When AI Systems Fail: Accountability Beyond Technical Errors

When an artificial intelligence system produces a harmful or incorrect outcome, the immediate reaction is often to search for a technical glitch—a bug in the code, a corrupted dataset, or a model hallucination. While technical failures are certainly part of the risk profile, the European regulatory landscape is shifting the focus toward a more complex and durable concept of accountability. This accountability is not merely about fixing the code; it is about assigning responsibility across a fragmented chain of actors, from the institution deploying the system to the vendor supplying it and the operator overseeing its daily use. The European Union’s approach, crystallized in the Artificial Intelligence Act (AI Act), moves beyond the traditional negligence-based models of product liability and introduces a risk-based framework where accountability is intrinsic to the design, deployment, and governance of AI systems.

Understanding who is responsible when AI fails requires a dissection of the interplay between the AI Act, the Product Liability Directive (PLD), the GDPR, and national civil codes. It requires looking at the “economic operator” definitions within the AI Act and the specific obligations placed on providers, deployers, and affected persons. The following analysis explores how these frameworks interact to create a mesh of liability that does not easily allow any single party to claim ignorance when an AI system causes damage or violates rights.

The Architectural Shift: From Negligence to Strict Obligations

Historically, European tort law has relied on establishing a breach of a duty of care. If a software vendor acted reasonably and a user misinterpreted the output, the courts would look for negligence. The AI Act disrupts this by codifying specific obligations based on the risk level of the AI system. For “high-risk” AI systems—those used in critical infrastructure, employment, biometrics, and education—the Act imposes a proactive duty to ensure safety and fundamental rights. Accountability is not determined solely by looking backward at what went wrong; it is determined by looking forward at what the actors failed to do before the system was placed on the market or put into service.

This shift is profound. It means that a harmful outcome can be considered a violation of the Act itself, regardless of whether the specific harm was foreseeable in a traditional sense. If a high-risk AI system lacks the required risk management system, or if it was not subject to conformity assessment, the mere existence of that system in the market creates a basis for liability. The burden of proof is effectively reversed in many contexts: the operator or provider must demonstrate compliance with these rigorous obligations to shield themselves from liability.

The Definition of “AI System” and Scope of Liability

Before assigning accountability, one must determine if the technology falls within the scope of the AI Act. The definition is functional, focusing on “machine-based systems designed to operate with varying levels of autonomy” that “infer how to achieve a given set of objectives using machine learning and/or logic-based approaches.” This broad definition captures everything from simple algorithmic decision-making tools to complex generative models.

Crucially, the AI Act applies to providers placing AI systems on the EU market, regardless of where they are located. This extraterritorial reach ensures that a US-based tech giant selling software to a German hospital is just as accountable as a local startup. If the AI fails, the location of the developer is irrelevant to the enforcement of EU standards.

Mapping the Accountability Chain: The Economic Operators

The AI Act mirrors the structure of the EU’s product safety legislation by defining specific roles, or “economic operators.” When an AI system fails, the first step in a regulatory or legal investigation is to identify which of these actors failed in their specific duties.

The Provider: The Burden of Upstream Design

The provider (the entity developing the AI system or having it developed and marketing it under its own name) bears the heaviest burden of accountability. Their responsibility begins at the drawing board and extends through the entire lifecycle of the product.

If an AI system fails, the provider is the primary target for regulatory action if they failed to:

• Establish a robust risk management system that identifies, estimates, and mitigates risks to health, safety, and fundamental rights.
• Ensure data governance practices that prevent bias and ensure training data is relevant, representative, and free of errors.
• Draw up technical documentation demonstrating compliance before the system is put into service.
• Design the system to enable human oversight.
• Implement a quality management system.

For General Purpose AI (GPAI) models, the accountability shifts slightly. The provider of the model must ensure transparency regarding the data used for training and comply with copyright laws. If a downstream application fails because the underlying GPAI model was inherently unsafe or lacked proper safeguards, the original model provider shares in the liability, particularly if they failed to mitigate systemic risks.

The Deployer: The Duty of Downstream Governance

The deployer (the entity using the AI system in a professional capacity) is the “user” in the chain. While they did not design the system, they are accountable for how it is used. A common misconception is that deploying a certified high-risk AI system absolves the user of responsibility. This is incorrect.

Accountability for the deployer arises from:

• Human Oversight: The deployer must ensure that human operators are adequately trained and have the authority to override the system. If an AI system recommends a discriminatory hiring decision and a human rubber-stamps it without review, the deployer is liable for that discrimination under GDPR and national equality laws.
• Intended Purpose: Deployers must use the system only for its intended purpose. If a company uses a biometric identification system intended for security access to monitor employee productivity, they have deviated from the intended purpose, assuming full liability for any resulting harm.
• Monitoring: Deployers must monitor the AI system for “concrete risks” or “anomalies.” If the system starts behaving erratically due to data drift, the deployer has a duty to stop using it and report the issue to the provider.

The Distributor and Importer: The Gatekeepers

Importers and distributors are often overlooked in discussions of AI accountability, yet they serve as critical checkpoints. An importer must verify that the provider outside the EU has complied with the AI Act before placing the system on the EU market. If an AI system fails because it lacked the required CE marking or technical documentation, the importer is held accountable alongside the foreign provider. This prevents the “race to the bottom” where non-compliant AI systems are simply routed through less regulated jurisdictions.

Product Liability: The Revised Framework for AI Harm

While the AI Act sets out regulatory obligations, the Revised Product Liability Directive (PLD) (Directive (EU) 2024/… ) provides the civil law mechanism for victims to seek compensation. The PLD explicitly adapts traditional product liability concepts to the realities of AI and digital manufacturing.

Under the PLD, a “product” includes software, including AI systems. Accountability is strict: the victim does not need to prove the manufacturer was negligent, only that the product was defective and that the defect caused the damage.

Defectiveness in AI: The “Defect” Redefined

Identifying a “defect” in AI is more complex than in physical goods. The PLD introduces criteria that are highly relevant to AI failures:

• The expectations of the end-user: Would a user reasonably expect an AI system to behave in this way? If an AI chatbot provides dangerous medical advice, it is defective, even if it was technically “hallucinating” based on its training data.
• The effect of not including features: A product can be considered defective if it lacks safety features that should have been included. For AI, this could mean the lack of an “off-switch” or explainability features.
• The state of the art: This is a critical defense for providers. If the AI system was in compliance with the state of the art at the time it was placed on the market, it might not be considered defective. However, “state of the art” in AI moves rapidly. A system that was compliant last year might be defective today if the provider fails to update it to address new vulnerabilities.

The Burden of Proof and Disclosure

One of the greatest hurdles for victims of AI harm is proving causality. How does a claimant prove that a specific neural network caused a specific financial loss or physical injury? The revised PLD includes provisions to ease this burden. If a claimant can demonstrate that the AI system failed to comply with regulatory obligations (such as the AI Act) and that this non-compliance likely caused the defect, the burden of proof may shift to the producer.

Furthermore, courts now have the power to order the disclosure of evidence from providers and deployers. This is vital. A victim cannot be expected to reverse-engineer a proprietary deep learning model to prove defectiveness. If a court orders disclosure and the provider fails to comply, the court may presume the AI system was defective.

GDPR and Fundamental Rights: Accountability for Non-Material Harm

When AI systems fail, the harm is not always physical or financial. Automated decision-making can lead to reputational damage, psychological distress, or discrimination. In these cases, accountability is enforced through the General Data Protection Regulation (GDPR).

Article 22 of the GDPR grants data subjects the right not to be subject to a decision based solely on automated processing, including profiling, which produces legal effects concerning them or similarly significantly affects them. If an AI system denies a loan or a visa without meaningful human intervention, the deploying institution is accountable for violating this right.

Moreover, the “right to an explanation” (Articles 13, 14, and 15) requires controllers to provide meaningful information about the logic involved. If an AI system fails to provide this explainability—often the case with “black box” models—and a user suffers harm as a result, the failure to provide transparency is itself a breach of the GDPR, compounding the liability.

National Data Protection Authorities (DPAs) are the enforcers here. Their approach varies. The French CNIL has been proactive in auditing algorithmic systems used by the public administration, while the Irish DPC focuses heavily on the compliance of big tech firms. This divergence means that a company operating across Europe might face different scrutiny levels depending on where its “main establishment” is.

Operationalizing Accountability: The Role of the Operator

In the context of the AI Act, the term “operator” is often used generically to refer to anyone involved in the supply chain. However, in practical terms, the “operator” is the human or entity in direct control of the system’s functioning. Accountability for operators is defined by the level of control they exercise.

The “Human in the Loop” Fallacy

Regulators are increasingly skeptical of the “human in the loop” defense if that human is merely a passive observer. Accountability requires meaningful human oversight. This means the human must have the competence, training, and authority to interpret the AI’s output and override it.

If a system is designed so that the human operator is pressured to accept the AI’s recommendation due to speed or volume of work, the “human oversight” requirement is technically met but practically void. In such a failure scenario, the deployer (the employer) is accountable for creating an operational environment that negates safety measures.

Incident Reporting and Feedback Loops

Accountability is not static; it is dynamic. The AI Act mandates that serious incidents be reported to national authorities. This creates a regulatory feedback loop. If an operator fails to report a near-miss or a malfunction, they are liable for that omission, separate from the original failure.

For example, if a robotic surgical arm malfunctions in a hospital, the hospital (deployer) must report it. If they fix the machine locally but fail to report the systemic issue to the manufacturer and the regulator, they prevent other hospitals from mitigating the risk. This failure of reporting constitutes a breach of regulatory duty.

National Implementation and Cross-Border Enforcement

While the AI Act is a Regulation (meaning it is directly applicable in all Member States without needing to be transposed into national law), its enforcement relies on national authorities. This creates a patchwork of enforcement capabilities and interpretations.

The Role of Market Surveillance Authorities

Each Member State must designate a Market Surveillance Authority (MSA). In some countries, this might be the same body that enforces product safety (e.g., the Health and Safety Executive in the UK, though post-Brexit, or the Federal Institute for Drugs and Medical Devices in Germany). In others, it might be a dedicated digital regulator.

If an AI system fails in a cross-border context—for instance, a Dutch company using a French AI provider’s software to make decisions about German citizens—the enforcement becomes complex. The MSA in the country where the harm occurred generally has jurisdiction, but they must cooperate with the MSA in the provider’s country. This requires a level of administrative coordination that is still being tested.

Divergence in Civil Liability

While the PLD harmonizes the basis for liability, national civil procedure laws still apply. This leads to variations in how easily a victim can obtain compensation.

• Germany: Has a strong tradition of strict liability for products. German courts are likely to be rigorous in demanding technical documentation from AI providers.
• France: Emphasizes the “right to explanation” and data protection, potentially making GDPR violations a primary avenue for AI accountability cases.
• Ireland: As a hub for tech headquarters, its courts may see a high volume of complex litigation regarding IP and liability, potentially setting precedents for the EU.

Professionals must therefore not only comply with the EU framework but also understand the specific litigation environment in the jurisdictions where they operate.

Managing Accountability: Practical Steps for Institutions

For professionals working with AI, the theoretical framework of accountability must translate into operational reality. The following practices are becoming the standard for demonstrating compliance and managing risk.

1. The AI Supply Chain Audit

Institutions can no longer treat AI vendors as standard software suppliers. When procuring an AI system, the deployer must verify the provider’s compliance. This includes requesting:

• The EU Declaration of Conformity.
• Technical documentation (specifically the risk management file).
• Information on the data governance practices used.

If a deployer buys a non-compliant AI system and uses it, both the provider and the deployer may be held liable. The deployer cannot claim ignorance if they failed to perform due diligence.

2. Continuous Monitoring and Drift Detection

AI systems are not static. A model trained on data from 2023 may behave unpredictably in 2025 due to changes in the real world (data drift). Accountability requires continuous monitoring. Organizations must implement systems to track model performance and detect when the “ground truth” changes. If a system starts exhibiting bias, the deployer must have a protocol to suspend its use immediately.

3. Documentation as a Shield

In the event of a failure, the first question a regulator will ask is: “Show me your documentation.” A lack of documentation is often treated as evidence of non-compliance. Organizations must maintain logs of:

• System updates and versioning.
• Human oversight decisions (e.g., logs showing when an operator overrode an AI recommendation).
• Incident reports and remediation actions.

This documentation serves as the primary evidence that the organization exercised due diligence and adhered to the principle of accountability.

The Future of Accountability: Emerging Challenges

As AI capabilities evolve, the boundaries of accountability are being tested. Two specific areas are currently stretching the regulatory frameworks.

Generative AI and Deepfakes

When a generative AI model produces a “deepfake” that causes reputational harm or fraud, accountability is diffuse. The user who prompted the model, the provider who trained it, and the platform hosting it all play a role. The AI Act attempts to address this by requiring watermarking and transparency for AI-generated content. However, if a system fails to watermark content and that content causes harm, the provider is accountable for the lack of transparency. If a user maliciously bypasses safeguards to create harmful content, the user is criminally liable, but the provider may still face regulatory fines if their safeguards were deemed inadequate.

Autonomous Agents

Looking further ahead, the rise of autonomous agents—AI systems that can take actions in the real world without direct human prompting—poses a challenge to the concept of “control.” If an autonomous agent makes a series of decisions that lead to a market crash or a physical accident, pinning accountability on a specific human operator becomes difficult. The regulatory consensus is moving toward treating the entity that deployed the agent as the responsible party, effectively applying a form of enterprise liability. The risk management obligations of the AI Act will likely be interpreted to require “kill switches” and strict boundaries for autonomous agents to ensure human accountability is never fully severed.

Conclusion on Institutional Responsibility

Ultimately, accountability for AI failures is an institutional discipline, not just a technical fix. It requires a culture where safety and rights are prioritized over performance metrics. The European regulatory ecosystem—from the AI Act to the GDPR and the PLD—creates a web of liability that ensures that if an AI system fails, someone is always responsible. Whether it is the provider who failed to ensure data quality, the deployer who failed to exercise meaningful oversight, or the importer who failed to check compliance, the law is designed to prevent the “accountability gap” that has plagued previous technological revolutions.

For professionals, the message is clear: accountability must be engineered into the AI lifecycle. It requires legal expertise to navigate the regulations,