A Multi-Modal Deep Learning and Explainable AI Framework for Transparent Job Matching and Career Development

Abstract

In the digital age, career and professional growth are being influenced with advanced systems which enable finding employment, reviewing applicants and skill advancement. The changing aspect of Explainable Artificial Intelligence (XAI) is essential to allow contextual job matching and reduce discrimination in AI-based job processes. However, most of the existing systems are still non-transparent and restrictive, perpetuating prejudice and weakening credibility. The study presents a new career development and recruitment platform using XAI and surpasses the traditional methods. The proposed system uses a hybrid two-stage system to combine deep learning with the Graph Neural Networks to encode candidate job relevance as well as structural dynamics of career progression, skills dependencies, and mentorship networks. New feature engineering algorithms simulate the dynamics of temporal profiles development and skill acquisition, which allow dynamic and context-sensitive candidate representations. In order to guarantee interpretability, a recruitment-specific explainability engine offers stakeholder-specific explanations such as comparative explanations between a candidate and a job, trajectory correspondence insights, and visualizations of fair trade-offs. The system is tested to execute its functions: a real-world evaluation, which is a combination of fairness statistical measures and accuracy with user-centric interpretability measures, proves the effectiveness of the system. The results highlight the potential of radically changing the current state of hybrid AI architectures and domain-specific explainability to create ethical, equitable, and adaptive solutions in the future of work.