Responsible AI

1. Fairness
2. Explainability
3. Privacy and Security
4. Safety
5. Controllability
6. Veracity and Robustness
7. Governance
8. Transparency

Amazon bedrock

It has model eveluation and model monitoring capabilities, which can help us to monitor the performance of the model and to identify any issues or areas for improvement.

Sagemaker Clarify

1. Bias detection: It can help us to detect bias in our data and models, which can help us to ensure that our models are fair and unbiased.
2. Model evaluation: It can do the evaluation continuously.
3. Explainability: It can help us to understand how our models are making predictions, for example showing which features are most important for the model’s prediction.

Sagemaker Model Monitor

We can get alerts for inaccurate responses.

Amazon Augemented AI

It insert humans in the loop to help correct results.

ML Design Principles

1. Assign Ownership and Accountability: It is important to assign ownership and accountability for the machine learning models and their outcomes.
2. Provide protection: we need to have security controls.
3. Enable Resiliency: We need to design our machine learning systems to be resilient to failures and to be able to recover quickly from any issues that may arise.
4. Enable Regularity: We need to design our machine learning systems to be able to handle regular updates and changes to the data and the models.
5. Enable Reproducibility: We need to design our machine learning systems to be able to reproduce results and to be able to track changes to the data and has the version control.
6. Optimise resource and reduce cost.
7. Enable automation: CI/CD, CT.
8. Enable continuous improvement: monitoring and analysis.