Year: 2025
Type: article
Abstract: Artificial Intelligence (AI) is transforming decision-making in critical fields like healthcare, finance, and governance.However, its "black box" nature undermines trust and comprehension.Explainable AI (XAI) addresses this by enhancing transparency and interpretability, yet aligning explainability with human cognitive and emotional needs remains challenging.This paper explores principles and methodologies for designing human-centered XAI, emphasizing user profiling, dynamic explanations, and ethical considerations like fairness and accountability.Key contributions include adaptive explanations tailored to diverse user needs and strategies to mitigate biases, advancing AI systems that are transparent, accessible, and trustworthy.
Source: IARJSET
Institution: University of Colorado Boulder
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4406577700