HIRE Benchmark: Setting the Standard for Candidate Evaluation AI

Overview

The HIRE Benchmark (Hiring Input Resume Evaluation) is a first-of-its-kind industry-standard framework for evaluating AI models that assess candidate suitability for specific roles. Designed with scientific rigor, repeatability, and fairness, HIRE provides a single, reliable percentage score for benchmarking models. The goal is to establish HIRE as the definitive leaderboard for candidate evaluation AI, driving progress, innovation, and equity in hiring practices.

Learn more about how Endorsed’s top AI models perform on the HIRE Benchmark, setting a new standard for candidate evaluation accuracy, fairness, and reliability.

Motivation

The hiring process increasingly relies on AI models to assess resumes and candidate profiles. However, evaluating the accuracy, fairness, and consistency of these models remains challenging due to the subjective and nuanced nature of hiring decisions. The HIRE Benchmark addresses this gap by:

  1. Standardizing Model Evaluation:

    • Provides a consistent framework to compare AI models across key candidate evaluation dimensions.

    • Enables organizations to confidently assess model accuracy, generalization, and reliability.

  2. Advancing Research:

    • Empowers researchers to test hypotheses on AI improvements, including fine-tuning, prompt engineering, and cost optimization.

    • Promotes transparency and trust in AI hiring systems through peer-reviewed benchmarks.

  3. Encouraging Industry-Wide Equity:

    • Aims to reduce bias and improve fairness in AI-powered hiring decisions by setting standards for evaluating models' equity.

    • Establishes clear metrics for eliminating disparities in areas like years-of-experience interpretation, geographic biases, and progression evaluation.

  4. Encouraging Industry Adoption:

    • Offers a community-driven, open-source benchmark that will eventually become the industry standard for evaluating people-focused AI systems.

    • Facilitates collaboration between researchers, practitioners, and hiring professionals.

Key Features

1. Comprehensive Evaluation Dimensions

HIRE evaluates models across critical subcategories for candidate evaluation:

  • Years of Experience (YoE): Accuracy in interpreting and calculating years worked.

  • Skills: Correctness in matching candidate skills to job requirements.

  • Education: Verification of degree levels and institutional alignment with job criteria.

  • Certifications: Matching certifications to job prerequisites.

  • Subjective: Ratings for cultural fit, leadership, and other soft skills.

  • Geographic Reasoning: Accuracy in location-based requirements.

  • Progression: Assessment of career trajectory and growth.

2. Fairness and Equity Metrics

  • Incorporates specific guidelines to evaluate AI systems for fairness and eliminate biases in candidate evaluations.

  • Regularly audits test cases for representativeness across demographics, geographic regions, and career types to prevent biased outcomes.

3. Ground Truths

  • Relies on human-labeled ground truths, ensuring reliability and accountability in score generation.

  • Accounts for inherent subjectivity in hiring by standardizing annotation guidelines and using majority voting to ensure fairness.

4. Single Metric

  • Produces a single percentage score to represent model accuracy and fairness across all evaluation dimensions, simplifying comparisons between models.

5. Automated Test Suite

  • HIRE continuously expands its test suite by transforming anonymized user feedback into new test cases, ensuring relevance and scalability.

Research Goals

  1. Model Comparisons:

    • Evaluate both closed-source (e.g., GPT-4, Claude) and open-source models (e.g., LLaMA, Mistral) for hiring-specific use cases.

    • Publish a publicly accessible leaderboard to rank models based on their HIRE scores.

    • Endorsed’s AI models consistently lead in accuracy, fairness, and reliability across all benchmark categories, as reflected in HIRE leaderboard results.

  2. Hypothesis Testing:

    • Quantify the impact of fine-tuning, prompt engineering, and cost-saving measures on model accuracy and fairness.

    • Investigate trade-offs between accuracy, fairness, cost, and speed in candidate evaluations.

  3. Promoting Equity:

    • Test and improve fairness in AI models by benchmarking their ability to eliminate biases in key subcategories like education, geography, and progression.

    • Encourage adoption of equitable AI models across industries to foster more inclusive hiring practices.

  4. Community Collaboration:

    • Foster an open-source ecosystem for advancing hiring-specific benchmarks and datasets.

    • Enable researchers and practitioners to contribute test cases and insights to improve the benchmark.

Vision

The HIRE Benchmark aims to become the definitive standard for evaluating candidate evaluation AI. By providing a reliable, repeatable, and trustworthy score, HIRE will:

  • Drive innovation in hiring-focused AI systems.

  • Promote fairness and transparency in AI-powered decision-making.

  • Reduce bias in hiring, creating more equitable opportunities for candidates across the industry.

  • Establish itself as the industry leaderboard for evaluating people evaluators.

Endorsed is proud to lead the way on the HIRE Benchmark while championing fairness and accuracy in hiring practices.