multivocal literature review

A multivocal literature review (MLR) is an expanded systematic review methodology. Its core feature is the systematic inclusion of "grey literature"—sources not published by commercial entities, such as industry reports, government documents, and expert blogs—alongside traditional academic literature. Originating in software engineering, it bridges the gap between academic theory and industry practice. In AI risk management, MLR provides critical intelligence for strategy. It directly supports compliance with standards like ISO/IEC 42001 (AI management system) by helping organizations understand stakeholder needs with real-world evidence. Unlike traditional reviews, an MLR captures timely, practical insights essential for navigating the fast-evolving AI landscape, ensuring governance frameworks are both academically sound and operationally relevant, aligning with the principles of the NIST AI RMF.

To apply a multivocal literature review for AI risk management, follow these steps: 1. **Scoping and Source Identification**: Define the risk topic, such as data privacy in AI models. Identify academic databases (e.g., IEEE Xplore) and grey literature sources like reports from regulators (e.g., GDPR enforcement decisions) and industry leaders (e.g., Google AI's white papers). 2. **Systematic Search and Quality Vetting**: Execute a consistent search strategy across all sources. Screen grey literature using a quality checklist, assessing author credibility, publisher reputation, and data verifiability. 3. **Data Extraction and Synthesis**: Extract key risk factors, mitigation techniques, and best practices. Synthesize findings to create a holistic view. For example, a fintech company used this method to assess algorithmic bias, combining academic papers on fairness metrics with regulatory guidance and practitioner blogs on implementation challenges. This led to a more robust validation process, reducing model risk and improving compliance audit outcomes.

Taiwan enterprises face three primary challenges when implementing a multivocal literature review: 1. **Inconsistent Quality of Grey Literature**: Many industry reports and blogs lack peer review, making their credibility difficult to assess. The solution is to develop a trusted source list, prioritizing reputable bodies like government agencies and major industry analysts, and to cross-verify information. 2. **Bilingual Data Integration**: Key AI research is in English, while local regulations and industry context are in Traditional Chinese. This requires a bilingual search taxonomy and analysts with domain expertise in both languages to ensure accurate synthesis. 3. **Resource and Skill Constraints**: A rigorous MLR is time-consuming and requires specialized skills. To overcome this, enterprises can start with a pilot project on a single high-risk AI application, use AI-powered research tools to accelerate screening, and seek external expertise to build internal capacity and standardized workflows.

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