A cautious interpretation of AI regulations and policy in the EU and the USA
place explainability as a central deliverable of compliant AI systems. However,
from a technical perspective, explainable AI (XAI) remains an elusive and
complex target where even state of the art methods often reach erroneous,
misleading, and incomplete explanations. "Explainability" has multiple meanings
which are often used interchangeably, and there are an even greater number of
XAI methods - none of which presents a clear edge. Indeed, there are multiple
failure modes for each XAI method, which require application-specific
development and continuous evaluation. In this paper, we analyze legislative
and policy developments in the United States and the European Union, such as
the Executive Order on the Safe, Secure, and Trustworthy Development and Use of
Artificial Intelligence, the AI Act, the AI Liability Directive, and the
General Data Protection Regulation (GDPR) from a right to explanation
perspective. We argue that these AI regulations and current market conditions
threaten effective AI governance and safety because the objective of
trustworthy, accountable, and transparent AI is intrinsically linked to the
questionable ability of AI operators to provide meaningful explanations. Unless
governments explicitly tackle the issue of explainability through clear
legislative and policy statements that take into account technical realities,
AI governance risks becoming a vacuous "box-ticking" exercise where scientific
standards are replaced with legalistic thresholds, providing only a false sense
of security in XAI.

通过分析有关 AI 规定和政策的立法进展，本研究讨论了可解释性人工智能（XAI）的技术现状及其对 AI 治理的潜在威胁，指出需要明确、合理的立法和政策以解决 AI 解释能力的问题。