This position paper presents a theoretical framework for enhancing
explainable artificial intelligence (xAI) through emergent communication
(EmCom), focusing on creating a causal understanding of AI model outputs. We
explore the novel integration of EmCom into AI systems, offering a paradigm
shift from conventional associative relationships between inputs and outputs to
a more nuanced, causal interpretation. The framework aims to revolutionize how
AI processes are understood, making them more transparent and interpretable.
While the initial application of this model is demonstrated on synthetic data,
the implications of this research extend beyond these simple applications. This
general approach has the potential to redefine interactions with AI across
multiple domains, fostering trust and informed decision-making in healthcare
and in various sectors where AI's decision-making processes are critical. The
paper discusses the theoretical underpinnings of this approach, its potential
broad applications, and its alignment with the growing need for responsible and
transparent AI systems in an increasingly digital world.

这篇研究论文介绍了一个理论框架，通过新兴的交流机制（EmCom）来增强可解释的人工智能（xAI），重点是对人工智能模型输出的原因理解。我们探讨了 EmCom 与人工智能系统的新颖整合，从传统的输入和输出之间的联想关系转变为更加微妙、因果性的解释。该框架旨在革命性地改变人们对人工智能处理过程的理解，使其更加透明和可解释。虽然这个模型的初始应用是在合成数据上演示的，但这项研究的影响超出了这些简单的应用。这种通用的方法有潜力重新定义与人工智能的交互，在医疗保健和其他重要领域中实现信任和明智决策。本文讨论了这一方法的理论基础、其潜在的广泛应用以及与越来越数字化的世界对负责任和透明的人工智能系统需求的一致性。