FST.ai 2.0: An Explainable AI Ecosystem for Fair, Fast, and Inclusive Decision-Making in Olympic and Paralympic Taekwondo

2025-10-20

AI Summary

Olympic and Paralympic taekwondo judging faces challenges in fairness, transparency, and explainability. This paper introduces the first scalable human-AI collaborative framework for sports governance, integrating graph convolutional networks for pose-action recognition, credal-set-based modeling of epistemic uncertainty, and multi-granularity visualization for explanation—yielding a real-time, interpretable decision-support system for referees, coaches, and athletes. Its key innovation lies in embedding quantitative uncertainty estimation directly into the judging inference pipeline and enabling dynamic provenance tracing and consensus building via an interactive dashboard. Empirical evaluation demonstrates an 85% reduction in adjudication review time and a 93% referee trust rate in AI-generated recommendations, significantly enhancing judging efficiency, inter-rater consistency, and institutional credibility.

Technology Category

• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Uncertainty Representations

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Fair, transparent, and explainable decision-making remains a critical challenge in Olympic and Paralympic combat sports. This paper presents emph{FST.ai 2.0}, an explainable AI ecosystem designed to support referees, coaches, and athletes in real time during Taekwondo competitions and training. The system integrates {pose-based action recognition} using graph convolutional networks (GCNs), {epistemic uncertainty modeling} through credal sets, and {explainability overlays} for visual decision support. A set of {interactive dashboards} enables human--AI collaboration in referee evaluation, athlete performance analysis, and Para-Taekwondo classification. Beyond automated scoring, FST.ai~2.0 incorporates modules for referee training, fairness monitoring, and policy-level analytics within the World Taekwondo ecosystem. Experimental validation on competition data demonstrates an {85% reduction in decision review time} and {93% referee trust} in AI-assisted decisions. The framework thus establishes a transparent and extensible pipeline for trustworthy, data-driven officiating and athlete assessment. By bridging real-time perception, explainable inference, and governance-aware design, FST.ai~2.0 represents a step toward equitable, accountable, and human-aligned AI in sports.

Problem

• Addressing fair and transparent decision-making challenges in combat sports
• Developing real-time AI support for Taekwondo referees and athletes
• Creating explainable systems for trustworthy officiating and performance analysis

Innovation

• Graph convolutional networks for pose-based action recognition
• Epistemic uncertainty modeling using credal sets
• Interactive dashboards with explainability overlays