AI incident ·
Epic Systems’s Sepsis Prediction Algorithms Revealed to Have High Error Rates on Seriously Ill Patients
In brief
An AI system built by Epic Systems and deployed by University of Michigan Hospital allegedly harmed sepsis patients.
- Risk domain
- AI system safety, failures, and limitations
- Occurred
- Coverage
- 5 reports
What happened
Epic System's sepsis prediction algorithms was shown by investigators at the University of Michigan Hospital to have high rates of false positives and false negatives, allegedly delivering inaccurate and irrelevant information on patients, contrasting sharply with their published claims.
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 States.
- Texas Responsible AI Governance Act (TRAIGA)
- California SB 53
- Tennessee ELVIS Act
- EO 14179
- New York RAISE Act (frontier model safety)
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 (5)
Titles link to the original publisher; report text is not reproduced here.
Who was involved
- Alleged deployer
- University of Michigan Hospital
- Alleged developer
- Epic Systems
- Alleged harmed party
- sepsis patients
AI systems implicated
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
- AI tangible harm issue
- Sectors
- human health and social work activities
- Countries
- US
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...
Related incidents
Linked by editors or by text similarity in the source dataset.
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Source record: incident #123 on the AI Incident Database · all 5 reports