Sustainable and Ethical AI-Driven Recognition in Robotics: Integrating ESG Analytics and Human–Robot Interaction

Environmental, Social, and Governance (ESG) information has become an essential component in evaluating corporate responsibility and long-term resilience. However, its incremental value in predicting firm profitability remains insufficiently understood. This study investigates whether integrating ESG analytics with traditional financial ratios enhances the machinelearning classification of firms into high- and low-profitability categories. Using a multi-industry dataset that combines firmlevel ESG pillar scores with accounting-based financial indicators, three supervised learning models—Decision Trees, Random Forests, and Support Vector Machines (SVM)—are developed and evaluated. Model validation is conducted through crossvalidation, and predictive performance is assessed using Accuracy, F1-score, and the Area Under the ROC Curve (AUROC). To isolate the specific contribution of ESG factors, ablation experiments and feature-importance analyses are performed. The findings reveal that the Random Forest model provides the most consistent and robust predictive performance (Accuracy = 0.89, F1-score = 0.88, AUROC = 0.93), with Environmental and Governance dimensions emerging as the most influential ESG predictors. The novelty of this research lies in establishing a clear mechanism linking ESG analytics to financial performance and in proposing an ESG-aware evaluation framework, rather than introducing a new predictive model or dataset.