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Principled AI Notes - Coggle Diagram
Principled AI Notes
Points to consider
Google AI
Ethics and Compliance, Trust
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Training data => limit the influence of historical bias against marginalized groups in training data both through data that's included and data that is absent or invisible due to historical exclusion.
Unfairness can enter into the system at any point in the ML lifecycle, from how you define the
problem originally, how you collect and prepare data, how the model is trained and evaluated, and on to how the model is integrated and used
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Google doesn't just build and control technology for its own use, but makes that technology available to others to use.
Criteria to assess
- Socially beneficial applications of the use case, and
- the potential for misuse.
AI doesn’t create unfair bias on its own; it exposes biases present in existing social systems and amplifies them
A major pitfall of AI is that its ability to scale can reinforce and perpetuate unfair biases which can lead to further unintentional harms
Security => new techniques of manipulation unique to AI, like deepfakes, which can impersonate someone's voice or biometrics.
AI pseudoscience, where AI practitioners promote systems that lack scientific foundation.
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