Principled AI Notes
Resource
Berkman Klein Center report
Points to consider
Google AI
Ethics and Compliance, Trust
Sustainability
Markkula Center for Applied Ethics
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
doing AI responsibly is about asking hard questions
Google doesn't just build and control technology for its own use, but makes that technology available to others to use.
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.
Concerns on large language models
- hallucinations refer to instances where the AI model generates content that is the AI model generates content that is unrealistic, fictional, or completely fabricated
- Factuality relates to the accuracy or truthfulness of the information generated by a generative AI model
- Anthropomorphization refers to the attribution of human-like qualities, characteristics, or behaviors to non-human entities, such as machines or AI models.
Consider the impact of both intended and unintended outcomes.
Celebrity Recognition API
Based on Cloud Vision API
Celebrity Recognition API available only to qualifying customers behind an allow list.
Criteria to assess
- Socially beneficial applications of the use case, and
- the potential for misuse.