Accountability
Accountability means owning responsibility for the effects of an AI system.
Accountability typically involves transparency, or sharing information about
system behavior and organizational process, which may include documenting and
sharing how models and datasets were created, trained, and evaluated. The
following sites explain two valuable modes of accountability documentation:
Another dimension of accountability is interpretability, which involves the
understanding of ML model decisions, where humans are able to identify features
that lead to a prediction. Moreover, explainability is the ability for a
model's automated decisions to be explained in a way for humans to understand.
Read more about building user trust in AI systems in the Explainability +
Trust section of the
People + AI Guidebook, and the
Interpretability section of Google's Responsible AI Practices.
You can also check out Google's Explainability Resources
for real life examples and best practices.
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Last updated 2024-12-20 UTC.
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