Methodological Approach

The methodological approach of EXCALIBUR is structured as an end-to-end strategy for democratizing trustworthy and responsible AI, by combining regulatory mapping, method development with Large Language Models (LLMs), tool building, and stakeholder-driven evaluation.

Phase 1: Regulatory & Requirements Mapping

The project begins by translating Trustworthy AI principles into actionable technical requirements, with emphasis on transparency, fairness, and accountability, while also specifying human-centric needs (human oversight, societal well-being, safety) and establishing ethical and data governance guidelines to ensure GDPR-aligned experimentation.

Phase 2: Framework Development

Building on this foundation, EXCALIBUR designs a model-agnostic LLM-based framework with two core components:

• Explainability Module: Learns from diverse XAI methods (e.g., LIME-like to more advanced techniques) to generate human-readable explanations.
• Fairness Evaluation Module: Computes and interprets a broad set of fairness metrics and uses the LLM to explain their meaning in context and help uncover potential sources of bias in data and models.

Phase 3: Validation & Experimentation

The framework is then validated through two complementary experimentation tracks in the wearable domain:

• In-lab Evaluation: Using baseline AI models built on public wearable datasets for method development and fine-tuning.
• In-the-wild Case Studies: Involving non-expert stakeholders in iterative rounds to assess usability, trust, clarity of explanations, and usefulness of fairness reports.

This process follows a citizen-science-inspired approach to ensure continuous feedback and real-world relevance.

Phase 4: Open-Source Delivery

Finally, EXCALIBUR delivers an open-source toolset and visualization platform (e.g., a Python library plus a web-based interface) that:

• Integrates the framework into real AI pipelines
• Presents results in a human-friendly way
• Supports user intervention and feedback
• Applies standard security and privacy practices for safe deployment