Explainable AI

Explainable AI

Explainable AI is a branch of artificial intelligence that focuses on developing machine learning models and algorithms that can provide clear, understandable explanations for their decision-making processes and outputs. In contrast to traditional black box AI systems, where the internal workings and reasoning behind predictions or classifications are opaque, explainable AI aims to create transparent and interpretable models. This transparency is crucial for building trust in AI systems, ensuring accountability, and facilitating human-AI collaboration. Explainable AI techniques include rule-based systems, decision trees, linear models, and attention mechanisms, which allow users to trace the steps and factors that contribute to a model's output. By providing insights into how an AI system arrives at its conclusions, explainable AI enables users to understand the model's strengths and limitations, identify potential biases, and make informed decisions based on the AI's recommendations. Moreover, explainable AI is particularly important in domains such as healthcare, finance, and criminal justice, where the consequences of AI-driven decisions can have significant impacts on individuals and society. As AI becomes increasingly integrated into various aspects of our lives, the development of explainable AI is essential for promoting responsible and ethical use of these technologies, fostering public trust, and ensuring that AI systems align with human values and expectations.

Transparency, Interpretability, Accountability, Trust, Human-AI Collaboration, Decision-Making, Bias Detection, Responsible AI, Ethical AI

— Robert Anderson

CITATION : "Robert Anderson. 'Explainable AI.' Design+Encyclopedia. https://design-encyclopedia.com/define/explainable-ai?E=432022 (Accessed on October 13, 2026)"