Explicability

Explicability is the ability of an AI system to provide human-understandable explanations for its decisions. Rooted in bioethics and central to trustworthy AI, it is cited as a key characteristic in standards like ISO/IEC TR 24028. Unlike interpretability, which focuses on the technical transparency of a model, explicability is audience-centric, tailoring explanations for users or regulators. It is crucial for ensuring fairness, accountability, and compliance with regulations like GDPR Article 22 concerning automated decision-making.

Implementation involves three steps: 1) Conduct AI risk assessments to identify high-stakes applications (e.g., credit scoring) and define the audience for explanations. 2) Deploy technical tools (e.g., LIME, SHAP) and governance processes like Model Cards to document AI systems. 3) Establish continuous monitoring and auditing to review explanation quality and provide user appeal mechanisms. This approach helps reduce customer complaints, improves audit efficiency, and ensures compliance with frameworks like the EU AI Act.

Taiwanese enterprises face three main challenges: 1) A talent gap in explainable AI (XAI) expertise. 2) Regulatory ambiguity, as local laws are less specific than the EU AI Act. 3) The trade-off between model accuracy and explainability. Solutions include investing in training, proactively adopting global best practices like the NIST AI Risk Management Framework, and using a risk-based approach where simpler, more interpretable models are used for high-stakes decisions.

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