AI in Critical and Public Infrastructure: When Over-Automation Becomes a Societal Hazard
Healthcare, energy, transportation, and public safety under autonomous decision risk
Contributed by Thane Russey, VP, Strategic AI Programs, LCG Discovery Experts
Series context. This article is Part 6 of Beyond Automation: Why Human Judgment Remains Critical in AI Systems. The series examines how the weakening or removal of human oversight in high-stakes domains creates systemic, often invisible failure modes. This installment shifts from enterprise systems to societal infrastructure, where autonomous AI decisions can affect public safety, civil liberties, and economic stability at scale. [1]
Infrastructure AI Is Not Just Operational. It Is Societal.
Artificial intelligence is increasingly embedded in systems that regulate power distribution, manage hospital triage, optimize transportation flows, and support public safety analytics.
Unlike enterprise automation, infrastructure AI operates at population scale. Errors do not remain localized. They propagate.
The NIST AI Risk Management Framework emphasizes that higher-impact AI systems require proportionally stronger governance, transparency, and human oversight mechanisms. [2] The EU AI Act similarly classifies many infrastructure applications, including healthcare and other critical domains, as high-risk systems subject to specific obligations and controls, including human oversight expectations. [3]
The rationale is straightforward. When automation influences life-sustaining services or public rights, technical failure becomes a societal consequence.
LCG perspective. In critical infrastructure, automation risk is not measured solely in downtime or financial loss. It is measured in public harm, institutional trust erosion, and long-term governance exposure. [4]
Healthcare: Triage at Machine Speed
AI-assisted triage tools are increasingly deployed to prioritize emergency cases, allocate beds, and recommend diagnostic pathways.
These systems are designed to reduce clinician overload and standardize care. Yet peer-reviewed research has shown that algorithmic systems can embed historical bias and misallocate resources when trained on incomplete or skewed data. [5]
Common risk pathways include:
- Under-prioritization of vulnerable populations due to proxy variables
- Over-reliance on model recommendations during surge events
- Inadequate escalation protocols when AI outputs conflict with clinical intuition
- Limited transparency in the decision rationale for the frontline review
Regulatory expectations are also moving toward lifecycle accountability. FDA materials increasingly emphasize ongoing monitoring and lifecycle management concepts for AI-enabled device software functions, particularly when models evolve post-deployment. [6]
Human clinicians provide contextual reasoning that integrates patient history, non-verbal cues, and emerging symptoms. Autonomous systems optimize for structured inputs. They do not independently recognize when context has shifted beyond their training assumptions.
In healthcare, delayed human intervention can mean irreversible harm.
Energy and Grid Management: Optimization Versus Stability
Electric grids increasingly use AI-driven demand forecasting and load balancing to improve efficiency and reduce outage frequency.
Optimization models are highly effective under normal conditions. However, grid infrastructure is vulnerable to cascading failure when predictive systems miscalculate during extreme weather events, cyber intrusion, or equipment degradation.
Critical infrastructure doctrine emphasizes layered resilience and partnership-based risk management across sectors. [7] Automated balancing systems that initiate rapid shutdown or rerouting actions without contextual evaluation can inadvertently destabilize interconnected systems.
Practical hazards include:
- Over-correction responses that propagate instability
- Automated isolation of grid segments serving hospitals or emergency services
- Inadequate detection of coordinated cyber-physical attacks
- Model drift under climate variability and changing demand patterns
Human operators interpret broader system signals, cross-sector dependencies, and real-world constraints that automated optimization cannot reliably model at population scale.
Transportation Systems: Efficiency Without Escalation Paths
AI optimizes traffic flows, rail signaling, and logistics coordination. In aviation and autonomous vehicle ecosystems, algorithmic systems assist with navigation and hazard detection.
Transportation guidance for automated driving systems has repeatedly stressed the importance of safety-oriented design, operational discipline, and appropriate oversight, particularly for edge cases and ambiguous environments. [8]
Failure modes in transportation AI often include:
- Misclassification of rare but critical obstacles
- Inadequate response to sensor degradation
- Overconfidence under atypical weather or road conditions
- Delayed human override when escalation protocols are unclear
Transportation failures are not abstract. They produce immediate physical harm.
Machine learning systems generalize from prior data. They struggle with rare events, precisely the scenarios where human judgment is most valuable.
Public Safety Analytics and Predictive Systems
AI-driven public safety tools analyze patterns, predict resource allocation needs, and flag anomalous behavior.
These systems operate within constitutional, civil rights, and evidentiary boundaries. When deployed without human validation, they risk amplifying bias, misdirecting investigations, and eroding community trust.
DOJ analysis on AI in criminal justice highlights persistent operational, ethical, and civil rights considerations that accompany AI adoption in justice contexts. [9] In predictive or analytic deployments, risk concentrates in three areas:
- Feedback loops that reinforce historical enforcement disparities
- Opaque scoring that influences prioritization without an explainable basis
- Over-escalation of ambiguous signals into high-impact interventions
Human oversight is necessary not only for accuracy but for legitimacy.
Public infrastructure decisions require democratic accountability. Autonomous analytics without clear review mechanisms undermines that accountability.
Population Scale Consequences of Autonomous Error
Infrastructure AI failures differ from enterprise errors in magnitude and velocity.
At scale, failure patterns include:
- Simultaneous impact across multiple geographic regions
- Compounded errors due to interdependent systems
- Loss of public confidence in institutional competence
- Regulatory and legislative backlash following high-visibility harm
In interconnected environments, automation errors cascade. A miscalculation in grid management can affect hospitals. A triage failure can overwhelm emergency transport. A transportation outage can disrupt supply chains and emergency response.
Resilience requires friction. Human review introduces a deliberate pause before irreversible action.
Transparency, Accountability, and Escalation Models
Effective infrastructure AI governance includes:
- Explicit human escalation thresholds for high-impact decisions
- Independent validation under stress and rare-event scenarios
- Documented oversight and override authority
- Cross-agency coordination for incident response
- Lifecycle monitoring for drift, degradation, and unintended impacts
ISO/IEC 23894 provides structured guidance for AI risk management across lifecycle stages, emphasizing governance, integration with organizational risk practices, and ongoing monitoring. [10]
Critical infrastructure operators should be able to demonstrate:
- Who authorized autonomous deployment
- Under what constraints autonomy operates
- How override authority is exercised
- How drift is detected and corrected
- How public harm is assessed and remediated post-incident
These are governance requirements, not purely technical safeguards.
Quick Checklist
- Define explicit human escalation triggers for all high-impact infrastructure AI decisions.
- Conduct stress testing that simulates rare and extreme edge cases.
- Document oversight, override authority, and lifecycle monitoring in alignment with AI risk standards. [2][10]
Final thought
Infrastructure AI promises efficiency, predictive insight, and operational precision. Those benefits are real.
So is the hazard of unbounded autonomy.
In healthcare, energy, transportation, and public safety, automation errors are not isolated system defects. They are societal events.
Human judgment is not an outdated artifact of slower systems. It is the stabilizing force that prevents optimization from becoming destabilization.
In critical infrastructure, the question is not whether AI should assist decision-making. It is whether we are willing to retain the human authority necessary to contain its inevitable limits.
References (endnotes)
[1] Beyond Automation: Why Human Judgment Remains Critical in AI Systems (Series outline, internal working document).
[2] NIST, Artificial Intelligence Risk Management Framework (AI RMF 1.0) (NIST.AI.100-1, January 2023). https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf (NIST Publications)
[3] European Union, Regulation (EU) 2024/1689 (Artificial Intelligence Act) (Official Journal, July 12, 2024). https://eur-lex.europa.eu/eli/reg/2024/1689/oj/eng (EUR-Lex)
[4] OECD, How are AI developers managing risks? (Report page). https://www.oecd.org/en/publications/how-are-ai-developers-managing-risks_658c2ad6-en.html (OECD)
[5] Obermeyer, Z., Powers, B., Vogeli, C., Mullainathan, S., Dissecting racial bias in an algorithm used to manage the health of populations (Science, 2019). https://www.science.org/doi/pdf/10.1126/science.aax2342 (Science)
[6] U.S. Food and Drug Administration, Artificial Intelligence-Enabled Medical Devices (FDA page). https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-enabled-medical-devices (U.S. Food and Drug Administration)
[7] U.S. Department of Homeland Security, NIPP 2013: Partnering for Critical Infrastructure Security and Resilience (PDF). https://www.cisa.gov/sites/default/files/2022-11/national-infrastructure-protection-plan-2013-508.pdf (CISA)
[8] National Highway Traffic Safety Administration, Automated Driving Systems 2.0: A Vision for Safety (PDF). https://www.nhtsa.gov/sites/nhtsa.gov/files/documents/13069a-ads2.0_090617_v9a_tag.pdf (NHTSA)
[9] U.S. Department of Justice, Office of Legal Policy, Artificial Intelligence and Criminal Justice (Report PDF). https://www.justice.gov/olp/media/1381796/dl?inline= (Department of Justice)
[10] ISO/IEC, ISO/IEC 23894:2023 AI, Guidance on risk management (standard overview page). https://www.iso.org/standard/77304.html (ISO)
This article is for general information and does not constitute legal advice.
