Mixed Methods Approach

Mixed Methods Approach refers to a research design that integrates both qualitative and quantitative data-gathering and analytical techniques within a single study. According to Creswell (2014), this approach allows for a more comprehensive understanding of research problems than either method alone. In the context of AI governance, it means combining quantitative metrics—such as model accuracy,-latency, and bias-score—with qualitative insights from stakeholder interviews and usability studies. This dual approach is essential for compliance with ISO 42001:2023 and the EU AI Act, which require organizations to assess not only the technical performance of AI but also its impact on human rights and ethical considerations. By using both data-driven and human-centric insights, companies can create a more robust AI risk-adjusted value proposition, ensuring that AI deployments are both technically sound and socially responsible.

In AI risk management, the application follows a structured three-step process: First, quantitative assessment involves collecting performance-based metrics,-error rates, and data-drift indicators from AI systems. Second, qualitative exploration uses focus groups and expert interviews to understand the contextual impact of AI decisions on end-users and employees. Third, integration occurs when these insights are synthesized into a unified AI Risk Management System (ARMS), as envisioned by the EU AI Act's risk-based approach. For instance, a Taiwan-based manufacturing firm could use sensor data to quantify predictive maintenance accuracy while simultaneously interviewing engineers to understand the practical implications of AI-suggested maintenance schedules. This approach can be measured by KPIs such as a 30% reduction in AI-related compliance incidents and a 20% improvement in stakeholder trust scores within the first year of implementation.

Taiwan enterprises typically face three challenges: Data Silos (technical teams and legal teams operating independently), Talent Scarcity (lack of professionals skilled in both data science and qualitative research), and Resource Constraints (perceived high cost of qualitative studies). To overcome these, companies should: 1) Establish a cross-functional AI Governance Committee to facilitate data-sharing between IT, Legal, and Business units. 2) Partner with specialized consultants like Winners Consulting Services Co., Ltd. to bridge the talent gap. 3) Implement a phased approach, starting with high-impact AI use cases to demonstrate ROI before scaling enterprise-wide. According to the Taiwan AI Basic Law (pending), companies must be able to demonstrate 'meaningful human oversight'—a requirement that is best satisfied through the qualitative insights provided by a mixed-methods approach. The priority should be establishing the data-gathering infrastructure first, followed by stakeholder engagement within 6 months.

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