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Privacy practices in Responsible AI
involve the consideration of potential implications in using sensitive
data. This includes not only respecting legal and regulatory requirements, but
also considering social norms and typical individual expectations. For example,
what safeguards need to be put in place to ensure the privacy of individuals,
considering that ML models may remember or reveal aspects of the data that they
have been exposed to? What steps are needed to ensure users have adequate
transparency and control of their data?
Learn more about ML privacy through PAIR Explorables' interactive walkthroughs:
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2025-08-25 UTC."],[[["\u003cp\u003eResponsible AI privacy practices involve respecting legal and regulatory requirements, social norms, and individual expectations regarding sensitive data.\u003c/p\u003e\n"],["\u003cp\u003eSafeguards are crucial to ensure individual privacy, as ML models can retain and potentially reveal aspects of the data used in training.\u003c/p\u003e\n"],["\u003cp\u003eTransparency and user control over their data are essential considerations in responsible AI development.\u003c/p\u003e\n"],["\u003cp\u003eGoogle's PAIR Explorables offer interactive learning experiences to deepen your understanding of ML privacy concepts like randomized response, federated learning, and data leakage.\u003c/p\u003e\n"]]],[],null,[]]