AI Output Failures: Mistakes, Hallucinations, Misuse

When an artificial intelligence system generates an output that is factually incorrect, fabricates non-existent data, or is utilised in a manner contrary to its intended purpose, the resulting event is rarely a simple technical glitch. In the context of European regulation, such occurrences—colloquially known as hallucinations, mistakes, or misuse—trigger a complex interplay of liability, compliance obligations, and risk management duties. For professionals deploying high-risk AI systems in sectors ranging from medical diagnostics to financial services, understanding the taxonomy of these failures is not merely an academic exercise; it is a prerequisite for navigating the European Union’s AI Act (AI Act) and the evolving landscape of civil liability.

The distinction between a model error and a system failure is critical. A model error, such as a statistical hallucination in a Large Language Model (LLM), is a technical artifact. However, when that model is integrated into a regulated workflow—such as drafting legal summaries or analyzing medical scans—the error transforms into a regulatory event. The AI Act, which entered into force on 1 August 2024, shifts the focus from the technology itself to the risk inherent in its application. Consequently, teams must move beyond debugging code to establishing governance frameworks that anticipate, classify, and mitigate these failures.

The Anatomy of AI Output Failures

To apply regulatory frameworks effectively, one must first dissect the nature of the failure. In professional practice, we observe three primary categories of output dysfunction: factual errors (hallucinations), logical mistakes, and misuse. While these often overlap, they carry distinct implications for risk classification and mitigation strategies.

Hallucinations and Fabrication

Hallucinations occur when a model generates plausible-sounding but factually incorrect information that is not grounded in the input data or reality. In generative AI, this might manifest as citing non-existent legal precedents or inventing biographical details. Under the AI Act, the severity of this failure depends entirely on the context. A hallucination in a creative writing assistant is a consumer annoyance; the same hallucination in an AI system summarizing patient history for a clinician constitutes a severe safety risk.

From a liability perspective, hallucinations challenge the traditional concept of “defect.” A product is usually considered defective if it deviates from its intended performance. However, if the model’s “intended performance” includes a known non-zero rate of fabrication (a statistical inevitability in probabilistic models), the legal question becomes: Did the provider know, or should they have known, that the hallucination rate was unacceptable for the specific high-risk context?

Logical and Contextual Mistakes

Distinct from pure fabrication, mistakes involve the correct processing of data but the application of flawed logic or context. For instance, an AI system might correctly identify a tumor in a scan but misclassify its malignancy due to a failure in integrating patient history data. In the context of the Product Liability Directive (PLD), which is currently being revised to explicitly cover AI, this points to a design defect. The system, while technically operational, failed to perform as safely as a person with ordinary knowledge would expect, given the legitimate expectations of the safety and performance of high-risk products.

Misuse and Unintended Application

Misuse represents a failure of the human-machine interface. It occurs when a user employs a system for a purpose outside its designated scope. The AI Act places significant emphasis on the duty of the provider to mitigate foreseeable misuse through design and instructions. However, the duty of the deployer (the entity using the AI) is equally weighty. Deployers must adhere to the intended purpose defined by the provider.

“Foreseeable misuse shall be taken into account when assessing the risks of high-risk AI systems. This includes the use of the system in a context that is not intended by the provider but which is reasonably likely to occur.”

If a hospital uses a triage AI intended for administrative sorting to make clinical diagnoses, this is misuse. In the event of harm, liability may shift heavily toward the deployer for overriding safety controls or failing to perform their own risk assessment.

Regulatory Classification: The AI Act’s Risk-Based Approach

The AI Act categorises AI systems based on the level of risk they pose to health, safety, and fundamental rights. The classification of an output failure is directly tied to the risk category of the system in which it occurs.

Unacceptable Risk (Prohibited Practices)

Systems that manipulate human behavior to circumvent free will or exploit vulnerabilities are prohibited. Output failures here are less about “mistakes” and more about the inherent design. However, a system that is intended to be “high-risk” but drifts into prohibited territory (e.g., social scoring) due to a failure in its output logic may face immediate enforcement action by national authorities.

High-Risk Systems (Annex III)

This is the core regulatory domain for most enterprise AI. Systems used in critical infrastructure, education, employment, essential services, law enforcement, and migration are classified as high-risk. Any output failure in these systems is a compliance event.

Under Article 8 of the AI Act, high-risk AI systems must be subject to a risk management system throughout their lifecycle. If a hallucination occurs in a high-risk system (e.g., an AI assessing creditworthiness), it is not merely a bug; it is a failure of the risk management system to ensure appropriate levels of accuracy, robustness, and cybersecurity.

Transparency Risk (Generative AI)

General Purpose AI (GPAI) models, including LLMs, fall largely into the transparency category. The obligation here is not necessarily to prevent every error, but to disclose that the content is AI-generated and to ensure copyright compliance. However, if a GPAI model is deemed to present a systemic risk (a designation based on computing power and impact), it falls under stricter obligations, including adversarial testing (red-teaming) to identify and mitigate potential output failures before market release.

Liability Frameworks: Who Pays for the Mistake?

Regulation dictates compliance, but liability dictates compensation. The European landscape is shifting from a provider-centric liability model to a more nuanced ecosystem involving deployers and model owners.

The AI Liability Directive (AILD) and the Revised PLD

The proposed AI Liability Directive (AILD) works in tandem with the revised Product Liability Directive (PLD). The PLD covers compensation for damage caused by a defective product. The AILD harmonizes rules for non-contractual damage caused by AI systems, specifically addressing the difficulties of proving fault.

A key mechanism introduced is the presumption of causality. If a claimant can demonstrate that an AI system caused the damage and that the provider failed to meet certain obligations (such as conducting a conformity assessment or maintaining logs), the burden of proof may shift. This means that an unexplained hallucination or mistake in a high-risk system could automatically trigger a presumption of fault against the provider, forcing them to prove they were not negligent.

Distinguishing Provider vs. Deployer Liability

When an output failure leads to damage, the investigation will focus on the “chain of custody” of the decision-making process:

• Provider Liability: Stems from design flaws, inadequate training data, or failure to implement accuracy metrics. If the model is inherently prone to hallucinations in a way that is incompatible with the declared intended purpose, the provider is liable.
• Deployer Liability: Stems from operational negligence. If a deployer ignores the instructions for use, fails to monitor the system’s performance (human oversight), or inputs biased data that leads to a mistake, they bear responsibility.

For example, if a bank uses an AI for loan approvals and the AI hallucinates a credit score, the bank (deployer) might be liable for acting on the output without verification, while the software vendor (provider) might be liable for the underlying model instability.

Incident Classification and Response Protocols

To manage these risks, organizations must implement a rigorous incident classification system. This is not just IT incident management; it is regulatory compliance management. The goal is to ensure consistent response and to generate the evidence required by regulators.

Classifying Severity: The “Risk Matrix” Approach

Teams should classify AI output failures based on two vectors: Impact (harm to rights, safety, or business) and Probability (likelihood of recurrence or escalation).

Level 1: Anomalies

Definition: Minor deviations in output quality that do not affect the final decision-making process (e.g., stylistic errors in generated text).

Response: Logged for model tuning; no regulatory reporting required.

Level 2: Operational Failures

Definition: Errors that disrupt business processes but cause no direct harm to individuals (e.g., a chatbot providing incorrect store hours).

Response: Internal investigation; update of risk management file; potential notification to the provider if the system is not performing as intended.

Level 3: High-Risk System Incidents

Definition: An output failure in a high-risk system that results in a breach of fundamental rights, safety, or significant financial loss (e.g., a recruitment AI systematically rejecting candidates based on hallucinated criteria).

Response: Immediate cessation of use. Under the AI Act, serious incidents must be reported to the relevant national notifying authority. Article 73 mandates reporting of serious incidents within 15 days of becoming aware of them.

The “Near Miss” Concept

Regulators are increasingly interested in “near misses”—instances where an output failure occurred but was caught by human oversight before causing harm. Treating near misses with the same rigor as actual incidents is a hallmark of a mature safety culture. Analyzing near misses helps satisfy the requirement for continuous improvement of the risk management system.

Mitigation Strategies: From Technical to Governance

Mitigating AI output failures requires a layered defense strategy. Relying solely on post-processing filters is insufficient under the AI Act’s requirement for “high accuracy” and “robustness.”

Technical Mitigations

At the model level, mitigation involves:

• Retrieval-Augmented Generation (RAG): Grounding LLM outputs in verified, external knowledge bases to reduce hallucinations.
• Adversarial Training: Exposing models to edge cases and misuse scenarios during training to improve resilience.
• Uncertainty Quantification: Programming systems to express confidence scores. If a model is 60% confident in a medical diagnosis, the system must flag it for human review.

Organizational Mitigations

Process-level mitigation is where compliance becomes tangible:

• Human-in-the-Loop (HITL): For high-risk systems, the AI Act mandates effective human oversight. This means the human operator must have the competence to intervene and the authority to override the system. A “rubber stamp” interface where the human blindly approves AI suggestions is a regulatory violation.
• Post-Market Monitoring: Providers must establish a system for systematically collecting data on the performance of their AI systems in the real world. This data is the primary source for identifying recurring hallucinations or mistakes.
• Data Governance: Ensuring the data used to train and fine-tune models is representative and free from biases that could lead to discriminatory outputs.

Documentation as a Shield

In the event of an investigation, the Technical Documentation required by Annex IV of the AI Act is the provider’s primary defense. It must demonstrate:

• The capabilities and limitations of the system (including known tendencies to hallucinate).
• The metrics used to evaluate accuracy and robustness.
• The steps taken to minimize risks from foreseeable misuse.

If an output failure occurs, but the documentation shows that the provider anticipated the risk, implemented appropriate mitigation, and the deployer failed to follow instructions, the liability landscape shifts significantly.

National Implementation and Cross-Border Nuances

While the AI Act is a Regulation (directly applicable in all Member States), its enforcement relies on national authorities. This creates a fragmented landscape where the interpretation of “output failure” may vary.

The Role of Market Surveillance Authorities

Each Member State must designate a Market Surveillance Authority (MSA). In Germany, this might involve the Federal Network Agency (BNetzA) or existing product safety bodies; in France, it is likely to be the DGCCRF. These bodies will investigate incidents. Their interpretation of whether a hallucination constitutes a “serious risk” can differ.

For example, a French authority might take a stricter view on AI in hiring than a regulator in a country aggressively pursuing digital innovation. Companies operating across borders must prepare for the strictest common denominator to ensure compliance everywhere.

GDPR Interplay

It is impossible to discuss AI output failures in Europe without mentioning the GDPR. An AI that hallucinates personal data (i.e., invents details about a real person) violates the principle of accuracy (Article 5). If an AI system makes a mistake that leads to an automated decision about a data subject (Article 22), the individual has the right to human intervention and to challenge the decision. An output failure here triggers dual liability: under the AI Act for safety and under the GDPR for data protection.

Practical Steps for Compliance Teams

For professionals tasked with implementing these frameworks, the path forward involves integrating AI risk management into existing corporate governance structures.

1. Map AI Systems to Risk Categories

Conduct an inventory of all AI systems in use. Determine if they fall under Annex III (High Risk). If a system is currently “low risk” but an output failure could elevate it to high risk (e.g., a customer service bot that starts giving financial advice), it must be re-evaluated.

2. Establish an AI Incident Response Team

This cross-functional team should include legal, IT, compliance, and domain experts. Their mandate is to classify failures as they occur. They must decide within hours whether an incident is a Level 1 anomaly or a reportable Level 3 serious incident.

3. Update Contracts with AI Vendors

Contracts must explicitly address the AI Act. The provider must warrant that the system is designed to minimize risks and must notify the deployer of any serious incidents they become aware of. The deployer must reserve the right to audit the provider’s compliance documentation.

4. Continuous Education

Users of AI systems must be trained to recognize the limitations of the technology. They must understand that an AI output is a probabilistic suggestion, not a deterministic fact. This training is part of the “human oversight” obligation.

Conclusion: The End of “Black Box” Excuses

The era of treating AI output failures as unavoidable side effects of complex technology is over. The European regulatory framework establishes a clear expectation: predictability, accountability, and safety. Whether a failure is a hallucination, a logical error, or a result of misuse, the responsibility lies with the human actors in the chain—providers to build safely, and deployers to use responsibly.

For the European professional, the classification of an incident is the pivot point upon which legal liability turns. By adopting a rigorous taxonomy of failure and embedding it into a robust risk management system, organizations can transform regulatory compliance from a burden into a competitive advantage: the assurance of reliability in an uncertain technological landscape.