z)
• AI Data Readiness Inspector (AIDRIN) for Quantitative Assessment of Data Readiness for AI [0.8553254686016967]
  "Garbage in Garbage Out" is a universally agreed quote by computer scientists from various domains, including Artificial Intelligence (AI)<n>There are no standard methods or frameworks for assessing the "readiness" of data for AI.<n>AIDRIN is a framework covering a broad range of readiness dimensions available in the literature.
  arXiv  Detail & Related papers  (2024-06-27T15:26:39Z)
• Fairness in AI-Driven Recruitment: Challenges, Metrics, Methods, and Future Directions [0.0]
  Big data and machine learning has led to a rapid transformation in the traditional recruitment process. Given the prevalence of AI-based recruitment, there is growing concern that human biases may carry over to decisions made by these systems. This paper provides a comprehensive overview of this emerging field by discussing the types of biases encountered in AI-driven recruitment.
  arXiv  Detail & Related papers  (2024-05-30T05:25:14Z)
• Beyond Static Evaluation: A Dynamic Approach to Assessing AI Assistants' API Invocation Capabilities [48.922660354417204]
  We propose Automated Dynamic Evaluation (AutoDE) to assess an assistant's API call capability without human involvement. In our framework, we endeavor to closely mirror genuine human conversation patterns in human-machine interactions.
  arXiv  Detail & Related papers  (2024-03-17T07:34:12Z)
• Human-Centric Multimodal Machine Learning: Recent Advances and Testbed on AI-based Recruitment [66.91538273487379]
  There is a certain consensus about the need to develop AI applications with a Human-Centric approach. Human-Centric Machine Learning needs to be developed based on four main requirements: (i) utility and social good; (ii) privacy and data ownership; (iii) transparency and accountability; and (iv) fairness in AI-driven decision-making processes. We study how current multimodal algorithms based on heterogeneous sources of information are affected by sensitive elements and inner biases in the data.
  arXiv  Detail & Related papers  (2023-02-13T16:44:44Z)
• Advancing Human-AI Complementarity: The Impact of User Expertise and Algorithmic Tuning on Joint Decision Making [10.890854857970488]
  Many factors can impact success of Human-AI teams, including a user's domain expertise, mental models of an AI system, trust in recommendations, and more. Our study examined user performance in a non-trivial blood vessel labeling task where participants indicated whether a given blood vessel was flowing or stalled. Our results show that while recommendations from an AI-Assistant can aid user decision making, factors such as users' baseline performance relative to the AI and complementary tuning of AI error types significantly impact overall team performance.
  arXiv  Detail & Related papers  (2022-08-16T21:39:58Z)
• Bias in Multimodal AI: Testbed for Fair Automatic Recruitment [73.85525896663371]
  We study how current multimodal algorithms based on heterogeneous sources of information are affected by sensitive elements and inner biases in the data. We train automatic recruitment algorithms using a set of multimodal synthetic profiles consciously scored with gender and racial biases. Our methodology and results show how to generate fairer AI-based tools in general, and in particular fairer automated recruitment systems.
  arXiv  Detail & Related papers  (2020-04-15T15:58:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.