Crisp-set QCA

Crisp-set QCA（清晰集定性比較分析）is a qualitative comparative analysis method that uses binary sets (0 or 1) to represent causal membership. Unlike fuzzy-set QCA, which uses degrees of membership, crisp-set QCA requires clear-cut thresholds for inclusion. In AI governance, this means a control is either implemented or not, based on predefined criteria. This method is particularly useful for identifying multiple causal pathways—how different combinations of AI practices, regulations, and organizational structures lead to the same outcome, such as regulatory compliance or risk-adjusted performance. It is based on the principle of equifinality: different paths can lead to the same result. This is critical in AI governance where no single regulation or technical control provides a complete solution. The method is widely cited in AI governance research, including studies evaluating the impact of international standards on AI practices.

Implementation typically follows three steps: 1. Variable Definition: Mapping AI governance practices (e.g., bias monitoring, data-use-rights-verification) into binary indicators (0: absent, 1: present). 2. Data Collection: Gathering evidence from internal audits, compliance reports, and employee surveys. 3. Causal Path Analysis: Using QCA software to identify combinations of practices that consistently lead to successful outcomes. For example, a financial institution might find that the combination of 'human-in-the-loop' and 'model-drift-monitoring' is the only path achieving a compliance score above 0.8. This allows the company to prioritize investments. Companies using this approach can see a measurable improvement in compliance readiness—often up to 30% reduction in regulatory inquiries—and a more efficient allocation of AI ethics resources.

Taiwan enterprises face three primary challenges: 1. Data Ambiguity: Many AI projects lack the documentation needed to justify a '1' or '0' rating. Solution: Standardize documentation requirements based on ISO 42001 before starting QCA. 2. Cultural Resistance: Stakeholders may resist the binary nature of the analysis. Solution: Use pilot projects to demonstrate value before scaling. 3. Lack of Expertise: Few practitioners in Taiwan are trained in QCA methodology. Solution: Partner with specialized consultants like Winners Consulting Services Co., Ltd. The recommended roadmap is to first audit existing AI practices, then apply QCA to identify the most effective control combinations, and finally integrate these findings into the enterprise risk management (ERM) framework within 12 months.

Winners Consulting Services Co., Ltd.專注台灣企業Crisp-set QCA相關議題，擁有豐富實戰輔導經驗，協助企業在90天內建立符合國際標準的AI治理機制，已服務超過100家台灣企業。申請免費機制診斷：https://winners.com.tw/contact