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

• URL: http://arxiv.org/abs/2508.11713v1
• Date: Thu, 14 Aug 2025 12:17:19 GMT
• Title: A Production-Ready Machine Learning System for Inclusive Employment: Requirements Engineering and Implementation of AI-Driven Disability Job Matching Platform
• Abstract summary: Employment inclusion of people with disabilities remains critically low in Italy, with only 3.5% employed nationally despite mandatory hiring quotas.<n>Our goal is to develop a production-ready machine learning system for disability employment matching that integrates social responsibility requirements while maintaining human oversight in decision-making.<n>The system achieves 90.1% F1-score and sub-100ms response times while processing 500,000 candidate-company combinations in under 10 minutes.
• Score: 20.821562115822182
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Mind the Gap: Can Frontier LLMs Pass a Standardized Office Proficiency Exam? [90.39633603556767]
  Large Language Model (LLM) agents for computer automation is accelerating, yet their ability to navigate complex, professional-grade productivity software is largely untested.<n>To quantify this capability, we introduce an evaluation based on China's National Computer Rank Examination (NCRE)<n>We benchmark 7 frontier LLMs and observe stark limitations: single-turn models score a maximum of 36.6%.<n>A stronger agentic system with execution feedback, iterative repair, and broader Office automation access reaches 68.8%, but remains below the 95.5% community-reference score used as a scoring sanity check.
  arXiv  Detail & Related papers  (2026-06-09T14:59:14Z)
• EXPO-FT: Sample-Efficient Reinforcement Learning Finetuning for Vision-Language-Action Models [84.73890225707264]
  We present EXPO-FT, a system for stable, sample-efficient RL finetuning of pretrained VLA policies.<n>Our system achieves perfect task performance (30/30 successes) across all evaluated tasks within an average of 19.1 minutes of online robot data.<n>We release an open-source robustness with the aim of facilitating broader adoption of RL finetuning of VLA models in robotics.
  arXiv  Detail & Related papers  (2026-05-25T06:31:03Z)
• Agentic AI and Occupational Displacement: A Multi-Regional Task Exposure Analysis of Emerging Labor Market Disruption [1.066082625513843]
  We extend the Acemoglu-Restrepo task exposure framework to address the labor market effects of agentic artificial intelligence systems.<n>Unlike prior automation technologies that substitute for individual subtasks, agentic AI systems execute end-to-end using multi-step reasoning.<n>We introduce the Agentic Exposure score, a composite measure computed algorithmically from O*NET task data.
  arXiv  Detail & Related papers  (2026-03-31T19:36:36Z)
• LLM-Powered Workflow Optimization for Multidisciplinary Software Development: An Automotive Industry Case Study [8.211397148550782]
  Multidisciplinary Software Development (MSD) requires domain experts and developers to collaborate across incompatible formalisms and separate artifact sets.<n>We evaluate our approach on textttspapi, a production in-vehicle API system at Volvo Group involving 192 endpoints, 420 properties, and 776 CAN signals.<n>The automated workflow achieves 93.7% F1 score while reducing per-API development time from approximately 5 hours to under 7 minutes, saving an estimated 979 engineering hours.
  arXiv  Detail & Related papers  (2026-03-22T22:59:28Z)
• AI+HW 2035: Shaping the Next Decade [135.53570243498987]
  Artificial intelligence (AI) and hardware (HW) are advancing at unprecedented rates, yet their trajectories have become inseparably intertwined.<n>This vision paper lays out a 10-year roadmap for AI+HW co-design and co-development, spanning algorithms, architectures, systems, and sustainability.<n>We identify key challenges and opportunities, candidly assess potential obstacles and pitfalls, and propose integrated solutions.
  arXiv  Detail & Related papers  (2026-03-05T14:36:33Z)
• SkillNet: Create, Evaluate, and Connect AI Skills [159.47504178122156]
  SkillNet is an open infrastructure designed to create, evaluate, and organize AI skills at scale.<n>Our infrastructure integrates a repository of over 200,000 skills, an interactive platform, and a versatile Python toolkit.
  arXiv  Detail & Related papers  (2026-02-26T14:24:02Z)
• AI-Driven Decision-Making System for Hiring Process [0.0]
  This paper presents an AI-driven, modular multi-agent hiring assistant.<n>It integrates (i) document and video preprocessing, (ii) structured candidate profile construction, (iii) public-data verification, (iv) technical/culture-fit scoring with explicit risk penalties, and (v) human-in-the-loop validation via an interactive interface.
  arXiv  Detail & Related papers  (2025-12-17T18:45:17Z)
• AI Autonomy Coefficient ($α$): Defining Boundaries for Responsible AI Systems [0.15293427903448018]
  Integrity of contemporary AI systems is compromised by misuse of Human-in-the-Loop models.<n>We introduce the AI-First, Human-Empowered (AFHE) paradigm, which requires AI systems to demonstrate a quantifiable level of functional independence.<n>We conclude that AFHE provides a metric-driven approach for ensuring verifiable autonomy, transparency, and sustainable operational integrity in modern AI systems.
  arXiv  Detail & Related papers  (2025-12-12T05:41:20Z)
• SelfAI: Building a Self-Training AI System with LLM Agents [79.10991818561907]
  SelfAI is a general multi-agent platform that combines a User Agent for translating high-level research objectives into standardized experimental configurations.<n>An Experiment Manager orchestrates parallel, fault-tolerant training across heterogeneous hardware while maintaining a structured knowledge base for continuous feedback.<n>Across regression, computer vision, scientific computing, medical imaging, and drug discovery benchmarks, SelfAI consistently achieves strong performance and reduces redundant trials.
  arXiv  Detail & Related papers  (2025-11-29T09:18:39Z)
• Higher Satisfaction, Lower Cost: A Technical Report on How LLMs Revolutionize Meituan's Intelligent Interaction Systems [67.18731675163589]
  We introduce WOWService, an intelligent interaction system tailored for industrial applications.<n>With the integration of LLMs and multi-agent architectures, WOWService enables autonomous task management and collaborative problem-solving.<n> WOWService is deployed on the Meituan App, achieving significant gains in key metrics.
  arXiv  Detail & Related papers  (2025-10-15T08:35:51Z)
• OmniEAR: Benchmarking Agent Reasoning in Embodied Tasks [52.87238755666243]
  We present OmniEAR, a framework for evaluating how language models reason about physical interactions, tool usage, and multi-agent coordination in embodied tasks.<n>We model continuous physical properties and complex spatial relationships across 1,500 scenarios spanning household and industrial domains.<n>Our systematic evaluation reveals severe performance degradation when models must reason from constraints.
  arXiv  Detail & Related papers  (2025-08-07T17:54:15Z)
• Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Survey [58.50944604905037]
  Edge-cloud collaborative computing (ECCC) has emerged as a pivotal paradigm for addressing the computational demands of modern intelligent applications.<n>Recent advancements in AI, particularly deep learning and large language models (LLMs), have dramatically enhanced the capabilities of these distributed systems.<n>This survey provides a structured tutorial on fundamental architectures, enabling technologies, and emerging applications.
  arXiv  Detail & Related papers  (2025-05-03T13:55:38Z)
• Intelligent Mobile AI-Generated Content Services via Interactive Prompt Engineering and Dynamic Service Provisioning [55.641299901038316]
  AI-generated content can organize collaborative Mobile AIGC Service Providers (MASPs) at network edges to provide ubiquitous and customized content for resource-constrained users.<n>Such a paradigm faces two significant challenges: 1) raw prompts often lead to poor generation quality due to users' lack of experience with specific AIGC models, and 2) static service provisioning fails to efficiently utilize computational and communication resources.<n>We develop an interactive prompt engineering mechanism that leverages a Large Language Model (LLM) to generate customized prompt corpora and employs Inverse Reinforcement Learning (IRL) for policy imitation.
  arXiv  Detail & Related papers  (2025-02-17T03:05:20Z)
• Meta-Computing Enhanced Federated Learning in IIoT: Satisfaction-Aware Incentive Scheme via DRL-Based Stackelberg Game [50.6166553799783]
  Efficient IIoT operations require a trade-off between model quality and training latency.<n>This paper designs a satisfaction function that accounts for data size, Age of Information (AoI), and training latency for meta-computing.<n>We employ a deep reinforcement learning approach to learn the Stackelberg equilibrium.
  arXiv  Detail & Related papers  (2025-02-10T03:33:36Z)
• Efficient Multi-Task Inferencing with a Shared Backbone and Lightweight Task-Specific Adapters for Automatic Scoring [1.2556373621040728]
  This paper proposes a shared backbone model architecture enhanced with lightweight LoRA adapters for task-specific fine-tuning.<n>It achieves competitive performance while reducing GPU memory consumption by 60% and inference latency by 40%.<n>The findings underscore the potential of scalable AI to enhance learning outcomes while maintaining fairness and transparency in automated scoring systems.
  arXiv  Detail & Related papers  (2024-12-30T16:34:11Z)
• Zero-touch realization of Pervasive Artificial Intelligence-as-a-service in 6G networks [8.500820283596774]
  We introduce a novel platform architecture to deploy a zero-touch PAI-as-a-Service (PAI) in 6G networks supported by a blockchain-based smart system. We present a proof of concept where we evaluate the ability of our proposed system to self-optimize and self-adapt to the dynamics of 6G networks.
  arXiv  Detail & Related papers  (2023-07-21T10:02:24Z)
• End-to-end Adaptive Distributed Training on PaddlePaddle [10.855328649169293]
  We design our distributed training framework in a systematic end-to-end view to provide the built-in adaptive ability for different scenarios. Based on the unified distributed graph and the unified cluster object, our adaptive framework is equipped with a global cost model and a global planner. Experiments demonstrate that our framework can satisfy various requirements from the diversity of applications.
  arXiv  Detail & Related papers  (2021-12-06T03:00:32Z)

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.