AI-AUGMENTED LEADERSHIP PROFILING: MACHINE LEARNING MODELS FOR IDENTIFYING TRANSFORMATIONAL AND ETHICAL LEADERSHIP TRAITS

Artificial intelligence has expanded the possibilities of leadership assessment by enabling objective, data-driven profiling that captures behavioural nuances far beyond traditional psychometric tools. This study proposes an AI-augmented framework for identifying transformational and ethical leadership traits using supervised machine learning models trained on multimodal organisational data, including communication patterns, decision-making logs, behavioural indicators, and validated leadership inventories.The research examines how algorithms such as Random Forest, Gradient Boosting, and Transformer-based language models can detect core dimensions of transformational leadershipidealised influence, inspirational motivation, intellectual stimulation, and individualised considerationalongside ethical traits such as fairness, transparency, accountability, and moral judgement. A hybrid methodology is adopted, combining feature engineering, natural language processing, and model interpretability techniques to ensure transparency and biasmitigation. Results demonstrate that AI-generated leadership profiles improve predictive reliability, reduce evaluator subjectivity, and uncover hidden behavioural signatures that traditional assessments miss. The study emphasises the importance of explainability, ethical safeguards, and organisational context to prevent algorithmic misclassification and reinforce trust. By integrating computational models with leadership theory, this research contributes a scalable and responsible approach to talent identification, succession planning, and leadership development in modern organisations.