AI incident ·
Driverless Train in Delhi Crashes due to Braking Failure
In brief
An AI system built by Unknown and deployed by Delhi Metro Rail Corporation allegedly harmed Delhi Metro Rail Corporation.
- Risk domain
- AI system safety, failures, and limitations
- Occurred
- Coverage
- 29 reports
What happened
A driverless metro train in Delhi, India crashed during a test run due to faulty brakes.
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 India.
- India DPDP Act
- India AI Governance Guidelines
- Law on Artificial Intelligence (2025)
- Law No. 132/2025 on artificial intelligence
- EU AI Act
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 (29)
Titles link to the original publisher; report text is not reproduced here.
Who was involved
- Alleged deployer
- Delhi Metro Rail Corporation
- Alleged developer
- Unknown
- Alleged harmed party
- Delhi Metro Rail Corporation
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
- Human
- Intent
- Unintentional
- Timing
- Pre-deployment
- Harm level
- none
- Sectors
- transportation and storage
- Countries
- IN
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 #31 on the AI Incident Database · all 29 reports