Responsible AI

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Included in this chapter:

Concern	Question a leader asks	Main risk if ignored	Example Google capability
Transparency	Do users know how the AI works and its limits?	Hidden failures erode trust and invite regulatory action	Model cards and documentation
Privacy	Is personal data protected before the model sees it?	Leaking or memorizing personal data	Sensitive Data Protection (de-identification)
Fairness (bias)	Does the model treat all groups equitably?	Skewed data produces discriminatory outputs	Representative data and output evaluation
Accountability	Can we explain and answer for each decision?	Opaque 'black box' decisions that no one owns	Vertex Explainable AI feature attributions

Cheat sheet

Responsible AI protects people; secure AI protects the system
Responsible AI means fair, accountable, safe, and privacy-respecting
Explainability is one pillar of responsible AI, not the whole thing
Responsible AI is a shared responsibility, not an automatic setting
Transparency means disclosing how the model works and where it fails
Anonymize when identity must be gone for good; pseudonymize when you must re-link
Tokenization is pseudonymization with a consistent, reversible mapping
Sensitive Data Protection de-identifies data before the model sees it
Bias starts in the data, so a bigger model will not fix it
Fairness means no group is systematically disadvantaged
Bias enters at collection, labeling, training, and feedback
A named human or organization stays accountable for the AI
Explainability opens the black box with feature attributions
Keep a human in the loop where a wrong answer is costly
Personal data in training or prompts risks being memorized and revealed

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Cheat sheet