Domain 4 Flashcards
Responsible AI
is a set of guidelines and principles to ensure that AI systems operate in a safe, trustworthy, and ethical manner
Fairness
aims to ensure that models treat everyone equitably and impartially, regardless of their age, where they live, their gender, or their ethnicity
It’s important to be able to explain in human terms why a model made a particular decision,
Explainability
Transparency
is about providing clear information about model capabilities, limitations, and potential risks to stakeholders. Transparency includes making sure that users know when they are interacting with AI.
Fairness of a model is measured by the bias and variance of outcomes across different groups.
the bias and variance of outcomes across different groups.
Overfitting becomes a problem when
the training dataset is not representative of the real world. As a result, the model only performs well on inputs that resemble the training data.
Underfitting can occur for some groups when
there wasn’t enough training data that matched their characteristics, so the model doesn’t perform well for them.
Class imbalance occurs when
a feature value has fewer training samples when compared with another value in the dataset. In this example, the feature for sex shows that women constitute 32.4% of the training data, whereas men constitute 67.6%.
These are crucial for conducting periodic reviews of datasets to identify and address potential issues or biases
Regular Audits
Consider using these as a starting point to reduce the amount of training that your model needs, reducing your environmental impact and sustainability
already-trained model. Reuse of existing work is the key principle of sustainability
Transparency is
about providing clear information about model capabilities, limitations, and potential risks. It also means making sure that users know when they are using AI.
Accountability
means establishing clear lines of responsibility for AI model outcomes and decision making.
Biases
are imbalances in data, or disparities in the performance of a model across different groups.
SageMaker Clarify helps you mitigate bias by
detecting potential bias during the data preparation, after model training, and in your deployed model, by examining specific attributes.
SageMaker Clarify can improve explainability by
looking at the inputs and outputs for your model, treating the model itself as a black box. By making these observations, it determines the relative importance of each feature.
How does SageMaker Clarify evaluate bias, etc?
SageMaker Clarify examines your dataset and model by using processing jobs. A SageMaker Clarify processing job uses the SageMaker Clarify processing container to interact with an Amazon S3 bucket. The S3 bucket would contain your input datasets, and a model that is deployed to a SageMaker inference endpoint. The SageMaker Clarify processing container obtains the input data set and configuration for analysis from an S3 bucket. For feature analysis, the SageMaker Clarify processing container sends requests to the model container, and retrieves model predictions from the response from the model container. After that step, the processing container computes and saves analysis results to the S3 bucket. These results include a JSON file with bias metrics, and global feature attributions, a visual report, and additional files for local feature attributions. You can download the results from the output location and view them.