I.2 Manage AI/ML transparency (e.g., data selection, algorithm selection)

Enablers (verbatim, ECO)

PMI, ECO p. 5: enablers are "illustrative examples of the work associated with the task" and "not meant to be an exhaustive list but rather offer a few examples to help demonstrate what the task encompasses."

Ours: PMI's stems paraphrase one of these as a symptom; the right option is that enabler in PMI's words; the distractors are its siblings or a neighbouring task's enablers.

1. Document model selection criteria and decision rationale
2. Create transparent reporting on data sources and preprocessing steps
3. Establish explainability requirements for stakeholder communication
4. Maintain audit trails for algorithmic decision-making processes
5. Implement model interpretability tools and techniques

Study Hall misses on this task (1)

• Full-length Q41 · confidence High · 91s
  Scenario: Thin selection documentation, no audit trail, stakeholders worried about data selection and decision logic; NEXT
  Decided by: The option that covers every named concern for the named audience (mechanisms plus transparent reporting)
  Chose: Interpretability tools shared only with technical teams (partial scope, wrong audience)

Nothing logged yet.

What to read

• Workbook p. 39-41 — Required AI Transparency Considerations; Required AI Explainability Considerations
• Trustworthy AI Framework p. 13, 16-17 — Systemic AI Transparency Principles; Algorithmic Transparency Principles
• Module 01 Lessons 39-40: Requirements for AI System Transparency; Visibility Into System Design and Methods
• Module 05 Lessons 23-27: AI Transparency; Limits of Explainability; Interpretable AI
• Study guide Lesson 9: ECO Task I.2 — Manage AI/ML transparency (e.g., data selection, algorithm selection)