XAI

Explainable AI

XAI, or eXplainable Artificial Intelligence, refers to methods and techniques in AI that aim to make the outputs of machine learning models more understandable to humans. The focus of XAI is to provide transparency in AI systems, allowing users to comprehend how decisions are made by these models.

Key Features of XAI

• Interpretability: XAI techniques help interpret model predictions, making it easier for users to understand the reasoning behind specific outputs.
• Transparency: By elucidating the decision-making process, XAI fosters trust in AI systems among users and stakeholders.
• Accountability: XAI allows for better accountability in AI applications, particularly in sensitive areas such as healthcare and finance, where understanding the rationale behind decisions is crucial.

Tools and Libraries

One notable resource in this field is the XAI toolbox available on GitHub, developed by EthicalML.