// MODEL OPTIMIZATION AND PROMPT SYNTAX TERM
Explainable AI
Tools and techniques that help people understand how an AI system made a particular decision or prediction.
TECHNICAL DEFINITION
Explainable AI (XAI) refers to methods and techniques that enable human users to comprehend and trust the outputs and decision-making processes of artificial intelligence models, particularly complex black-box models, by providing interpretable insights.
BACKGROUND
Prompt engineering is the process of structuring natural language inputs to produce specified outputs from a generative artificial intelligence (GenAI) model. Context engineering is the related area of software engineering that focuses on the management of non-prompt contexts supplied to the GenAI model, such as metadata, API tools, and tokens.
READ MORE ON WIKIPEDIASYNONYMS & ALIASES
- XAI
- interpretable AI
- transparent AI
- understandable AI
USAGE NOTE
XAI is crucial in regulated industries like healthcare and finance to justify AI decisions.
DEVELOPERS
Organizations developing technology related to Explainable AI.
IBM Research is a leader in developing explainable AI technologies, including the open-source AI Explainability 360 (AIX360) toolkit, which provides a comprehensive library of algorithms and tools for understanding and explaining machine learning models.
Google AI is deeply invested in responsible AI initiatives, including the development of frameworks and tools for model interpretability and explainability to help users understand, diagnose, and improve their AI systems.
Microsoft Research actively develops and contributes to the field of explainable AI through research into interpretability techniques, fairness assessments, and the creation of tools like the Responsible AI Toolbox.
MIT CSAIL is a prominent academic research institution conducting foundational research in explainable AI, developing novel algorithms, theoretical frameworks, and practical applications for understanding complex AI models.
DARPA initiated and funded the pioneering Explainable AI (XAI) program, driving significant advancements in the field by commissioning research and development projects to create AI systems that provide explanations for their decisions.
AWS integrates explainability features into its machine learning services, such as Amazon SageMaker Clarify, allowing developers to detect bias in datasets and models, and enhance the explainability of model predictions.
Salesforce AI Research conducts advanced studies and publishes papers on AI interpretability, fairness, and accountability, focusing on developing techniques to make AI models more understandable and trustworthy, especially in enterprise contexts.