# Accountability

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**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:

- [Model Cards](https://modelcards.withgoogle.com/about)
- [Data Cards](https://sites.research.google/datacardsplaybook/)

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](https://pair.withgoogle.com/chapter/explainability-trust/) section of the
[People + AI Guidebook](https://pair.withgoogle.com/guidebook).
You can also check out [Google's Explainability Resources](https://explainability.withgoogle.com/)
for real life examples and best practices.