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@article{200034,
        author = {Vaishnavi Bhosale and Harshali Girase and Nikita Dhame and Prof.Santosh Pandure},
        title = {Integrating Emotional Intelligence and Fairness in Transformer- Based Language Models},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {54-72},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=200034},
        abstract = {Transformer-based language models such as GPT, BERT, and T5 have revolutionized Natural Language Processing (NLP) by achieving high performance in tasks like translation, summarization, and text generation. However, these models often lack emotional intelligence (EI) and fairness—two crucial elements needed to ensure ethical, empathetic, and unbiased human–AI interaction. This research focuses on integrating emotional intelligence and fairness into transformer-based models to enhance their emotional awareness and reduce algorithmic bias. 
The emotional intelligence component aims to enable the model to recognize and respond appropriately to human emotions. A multimodal approach is employed, combining textual, acoustic, and physiological signals to train the model in detecting emotional cues more effectively. Emotionally annotated datasets and affective computing methods are used to enrich the transformer’s contextual understanding. Emotion-aware attention layers are introduced into the transformer architecture, allowing it to modulate responses according to emotional tone, intensity, and user sentiment. This helps the model produce empathetic, contextually sensitive, and human-like responses. 
For fairness integration, the study implements bias detection and mitigation techniques during model training. Fairness-aware loss functions, counterfactual data augmentation, and adversarial debiasing are applied to minimize demographic, gender, and cultural biases in model predictions. The research also develops a fairness evaluation framework that measures and compares prediction fairness across user groups. This ensures the model delivers equitable and unbiased responses regardless of user background. 
Experimental results using benchmark datasets such as GoEmotions and Bias-in-Bios demonstrate significant improvement in emotional adaptability and fairness performance. Metrics such as emotion recognition accuracy, bias amplification rate, and fairness index show that the proposed model achieves balanced outcomes between empathy and objectivity while maintaining high linguistic accuracy. 
The key contributions of this research are twofold: (1) the development of an emotion-aware transformer framework capable of understanding and expressing empathy, and (2) the introduction of fairness-driven learning mechanisms that promote ethical and inclusive AI behavior. The findings have 
practical implications in areas such as conversational AI, healthcare chatbots, educational tools, and social robotics.},
        keywords = {Emotional Intelligence, Fairness, Transformer Models, Affective Computing, Ethical AI, Empathetic AI, Bias Mitigation, Human-Centric AI, Natural Language Processing, Emotion Recognitio.},
        month = {May},
        }