Skaters and Officials Hope AI Will Bring Clarity to Scoring
Figure skating, a sport long admired for its blend of athleticism and artistry, is facing calls for greater fairness in how performances are judged. Decades of controversies have led the sport's governing body, the International Skating Union (ISU), to explore the use of artificial intelligence (AI) and computer vision technology. The goal is to introduce more consistency and openness into the scoring system, potentially reducing claims of bias and improving how athletes are evaluated.
A Shift Towards Data in a Subjective Sport
For years, figure skating has grappled with subjective scoring. While athletic elements like jumps and spins can be measured with some objectivity, the artistic and performance aspects have often been left to the discretion of human judges. This has, at times, led to accusations of favoritism and inconsistent evaluations.
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Historical Context: The sport has a history marked by judging disputes, prompting a search for more reliable methods.
Athlete Support: Skaters themselves have expressed a willingness to embrace technological assistance to ensure fairer assessments.
ISU's Vision: The ISU is looking at AI not only for scoring but also to analyze the performance and consistency of human judges.
AI's Expanding Role: From Training to Transparency
The application of AI in figure skating extends beyond just scoring. It is also being integrated into athlete training, providing data-driven feedback to help skaters improve their skills more efficiently.
Training Applications: AI systems are already being used to assess movement execution and fluidity, aiding in daily practice and specific disciplines like pairs skating.
System Rollout: The ISU plans a phased introduction of AI scoring, beginning with singles skating and later extending to pairs and ice dance.
Technological Integration: China, for instance, has developed its own AI-Assisted Scoring System, which evaluates performance based on professional scoring criteria.
Evaluating Performance Metrics
The complexity of figure skating moves, such as multi-rotation jumps, requires precise analysis. AI and computer vision can offer a level of detail that is difficult for the human eye to capture in real-time.
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Jump Analysis: AI can accurately measure jump height and rotation counts, providing objective data points.
Spin Dynamics: Spin speeds and the quality of the position can be quantified.
Beyond Scoring: This technology also offers potential for enhanced fan engagement, with broadcast graphics illustrating key performance metrics.
Addressing Subjectivity and Artistic Interpretation
A key question remains regarding AI's ability to evaluate the artistic dimensions of figure skating. While AI can meticulously track technical execution, the interpretation of choreography, musicality, and emotional expression presents a different challenge.
Technical vs. Artistic: Can AI adequately assess the quality of an artistic performance, or will its focus remain on measurable technical execution?
Future Possibilities: While the immediate focus is on objective elements, the long-term potential for AI in evaluating artistic merit is a subject of ongoing consideration.
Athlete Ambition: The pursuit of complex new elements, such as the quadruple jump (quad), highlights the need for precise measurement in athletic development.
Expert Insights on AI's Impact
The integration of AI into figure skating is seen by many as a logical step towards modernizing the sport and addressing long-standing issues.
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"After decades of judging controversies, figure skating is turning to artificial intelligence (AI) and computer vision technology to try to bring greater consistency and transparency to how performances are scored." - The Strait Times
"In figure skating, you need to understand: How high did this person jump, how many times did they go around, and how well did they land? Skating looks slow on television, but it’s not." - MIT Technology Review
Conclusion and Future Direction
The adoption of AI in figure skating represents a significant effort to enhance objectivity and transparency in a sport where scoring has historically been a source of debate. By leveraging technology, the ISU aims to:
Improve Judging Consistency: Data analytics will be used to assess the performance and alignment of human judges.
Enhance Athlete Development: AI tools can provide detailed feedback for training.
Increase Transparency: Objective data can offer a clearer understanding of performance evaluations.
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The ongoing exploration and implementation of AI mark a pivotal moment for figure skating as it navigates the complexities of maintaining both athletic rigor and artistic integrity in a data-driven era.
Sources Used:
CGTN: https://news.cgtn.com/news/2026-02-11/International-Skating-Union-weighs-AI-s-role-in-judging—1KFKl1sSgLK/p.html - Focuses on ISU's plans for AI in judging and training, including specific system introductions in China.
MIT Technology Review: https://news.mit.edu/2026/3-questions-using-ai-help-olympic-skaters-land-quint-0210 - Explores the rationale for applying AI to measure specific athletic components of figure skating jumps and touches upon artistic evaluation.
The Strait Times: https://www.straitstimes.com/sport/figure-skating-turns-to-ai-to-tackle-judging-controversies - Highlights figure skating's move towards AI and computer vision to address judging issues and increase transparency, noting skater reception and ISU's plans.
