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10.2 Responsible AI: Ethics & Governance
Using AI is not just a technical choice; it is an ethical one. As a Project Manager, you are the steward of your organization's data, your client's secrets, and your team's integrity.
🛡️ The 3 Pillars of Responsible AI
To navigate the risks of AI, follow these pillars:
1. Data Privacy
The Red Line
Rule: Never put sensitive data (PII, Financials, IP, Strategy) into a public, free AI model (like standard ChatGPT). It may be used to train the model.
2. Bias Awareness
The Blind Spot
Rule: AI is trained on history. If history was biased (e.g., hiring practices), the AI will be biased. You must actively audit outputs for fairness.
3. Accountability
The Owner
Rule: The AI is a tool, like a calculator. If a bridge collapses because of a calculation error, the engineer is blamed, not the calculator.
📜 The "AI Charter"
Just as you create a Team Charter for human interactions, you need an AI Working Agreement. It should define:
- Approved Tools: Which specific engines (Enterprise versions) are safe to use?
- Transparency: When must a team member disclose that a document was written by AI?
- Validation: What is the mandatory review process before AI content leaves the team?
🚫 "Shadow AI"
Using unapproved AI tools to bypass security protocols is known as "Shadow AI." This is a major compliance violation. The PM must provide safe, approved alternatives so the team isn't tempted to go rogue.
🏛️ Explainability (XAI)
In regulated industries (Healthcare, Finance), you cannot just say "The AI told me to reject this loan." You need Explainability.
- Black Box AI: Inputs go in, answers come out, no one knows why. (Avoid for critical decisions).
- Explainable AI: The system provides the "Why" behind the decision. (Required for Governance).
📝 Exam Insight: If a team member uses a free online AI to summarize a confidential meeting with a client, they have committed a Security Breach. The PM's response is to immediateContain (notify IT/Security) and then Educate the team on proper tool usage.