Emergent Explainability: Adding a causal chain to neural network
  inference
Adam Perrett
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.