doctrinal research

Doctrinal research, also known as 'black-letter law' analysis, is a legal research methodology focused on the systematic analysis and interpretation of legal texts such as statutes, regulations, and case law. Its primary goal is to articulate 'what the law is' on a particular issue. In AI risk management, it serves as the foundation for building a compliance framework. For instance, it is used to interpret the complex requirements of the EU AI Act, such as the definition of high-risk AI systems, data governance obligations (Article 10), and transparency duties (Article 52), or the implications of GDPR's Article 22 on automated decision-making. Unlike empirical research, which studies the law's real-world effects, doctrinal research provides clear, rule-based guidance for corporate compliance.

In enterprise risk management, doctrinal research translates abstract legal rules into concrete operational actions. The implementation process involves three key steps: 1. **Scoping the Legal Question:** Clearly define the specific legal issue, e.g., 'What are the precise transparency obligations for our high-risk AI system under the EU AI Act?'. 2. **Systematic Source Analysis:** The legal team gathers and analyzes all relevant legal sources, including the Act, related directives like GDPR, and guidance from regulatory bodies, to derive the exact compliance requirements. 3. **Formulating Compliance Guidance:** The findings are synthesized into an actionable compliance checklist or a formal legal opinion to guide technical teams. For example, a global manufacturer using AI for quality control would apply this method to ensure its systems comply with product liability directives, potentially reducing legal query resolution time by 40% and ensuring audit readiness.

Taiwanese enterprises face several challenges when applying doctrinal research to AI governance: 1. **Jurisdictional Complexity:** Navigating the intricate and sometimes conflicting requirements between different legal frameworks, such as the EU AI Act, US guidelines like the NIST AI RMF, and local Taiwanese laws. The solution is to develop a comprehensive compliance matrix that maps obligations across all relevant jurisdictions. 2. **Lack of Legal Precedent:** For new legislation like the EU AI Act, there is minimal case law to guide interpretation, creating legal uncertainty. Mitigation involves relying on official regulatory guidance, industry best practices, and established standards like ISO/IEC 42001 as interpretive aids. 3. **Bridging the Legal-Technical Gap:** Legal and engineering teams often lack a common language, hindering the effective implementation of legal requirements. The solution is to use structured frameworks like the NIST AI RMF to facilitate cross-departmental workshops and create shared understanding.

Winners Consulting specializes in doctrinal research for Taiwan enterprises, delivering compliant management systems within 90 days. Free consultation: https://winners.com.tw/contact