AI incident ·
AI mistakes referee’s bald head for football — hilarity ensued
In brief
An AI system built by Unknown and deployed by Inverness Caledonian Thistle Football Club allegedly harmed Livestream Viewers.
- Risk domain
- AI system safety, failures, and limitations
- Occurred
- Coverage
- 2 reports
What happened
In a Scottish soccer match the AI-enabled ball-tracking camera used to livestream the game repeatedly tracked an official’s bald head as though it were the soccer ball.
Laws that address this harm
Policy angle: Classified under AI system safety, failures, and limitations (Lack of capability or robustness) in the MIT AI Risk Repository taxonomy; 5 recorded instruments address this use case in the United Kingdom.
- UK AI regulation framework
- ICO AI guidance
- India DPDP Act
- Law on Artificial Intelligence (2025)
- Law No. 132/2025 on artificial intelligence
Matched from the record's risk domain and country to the instruments recorded here. A reviewer can correct the match in the repository (data/external/incident_overrides.yaml).
News reports (2)
Titles link to the original publisher; report text is not reproduced here.
Who was involved
- Alleged deployer
- Inverness Caledonian Thistle Football Club
- Alleged developer
- Unknown
- Alleged harmed party
- Livestream Viewers
Classification (MIT AI Risk Repository taxonomy)
- Risk domain
- AI system safety, failures, and limitations
- Risk subdomain
- 7.3 Lack of capability or robustness
- Causal entity
- AI
- Intent
- Unintentional
- Timing
- Post-deployment
- Harm level
- none
- Sectors
- arts, entertainment and recreation
- Countries
- GB
Risk entries describing this failure mode
Entries from the MIT AI Risk Repository coded to subdomain 7.3.
- Reliability issues
"Relying on general-purpose AI products that fail to fulfil their intended function can lead to harm. For example, general- purpose AI systems can make up facts (‘hallucination’), generate erroneous computer code, or pro...
- Type 2: Bigger than expected
Harm can result from AI that was not expected to have a large impact at all, such as a lab leak, a surprisingly addictive open-source product, or an unexpected repurposing of a research prototype.
- Type 3: Worse than expected
AI intended to have a large societal impact can turn out harmful by mistake, such as a popular product that creates problems and partially solves them only for its users.
- Ethics and Morality Issues
LMs need to pay more attention to universally accepted societal values at the level of ethics and morality, including the judgement of right and wrong, and its relationship with social norms and laws.
- Safe learning
"AGIs should avoid making fatal mistakes during the learning phase. Subproblems include safe exploration and distributional shift (DeepMind, OpenAI), and continual learning (Berkeley)."
- Malign belief distributions
"Christiano (2016) argues that the universal distribution M (Hutter, 2005; Solomonoff, 1964a,b, 1978) is malign. The argument is somewhat intricate, and is based on the idea that a hypothesis about the world often includ...
- Meta-cognition
"Agents that reason about their own computational resources and logically uncertain events can encounter strange paradoxes due to Godelian limitations (Fallenstein and Soares, 2015; Soares and Fallenstein, 2014, 2017) an...
- Technical and operational risks
"To date, technical limitations and vulnerabilities are present in most generative AI models in various contexts. Consequently, malicious users find it easier to breach an AI system’s safety and ethical guardrails to e...
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Source record: incident #80 on the AI Incident Database · all 2 reports