Introduction
As organisations deploy AI that makes or materially influences decisions affecting individuals and businesses, the question of who bears legal responsibility becomes central. This article offers a structured, comparative approach to allocating liability in AI-driven decision-making, with practical implications for counsel advising clients in Türkiye. The analysis is intentionally pragmatic: it distinguishes routes to liability, identifies evidentiary and remedial challenges, and sets out concrete risk-reduction measures.
Legal pathways to liability
Four principal legal routes typically govern wrongful outcomes from AI systems:
- Contractual allocation – parties can contractually assign responsibilities, warranties and limits of liability for AI performance and defects.
- Product liability and safety regimes – where an AI product causes damage, manufacturers or distributors may face strict or fault-based liability depending on jurisdictional rules.
- Negligence/tort law – a claimant may allege breach of a duty of care in designing, deploying or supervising AI, requiring proof of foreseeability and causation.
- Regulatory enforcement – regulators may impose administrative sanctions for breaches of data protection, consumer protection or sector-specific rules (for example, financial supervision or healthcare compliance).
Comparative considerations
European frameworks, including the EU’s AI regulatory agenda, emphasise risk-based obligations and may impose specific duties for so-called high-risk systems. Common-law jurisdictions often frame liability under negligence and product liability doctrines, with courts examining foreseeability and reasonableness of precautions. In Türkiye, the relevant mix includes consumer law, product liability principles, tort-law causation analysis and data protection obligations under the Personal Data Protection Law (KVKK). Counsel should therefore assess multiple overlapping avenues when advising clients.
Practical evidentiary challenges
AI systems often operate as complex, opaque chains of models, datasets and third-party components. Key evidentiary hurdles include demonstrating:
- The proximate cause between an AI output and the claimant’s loss;
- The responsible actor in a multi-party supply chain (model developer, data provider, system integrator, operator); and
- Whether the system behaved negligently or in breach of an express warranty or regulatory duty.
These challenges make documentation, logging and reproducibility essential to both defending and prosecuting claims.
Risk allocation and contracting — recommended clauses
Parties can significantly reduce post-incident uncertainty by adopting clear, commercially realistic contract terms. Typical mechanisms include:
- Detailed functional and non-functional specifications and acceptance testing protocols;
- Robust warranties limited in scope and duration, and express disclaimers of consequential damages where enforceable;
- Indemnities for third-party claims arising from data or IP infringement;
- Obligations to maintain logs, permit audits and retain models and training data for a defined period;
- Escalation procedures and shared incident response plans.
Regulatory compliance and governance
Organisations should map regulatory duties applicable to their AI use-case. For Türkiye-based operations, that includes KVKK obligations with respect to automated decision-making where personal data are involved, sectoral supervisory rules (for banking, healthcare, telecoms) and cross-border transfer controls. A governance framework should allocate internal roles, adopt impact assessments, and define human oversight mechanisms for high-risk outcomes.
Practical checklist for counsel
- Identify actors across the AI supply chain and align contractual responsibilities.
- Mandate explainability, logging and reproducibility where possible.
- Draft balanced warranties, liability caps and indemnities tailored to the client’s risk appetite and regulatory environment.
- Implement a compliance programme with impact assessments and human-in-the-loop policies.
- Preserve evidence and maintain record-keeping consistent with potential litigation and regulatory inquiries.
Conclusion
Allocating liability for AI-driven decisions requires a hybrid legal strategy combining contract, tort/product liability analysis and regulatory compliance. For practitioners in Türkiye, the prudent path is proactive: deploy contractual clarity, robust governance and technical controls to manage legal exposure. Av. Burak Şahin of Şahin Hukuk regularly advises clients on these risk-allocation strategies and can assist in tailoring contractual and compliance solutions to specific AI deployments.
This article is provided for general legal information and analytical purposes. Specific matters should be assessed under the current law and their own facts.
