AWS' Nashlie Sephus talks about Amazon SageMaker Clarify at AWS re:Invent 2020. 'This can help the consumer of your machine learning model better understand why a model is making a certain prediction,' said Nashlie Sephus, applied science manager at AWS Machine Learning, during a keynote session at the conference, held virtually over three weeks this month. Using SageMaker Clarify, developers can specify important model attributes, such as location or occupation, and then run a set of algorithms on their data to detect bias.Īfter training a model, Clarify will automatically check the model for imbalances with the attributes and provide users with a detailed report showing the importance of each attribute to the model's output. It will help explain how feature values contribute to the predicted outcome, both for the model overall and for individual predictions, serving the needs of different stakeholders,' she said.

'It will help measure bias using a variety of statistical metrics. SageMaker Clarify will help data scientists detect bias in data sets before training and in their models after training, Jyoti said. Integrated with many SageMaker applications, SageMaker Clarify comes as AWS works to build out its AI portfolio and many AI creators work to eliminate biases in their models. The launch of Amazon SageMaker Clarify also is timely in that it accompanies a recent AWS push in AI, said Ritu Jyoti, program vice president of AI Research at IDC. Last week, AWS rival Google was at the center of the debate as a former top Google AI researcher said she had been fired for criticizing Google's treatment of women and people of color and not removing her name from a research paper discussing concerns that a language AI tool used by Google contains bias and could lead to hate speech.

The product comes amid intensifying debate in society about AI ethics and the role of bias in machine learning models and whether bias can cause racial and other discrimination in facial recognition and other AI systems.

Sagemaker clarify