grounded theory methodology

Grounded theory methodology, originated by sociologists Glaser and Strauss in 1967, is a systematic, inductive qualitative research method. Its core principle is to generate theory directly from data, rather than testing preconceived hypotheses. In AI risk management, it is vital for fulfilling the requirements of frameworks like the NIST AI Risk Management Framework (RMF), which mandates understanding the socio-technical context and societal impacts of AI systems. It also supports standards like ISO/IEC 42001 (AI management system) by identifying stakeholder needs and expectations. This approach uncovers nuanced risks, such as algorithmic bias or latent privacy concerns, that purely technical assessments might miss, ensuring AI governance is ethically sound and responsive to societal values.

In enterprise AI risk management, application involves three main steps: 1. **Open Coding:** Breaking down raw data from stakeholder interviews into discrete concepts and properties. 2. **Axial Coding:** Systematically identifying relationships between the concepts to form more abstract and robust categories. 3. **Selective Coding:** Integrating these categories around a central core category to build a cohesive theoretical framework that explains the phenomenon. For example, a fintech company used this method to analyze user concerns about its AI credit scoring model. They discovered that "perceived procedural fairness" was the core issue, not just accuracy. This insight led to a redesigned user interface with better explanations, reducing related customer complaints by 40% and enhancing compliance with transparency requirements under regulations like the EU's GDPR.

Taiwanese enterprises face three key challenges: 1. **Quantitative Bias:** A cultural preference in the tech sector for hard metrics over deep qualitative insights. The solution is to link qualitative findings to concrete business risks, such as customer churn or brand reputation damage, and use pilot projects to demonstrate ROI. 2. **Resource Intensity:** The methodology is time- and labor-intensive, which can be a barrier for fast-paced companies. Mitigation involves leveraging AI-powered qualitative analysis software to accelerate the coding process and prioritizing high-risk AI applications for this in-depth analysis. 3. **Implementation Gap:** Difficulty in translating abstract theoretical findings into actionable engineering tasks. To overcome this, enterprises should create cross-functional "Ethics & Design" teams to translate insights into technical specifications and risk controls, setting clear 90-day implementation targets.

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