Building an advanced neural network is only half the battle; evaluating its true performance and making its decisions interpretable is vital for real-world deployment. This session explores how to look inside the "black box" of sequence-to-sequence neural networks using post-hoc explainability tools and specialised evaluation metrics. We will discuss why standard accuracy metrics fall short on character-level and text-generation tasks, introducing Character Error Rate (CER) and Word Error Rate (WER) as industry standards. The core of the session will walk through the implementation of SHAP (Shapley Additive exPlanations) analysis on complex Transformer components. Attendees will learn how to visually quantify the exact feature attribution of different layers—such as character embeddings versus phonetic fusion networks—so they can build trustworthy, explainable AI systems.

Session Format & Duration: 30-Minute Technical Talk. Target Audience: Machine Learning Engineers, Developers, and Product Managers who want to implement Explainable AI (XAI) in production. Technical Requirements: Standard projector setup with HDMI. Presentation Outline (30 Minutes):00:00–00:10 | The Evaluation Gap: Transitioning from basic accuracy to sequence metrics like Character Error Rate (CER) and Word Error Rate (WER). 00:10–00:25 | SHAP Framework Deep Dive: Step-by-step breakdown of feature attribution mechanics, explaining how to calculate layer-by-layer contributions visually. 00:25–00:30 | Explainability for Trust: Best practices for presenting model explanations to non-technical stakeholders, followed by Q&A.