Scripted Minds Using EquiLex Framework to Eliminate Bias in AI-Powered Language Understanding Systems

Language understanding systems based on AI are beginning to be applied to more serious areas like healthcare, where biased interpretation can result in negative, unfair, or inaccurate results. Due to biased training data and the lack of control over fairness constraints, traditional NLP models tend to pick up micro- and macro-level societal biases and lead to unreliable predictions within the demographic groups. In order to remove these limitations, the present paper suggests LexQuill, a multi-stage bias mitigation system that combines lexical debiasing, BERT-based semantic encoder, MLP-based bias classification, and text generation based on fairness. The system has been able to effectively detect explicit, implicit and stereotype-based biases maintaining semantic fidelity. LexQuill has a bias-type classification accuracy of above 98% and a fairness consistency of strong counterfactual assessment. The framework, as the modular one, is supporting healthcare chatbots, clinical documentation, and decision-support systems to facilitate the transparent, equitable, and reliable AI-assisted communication. LexQuill is an interim step of scale-up to responsible and inclusive language technologies.