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Privacy practices in Responsible AI
involve the consideration of potential implications in using sensitive
data (see Privacy section of Google
Responsible AI Practices). 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complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2024-12-20 UTC."],[[["Responsible AI privacy practices involve respecting legal and regulatory requirements, social norms, and individual expectations regarding sensitive data."],["Safeguards are crucial to ensure individual privacy, as ML models can retain and potentially reveal aspects of the data used in training."],["Transparency and user control over their data are essential considerations in responsible AI development."],["Google's PAIR Explorables offer interactive learning experiences to deepen your understanding of ML privacy concepts like randomized response, federated learning, and data leakage."]]],[]]