2.2. Reflection Checkpoint

Key Takeaways

• Six principles, each guarding a specific failure mode: fairness (inherited bias), reliability & safety (unpredictable behavior), privacy & security (data exposure and attacks), inclusiveness (excluding some users), transparency (hidden or unexplained AI), accountability (no one answerable).
• Fairness checks outcomes, not just inputs. Removing a sensitive attribute doesn't ensure fairness because proxies reintroduce bias.
• Reliability & safety is broader than accuracy — it's about consistent, safe behavior in unexpected conditions, including safe failure.
• Transparency vs. accountability: transparency makes the system understandable; accountability keeps humans answerable. Transparency enables accountability but doesn't replace it.
• Inclusiveness vs. fairness: inclusiveness is about access and usability for all; fairness is about equitable outcomes across groups.

Connecting Forward

You've seen why AI needs guardrails and what those guardrails are. Phase 3 shifts from principles to mechanics: how generative AI models actually work, how to choose and configure one for a given job, and the full catalog of AI workloads (text, speech, vision, information extraction, and agentic AI). Several configuration choices in Phase 3 — like grounding a model or selecting deployment options — are the concrete levers you'll pull to honor the principles you just learned.

Self-Check Questions

• For each of these scenarios, name the single most relevant principle: (a) a chatbot doesn't disclose it's an AI; (b) a resume screener favors one school's graduates; (c) a captioning feature fails for non-native speakers; (d) no one at the company can explain who's responsible when the model errs.
• Pick any two principles and explain how strengthening one can strengthen the other. Use a concrete example.