The processes and methods that allow human users to understand and trust the outputs produced by AI are important in addressing which key regulatory…

Explainable AI (XAI) refers to the set of processes, methods, and techniques that ensure the outputs and decisions made by an AI system can be understood by humans. The core purpose of XAI is to open the "black box" of complex models, providing clear explanations for their behavior. This directly addresses the regulatory concern for transparency and accountability by allowing users, developers, and regulators to understand why an AI system reached a particular conclusion, which is fundamental for building trust and verifying compliance with legal and ethical standards.

A. Interpretable AI: This is a narrower concept, often referring to the intrinsic property of a model (e.g., a simple decision tree) being understandable due to its simple structure, whereas XAI includes methods applied to complex, non-interpretable models.

B. Trustworthy AI: This is a broad, overarching goal or framework. Explainability is a component required to achieve trustworthy AI, but it is not the specific term for the methods of understanding outputs.

D. Responsible AI: Similar to Trustworthy AI, this is a comprehensive governance framework that encompasses many principles, including fairness, privacy, and security, with XAI being just one of its essential pillars.

1. National Institute of Standards and Technology (NIST). (2023). AI Risk Management Framework (AI RMF 1.0).

Page 4

Section 2

Definitions: "Explainability: A representation of the mechanisms underlying AI system operation

provided in a form that is understandable to a human." This definition directly aligns with the question's description of processes allowing users to understand AI outputs.

2. Gunning

D.

& Aha

D. (2019). DARPA’s Explainable Artificial Intelligence (XAI) Program. AI Magazine

40(2)

44-58.

Page 44

Abstract: The XAI program's goal is to create AI systems where "human users to understand

appropriately trust

and effectively manage the emerging generation of artificially intelligent partners." This foundational source links understanding with trust

matching the question's phrasing.

DOI: https://doi.org/10.1609/aimag.v40i2.2850

3. Stanford University Human-Centered Artificial Intelligence (HAI). (2023). What Are Foundation Models? Report from the Center for Research on Foundation Models (CRFM).

Page 108

Section 6.2

Transparency: The report discusses transparency as a key societal impact

stating

"Transparency is a prerequisite for accountability... At the model level

transparency can mean explainability (i.e.

being able to understand why a model makes the decisions it does)." This highlights explainability as the mechanism for understanding decisions.