A Production-Ready Machine Learning System for Inclusive Employment: Requirements Engineering and Implementation of AI-Driven Disability Job Matching Platform

TL;DR

This paper presents a production-ready AI system for disability employment matching that combines participatory requirements engineering, ensemble machine learning models, and multi-dimensional scoring to improve matching efficiency and social responsibility.

Contribution

It introduces a novel participatory design process and a high-performance ensemble ML system tailored for inclusive employment, with open-source resources for replication.

Findings

Achieves 90.1% F1-score in matching accuracy.

Processes 500,000 candidate-company pairs in under 10 minutes.

Expert validation shows 60-100% increase in employment capacity.

Abstract

Employment inclusion of people with disabilities remains critically low in Italy, with only 3.5% employed nationally despite mandatory hiring quotas. Traditional manual matching processes require 30-60 minutes per candidate, creating bottlenecks that limit service capacity. Our goal is to develop and validate a production-ready machine learning system for disability employment matching that integrates social responsibility requirements while maintaining human oversight in decision-making. We employed participatory requirements engineering with Centro per l'Impiego di Villafranca di Verona professionals. The system implements a seven-model ensemble with parallel hyperparameter optimization using Optuna. Multi-dimensional scoring combines semantic compatibility, geographic distance, and employment readiness assessment. The system achieves 90.1% F1-score and sub-100ms response times while processing 500,000 candidate-company combinations in under 10 minutes. Expert validation confirms 60-100% capacity increases for employment centers. The LightGBM ensemble shows optimal performance with 94.6-second training time. Thus, advanced AI systems can successfully integrate social responsibility requirements without compromising technical performance. The participatory design methodology provides a replicable framework for developing ethical AI applications in sensitive social domains. The complete system, including source code, documentation, and deployment guides, is openly available to facilitate replication and adaptation by other regions and countries facing similar challenges.