Who Owns the Blame When Machines Get It Wrong?

Who Owns the Blame When Machines Get It Wrong?

Artificial intelligence has rapidly shifted from a futuristic curiosity to an invisible engine powering everyday decisions in business, law, media, and design. Yet amid this transformation, one truth remains unchanged: AI has no intent, no consciousness, and no moral agency. It is software. It does not understand what it is doing. When an algorithm goes off-course, producing false, misleading, biased, or simply bad outputs, the blame cannot be assigned to the code itself. Responsibility rests squarely with the human beings who design, deploy, or rely on it.

The rise of recent AI-related mishaps illustrates this with striking clarity. Fantasy romance writer Lena McDonald inserted AI-generated paragraphs into her book without reviewing them, revealing a complete absence of professional oversight. Syndicated columnist Marco Buscaglia used AI to create a summer reading list that included books and authors that did not exist. In a more serious example, Canadian lawyer Chong Ke submitted fabricated case law generated by ChatGPT in court and was penalised by the judge. In every instance, the failure was not technological but human, a careless willingness to trust AI outputs without the basic diligence of verification.

This raises a critical question that regulators, lawyers, compliance officers and policymakers are now forced to confront: when AI systems get it wrong, who should be held accountable?

Three Dimensions of Creating Ethical and Accountable AI:

Building accountability in AI requires organisations to focus on three interconnected dimensions: the system's functionality, the quality of the data it relies on, and the way the technology is ultimately utilised.

The first dimension is functional performance. Most AI tools, whether powered by machine learning, NLP, or other models, analyse data to generate predictions or decisions. For those outputs to be accurate, fair, and legally sound, every component of the system must work as intended. A credit card fraud detection model, for example, must be calibrated precisely enough to catch unauthorised transactions without wrongly flagging legitimate but unusual purchases. If the system misses fraud or produces too many false alarms, customers quickly lose confidence in it.

The second dimension involves data quality and data protection. AI performs best when it is trained and operated on large volumes of clean, representative, and unbiased data. Poor-quality or skewed data inevitably leads to flawed results. Additionally, organizations must ensure that this data is secure and protected from misuse or unauthorised access.

The third dimension concerns preventing unintended bias and misuse. Bias can emerge both from the datasets that feed the model and from the algorithms themselves. Amazon’s hiring tool is a well-known example: it was trained on a decade’s worth of résumés, most of which came from men. As a result, the model learned to favor male applicants, reflecting historical patterns rather than fair hiring practices, an issue that could have led to serious legal consequences if it had gone undetected.

Accountability also requires ensuring that AI systems are not repurposed in contexts for which they were never designed. A model built to optimize wheat crop yields, for instance, cannot be directly applied to rice farming without producing misleading or inaccurate guidance.

Organizations that pay close attention to these three areas, and the complexities within each, are better positioned to develop AI systems that deliver accurate, equitable outcomes. They are also more likely to detect early signs of malfunction, demonstrating a stronger commitment to responsible and accountable AI development.

The Role of Law and Internal Policies in Ensuring AI Accountability:

Ensuring accountability in artificial intelligence is not the responsibility of a single entity; it demands coordinated action through strong legal frameworks and well-designed organisational policies.

• Legislation: AI operates within a rapidly changing technological and legal environment, clear and adaptive laws are essential. Legislation provides the foundational rules that govern how AI should be developed, deployed, and monitored. It protects the public by clarifying the duties of all stakeholders and outlining consequences for violations. As AI capabilities advance, the law must evolve alongside it to remain effective and relevant.
• Company Policies: While legislation sets the broad standards, internal policies translate those standards into day-to-day practice. These policies must comply with legal norms but also address organisation-specific needs, detailing procedures, responsibilities, and safeguards for AI use. Strong internal frameworks promote responsible behaviour, guide employees, and ensure preparedness for any issues arising from AI systems.

Together, legislation and company policies create the structural foundation for meaningful AI accountability. As AI becomes more deeply embedded in business and society, cooperation between regulators and organisations will be crucial in building systems grounded in responsibility, ethical practices, and public trust.

Global Legal Approaches to AI Accountability:

Countries are taking different paths to regulate AI and assign responsibility when systems cause harm.

• United Kingdom: The UK relies on existing laws and sector-specific updates rather than a single AI statute. The 2023 AI White Paper sets out five principles, safety, transparency, fairness, accountability, and contestability, for regulators to apply within current frameworks. The Automated and Electric Vehicles Act 2018 makes insurers liable for accidents caused by self-driving cars, with insurers able to recover costs from manufacturers if the autonomous system was at fault. Proposed reforms (linked to the Automated Vehicles Act 2024) would shift liability to an “authorised self-driving entity” when a vehicle is in full autonomous mode. Outside transport, product liability under the Consumer Protection Act 1987 and general negligence law continue to govern AI-related harm.
• European Union: The EU has adopted a comprehensive model centred on the EU AI Act, which classifies systems by risk and imposes strict duties on high-risk applications, including transparency, documentation, and human oversight. The updated Product Liability Directive (2024/2853) expands “product” to include AI software and makes it easier for claimants to challenge opaque or complex systems. Although the proposed AI Liability Directive did not pass, the combination of existing tort laws, the AI Act, and updated product liability rules creates one of the strongest accountability regimes globally.
• United States: The US has no unified federal AI liability law; instead, AI disputes are handled through negligence, product liability, and consumer protection rules at the state level. Courts assessing autonomous vehicle crashes often examine whether companies overstated system capabilities or failed to implement reasonable safety features. Tesla has faced multiple lawsuits on these grounds. Sector-specific regulators, such as the NHTSA, FDA, and SEC, oversee autonomous vehicles, medical AI, and algorithmic trading. While federal guidance like the “AI Bill of Rights” and NIST’s AI Risk Management Framework sets out best practices, it is not legally binding.
• Cross-cutting legal themes: Negligence, strict product liability, and foreseeability remain central doctrines. Yet AI challenges traditional ideas of causation because systems may evolve after deployment, making fault difficult to pinpoint. Many jurisdictions also still lack clarity on whether stand-alone software counts as a “product,” creating gaps in liability. A continuing debate concerns who is best placed to prevent harm: developers, deployers, leadership, compliance teams, cybersecurity experts, or end-users.

Understanding Neuro-Symbolic Integration:

A practical approach to ensuring accountability starts with strong internal governance. Organisations should establish oversight bodies made up of legal, technical, compliance, ethics, and risk professionals who can assess AI systems before deployment. Such reviews help uncover biased training data, security weaknesses, and operational risks, while also ensuring that clear intervention measures exist if the system behaves unpredictably.

Clear contractual terms also play an important role. Companies acquiring AI tools from external vendors can negotiate warranties, indemnities, and obligations to fix vulnerabilities promptly. Although courts may still scrutinise liability waivers in cases of serious harm, well-drafted agreements define each party’s responsibilities and create a record of who controlled various stages of the AI lifecycle.

Insurance offers another layer of protection. Traditional product liability or cyber insurance can be expanded to cover autonomous software, data manipulation, or high-risk AI environments such as surgical robotics or algorithmic trading. Insurers, in turn, typically require detailed risk assessments, pushing organisations towards safer design, stronger testing, and greater transparency. Excessive premiums may also discourage unsafe AI applications unless risk-mitigation measures are improved.

Organisational culture is equally important. If businesses prioritise speed over safety, employees may skip audits or testing, increasing the likelihood of harm. By contrast, companies that foster a “safety first” or “ethics first” mindset encourage early detection of problems. Rewarding staff for reporting vulnerabilities or ethical concerns builds a culture of shared responsibility rather than blame-shifting after failures occur.

Since machine-learning models often drift or degrade as external conditions change, regular monitoring and updates are essential. Such audits not only reduce risk but also demonstrate due diligence if the organisation later faces legal challenges. Logging systems and explainability tools assist in tracing AI decisions and identifying root causes, functioning much like a “black box” for investigations.

Finally, education and cross-disciplinary collaboration are becoming indispensable. Lawyers must gain basic technical literacy, while developers need awareness of legal and ethical obligations. Although explainable AI can offer insight into how models reach decisions, it remains imperfect; explanations may reveal surface-level influences without exposing deeper design flaws or systemic pressures. The inherent opacity of modern machine learning means that perfect clarity, and perfect blame attribution, will remain difficult, underscoring the need for layered safeguards across technical, legal, and organisational domains.

Conclusion:s

As AI systems increasingly influence decisions in finance, healthcare, employment, and governance, the question of accountability can no longer remain unresolved. When machines get it wrong, the error is rarely mechanical; it reflects choices made by developers, data curators, deployers, and regulators. Assigning blame to a system that cannot comprehend responsibility only obscures the real human actors who shape its behaviour. A mature accountability framework must therefore combine transparent design, auditable decision-making processes, and clearly allocated legal duties across the AI pipeline. Ultimately, safeguarding society from AI-driven harms requires recognising that while machines may execute decisions, humans remain answerable for their construction, oversight, and consequences.

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