Build more responsible, inclusive AI with AWS
AWS is committed to developing fair and accurate AI and ML services and
providing customers with the tools and guidance needed to build AI and
ML applications responsibly.
As you scale your use of AI and ML technologies, you can leverage AWS
resources to help implement responsible AI across the entire AI and ML
lifecycle. AWS services help you better detect bias in datasets and models,
provide insights into model predictions, and better monitor and review model
predictions through automation and human oversight.
You can mitigate bias and improve explainability with AWS purpose-built
services. Amazon SageMaker Clarify helps you mitigate bias across the ML
lifecycle by detecting potential bias during data preparation, after model
training, and in your deployed model by examining specific attributes.
Similarly, SageMaker Clarify provides greater visibility into model behavior,
both overall and for individual predictions, so you can provide transparency
to stakeholders, more deeply inform humans making decisions, and track
whether a model is performing as intended. Monitoring is also important
to maintaining high-quality ML models and ensuring accurate predictions.
Amazon SageMaker Model Monitor automatically detects and alerts you to
inaccurate predictions from models deployed in production.
Check out three essential resources to enable more responsible AI:
• The Responsible Use of Machine Learning guide provides considerations
and recommendations for responsibly developing and using ML systems
across three major phases of their lifecycles: 1) design and development,
2) deployment, and 3) ongoing use. Read the guide ›
• Work with experts in responsible AI within our AWS Professional Services
organization to create an operational approach encompassing people,
processes, and technology that maximizes benefit and minimizes risk. The
engagement includes the development, deployment, and operationalization
of responsible AI principles. Learn more ›
• Continuous education on the latest developments in ML is an important
part of responsible use. AWS offers the latest in ML education across your
learning journey through programs like the Machine Learning University
(MLU), Training and Certification program, AI & ML Scholarship program,
and AWS Machine Learning Embark program.
AWS is committed to developing artificial intelligence and machine
learning in a responsible way, helping our customers put responsible AI
into practice and spurring research and continued development in this
area. Our work to build more responsible, inclusive AI is just beginning.
Learn more ›
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