Artificial Intelligence (AI) is rapidly integrating into various aspects of
our daily lives, influencing decision-making processes in areas such as
targeted advertising and matchmaking algorithms. As AI systems become
increasingly sophisticated, ensuring their transparency and explainability
becomes crucial. Functional transparency is a fundamental aspect of algorithmic
decision-making systems, allowing stakeholders to comprehend the inner workings
of these systems and enabling them to evaluate their fairness and accuracy.
However, achieving functional transparency poses significant challenges that
need to be addressed. In this paper, we propose a design for user-centered
compliant-by-design transparency in transparent systems. We emphasize that the
development of transparent and explainable AI systems is a complex and
multidisciplinary endeavor, necessitating collaboration among researchers from
diverse fields such as computer science, artificial intelligence, ethics, law,
and social science. By providing a comprehensive understanding of the
challenges associated with transparency in AI systems and proposing a
user-centered design framework, we aim to facilitate the development of AI
systems that are accountable, trustworthy, and aligned with societal values.

人工智能的功能透明性在透明系统中遵守设计框架中成为关键挑战，要求跨学科合作来推进透明和可解释的人工智能系统的发展。

实现人工智能功能透明度与有意义的可解释性之路

Path To Gain Functional Transparency In Artificial Intelligence With  Meaningful Explainability

This paper investigates the prospect of developing human-interpretable,
explainable artificial intelligence (AI) systems based on active inference and
the free energy principle. We first provide a brief overview of active
inference, and in particular, of how it applies to the modeling of
decision-making, introspection, as well as the generation of overt and covert
actions. We then discuss how active inference can be leveraged to design
explainable AI systems, namely, by allowing us to model core features of
``introspective'' processes and by generating useful, human-interpretable
models of the processes involved in decision-making. We propose an architecture
for explainable AI systems using active inference. This architecture
foregrounds the role of an explicit hierarchical generative model, the
operation of which enables the AI system to track and explain the factors that
contribute to its own decisions, and whose structure is designed to be
interpretable and auditable by human users. We outline how this architecture
can integrate diverse sources of information to make informed decisions in an
auditable manner, mimicking or reproducing aspects of human-like consciousness
and introspection. Finally, we discuss the implications of our findings for
future research in AI, and the potential ethical considerations of developing
AI systems with (the appearance of) introspective capabilities.

本文探讨了基于活跃推断和自由能原理开发人类可解释、可理解的人工智能系统的前景，并提出了一个使用活跃推断的可解释 AI 系统架构，其结构设计为可被人类用户解释和审计。

利用主动推理设计可解释人工智能：透明反思和决策的框架

Designing explainable artificial intelligence with active inference: A  framework for transparent introspection and decision-making

The current literature on AI-advised decision making -- involving explainable
AI systems advising human decision makers -- presents a series of inconclusive
and confounding results. To synthesize these findings, we propose a simple
theory that elucidates the frequent failure of AI explanations to engender
appropriate reliance and complementary decision making performance. We argue
explanations are only useful to the extent that they allow a human decision
maker to verify the correctness of an AI's prediction, in contrast to other
desiderata, e.g., interpretability or spelling out the AI's reasoning process.
Prior studies find in many decision making contexts AI explanations do not
facilitate such verification. Moreover, most contexts fundamentally do not
allow verification, regardless of explanation method. We conclude with a
discussion of potential approaches for more effective explainable AI-advised
decision making and human-AI collaboration.

本文聚焦 AI 辅助决策，在 AI 解释人类决策过程中频繁失败的背景下，提出了一个简单的理论，即解释只有在允许决策者验证其正确性时才有用，我们讨论了更有效的 AI 决策解释方法和人工智能 - 人类决策的协作。