arXiv:2604.23354v3 Announce Type: replace-cross Abstract: Neural networks can be trained to learn task-relevant representations from data. Understanding how these networks make decisions falls within the Explainable AI (XAI) domain. This paper proposes to study an XAI topic: analysing, visualising and understanding the unknown organisation of network representations, particularly those a speaker recognition network learns from utterances, for recognising speaker identity. Past studies have employed algorithms (e.g. K-means) to analyse the different ways in which network representations can be
The proliferation of advanced AI systems necessitates greater transparency and trustworthiness, making Explainable AI a critical concurrent development to ensure adoption and ethical deployment.
Understanding the decision-making processes of AI, particularly in sensitive applications like speaker recognition, is crucial for accountability, bias detection, and ultimately, widespread societal acceptance and regulatory compliance.
The focus on making latent AI representations understandable shifts the development paradigm towards more interpretable models, potentially influencing future AI architecture design and audit requirements.
- · AI ethicists
- · Regulatory bodies
- · Enterprises deploying AI
- · AI audit firms
- · Black-box AI developers
- · Unaccountable AI systems
Improved trust and reliability will accelerate the integration of AI into high-stakes domains.
New standards for AI explainability will emerge, becoming prerequisites for market entry in some sectors.
The ability to 'debug' AI decisions at a fundamental level could lead to significantly more robust and less biased AI systems across various applications.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI