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GESA: Graph-Enhanced Semantic Allocation for Generalized, Fair, and Explainable Candidate-Role Matching

Rishi Ashish Shah, Shivaay Dhondiyal, Kartik Sharma, Sukriti Talwar, Saksham Jain, Sparsh Jain

TL;DR

GESA tackles the infamous triad of semantic flexibility, fairness, and explainability in candidate-role matching by integrating domain-adaptive semantic profiling (IntBERT), heterogeneous graph modeling (NexusGNN), adversarial debiasing, NSGA-II multi-objective optimization, and SHAP explanations. The framework achieves state-of-the-art top-3 allocation accuracy, substantial diversity gains, and near-perfect fairness scores on large-scale benchmarks, while maintaining sub-second latency. Its modular design supports deployment across industry, academia, and non-profits, enabling transparent decision-making and human oversight through explainable outputs and override capabilities. The work demonstrates practical impact by delivering a scalable, adaptable, and auditable allocation solution that balances merit, diversity, and user preferences in dynamic policy environments.

Abstract

Accurate, fair, and explainable allocation of candidates to roles represents a fundamental challenge across multiple domains including corporate hiring, academic admissions, fellowship awards, and volunteer placement systems. Current state-of-the-art approaches suffer from semantic inflexibility, persistent demographic bias, opacity in decision-making processes, and poor scalability under dynamic policy constraints. We present GESA (Graph-Enhanced Semantic Allocation), a comprehensive framework that addresses these limitations through the integration of domain-adaptive transformer embeddings, heterogeneous self-supervised graph neural networks, adversarial debiasing mechanisms, multi-objective genetic optimization, and explainable AI components. Our experimental evaluation on large-scale international benchmarks comprising 20,000 candidate profiles and 3,000 role specifications demonstrates superior performance with 94.5% top-3 allocation accuracy, 37% improvement in diversity representation, 0.98 fairness score across demographic categories, and sub-second end-to-end latency. Additionally, GESA incorporates hybrid recommendation capabilities and glass-box explainability, making it suitable for deployment across diverse international contexts in industry, academia, and non-profit sectors.

GESA: Graph-Enhanced Semantic Allocation for Generalized, Fair, and Explainable Candidate-Role Matching

TL;DR

GESA tackles the infamous triad of semantic flexibility, fairness, and explainability in candidate-role matching by integrating domain-adaptive semantic profiling (IntBERT), heterogeneous graph modeling (NexusGNN), adversarial debiasing, NSGA-II multi-objective optimization, and SHAP explanations. The framework achieves state-of-the-art top-3 allocation accuracy, substantial diversity gains, and near-perfect fairness scores on large-scale benchmarks, while maintaining sub-second latency. Its modular design supports deployment across industry, academia, and non-profits, enabling transparent decision-making and human oversight through explainable outputs and override capabilities. The work demonstrates practical impact by delivering a scalable, adaptable, and auditable allocation solution that balances merit, diversity, and user preferences in dynamic policy environments.

Abstract

Accurate, fair, and explainable allocation of candidates to roles represents a fundamental challenge across multiple domains including corporate hiring, academic admissions, fellowship awards, and volunteer placement systems. Current state-of-the-art approaches suffer from semantic inflexibility, persistent demographic bias, opacity in decision-making processes, and poor scalability under dynamic policy constraints. We present GESA (Graph-Enhanced Semantic Allocation), a comprehensive framework that addresses these limitations through the integration of domain-adaptive transformer embeddings, heterogeneous self-supervised graph neural networks, adversarial debiasing mechanisms, multi-objective genetic optimization, and explainable AI components. Our experimental evaluation on large-scale international benchmarks comprising 20,000 candidate profiles and 3,000 role specifications demonstrates superior performance with 94.5% top-3 allocation accuracy, 37% improvement in diversity representation, 0.98 fairness score across demographic categories, and sub-second end-to-end latency. Additionally, GESA incorporates hybrid recommendation capabilities and glass-box explainability, making it suitable for deployment across diverse international contexts in industry, academia, and non-profit sectors.

Paper Structure

This paper contains 54 sections, 14 equations, 2 figures, 6 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Traditional Allocation Approaches
  4. Machine Learning-Based Matching
  5. Deep Learning and Embedding Approaches
  6. Graph-Based Approaches
  7. Fairness and Bias Mitigation
  8. Explainable AI in Allocation
  9. System Architecture and Design Principles
  10. Modular Design Principles
  11. Data Flow and Processing Pipeline
  12. Scalability Considerations
  13. Semantic Profiling and Representation Learning
  14. Domain-Adaptive Transformer Architecture
  15. Semantic Vector Generation and Properties
  16. ...and 39 more sections

Figures (2)

  • Figure 1: General architectural model of a Job Recommender System
  • Figure 2: Comprehensive GESA system architecture showing the flow from data ingestion through semantic profiling, graph construction, adversarial debiasing, multi-objective optimization, and explainable output generation.