Stay organized with collections
Save and categorize content based on your preferences.
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.
[[["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complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2025-02-25 UTC."],[[["Accountability in AI involves taking ownership for the effects of a system, often achieved through transparency about the system's development and behavior."],["Transparency can be enhanced using documentation practices like Model Cards and Data Cards, which provide information about models and datasets."],["Interpretability and explainability are crucial aspects of accountability, enabling understanding of model decisions and providing human-understandable explanations for automated actions."],["Fostering user trust in AI systems requires focusing on explainability and transparency, with further resources available in Google's Responsible AI Practices and Explainability Resources."]]],[]]