Exploratory Study

An exploratory study is a research methodology designed to investigate a problem that is not well understood, aiming to generate insights, define the problem more precisely, and develop hypotheses. In AI risk management, it serves as a critical initial step. The NIST AI Risk Management Framework (AI RMF 1.0) emphasizes the "MAP" function, which requires organizations to understand an AI system's context and potential impacts. Exploratory studies, using qualitative methods like expert interviews, are essential for this mapping process. They help uncover novel risks, such as algorithmic bias, that might be missed by standard checklists. This aligns with the "Risk Identification" stage of ISO 31000, ensuring a comprehensive understanding of the risk landscape from the outset.

For a financial institution developing an AI credit scoring model, the steps are: 1) **Scoping and Stakeholder Mapping:** Assemble a cross-functional team (product, legal, data science, ethics) to define the study's scope and brainstorm potential risks. 2) **Qualitative Data Collection:** Conduct in-depth interviews with loan officers and analyze global case studies of AI credit scoring controversies to identify key risk themes like data bias and lack of explainability. 3) **Hypothesis Formulation:** Based on the insights, create specific risk hypotheses, e.g., "The model may assign unfairly lower scores to female applicants due to historical biases in the training data." This hypothesis then guides technical testing and bias mitigation efforts, aligning with ISO/IEC TR 24027 guidelines on AI bias. This process can identify over 80% of potential ethical risks early, significantly reducing post-deployment costs and reputational damage.

Taiwan enterprises face three key challenges. First, a **shortage of interdisciplinary talent**; AI ethics requires expertise in law, sociology, and data science. The solution is to form cross-functional AI governance committees and partner with external experts. Second, the **dilemma between regulatory compliance and data access**. Analyzing bias often requires sensitive data, strictly regulated by Taiwan's Personal Data Protection Act (PDPA). Mitigation involves using Privacy-Enhancing Technologies (PETs) or high-quality synthetic data. Third, a **short-term ROI-driven culture** often deprioritizes preventive measures. The strategy is to link study findings to risk scenarios, estimating potential financial impacts from reputational damage or legal fines to secure management buy-in. The priority action is to establish the governance committee within three months.

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