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.

翻译：本文提出一个理论框架，旨在通过涌现通信（EmCom）增强可解释人工智能（xAI），重点关注建立对AI模型输出的因果理解。我们探索将EmCom创新性地融入AI系统，实现从传统输入-输出关联关系到更精细的因果解释的范式转变。该框架旨在革新AI处理过程的理解方式，使其更加透明和可解释。尽管该模型的初始应用在合成数据上得到验证，但本研究的意义远不止于此。这种通用方法具有潜力重新定义跨多个领域的AI交互，在医疗保健及其他AI决策过程至关重要的行业中促进信任与知情决策。本文讨论了该方法的理论基础、其广泛的应用前景，以及其如何契合日益数字化世界中对负责任且透明AI系统日益增长的需求。