A Theoretical Framework for Visual Representation and User Interface Design in Explainable AI for HR Management using Machine Learning Models

This research develops a theoretical framework to improve the visual representation and user interface design in Explainable Artificial Intelligence systems applied in Human Resource management that uses machine learning models. HR practices are evolving with AI integration for their tasks such as recruitment, performance analysis, and employee retention. Demand for transparent and interpretable AI systems is still unsatisfied. XAI techniques such as Local Interpretable Model-agnostic Explanations (LIME), SHapley Additive exPlanations (SHAP), and attention mechanisms help in explaining complex AI models. However, these techniques are inadequate, when we consider the integration of these data into visual representations and user interfaces for non-technical HR users of AI applications. A theoretical framework is developed that helps represent existing XAI methods results in a visual model along with UI design principles. This can help developers to create XAI interfaces that enhances interpretability, scalability and trust. Literature review covers the acknowledgement of current developments in XAI techniques and HR needs. Further the research develops practical guidelines for visualization and UI design. The bridging between XAI and HR in this study contributes to the advancement of explainable AI applications and contributes to improved decision-making and AI trust in HR management processes.