AWS ML Heroes in 15: Detect Bias in ML Data, Models & Explain Predictions

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Published Feb 20, 2025

As machine learning moves from the research lab to the enterprise, questions around ethics and risk become even more critical for producing well-performing models. Bias goes against a code of ethics and can present itself throughout the machine learning lifecycle. We will explore how bias can appear in your people, strategy, data, algorithms, and models. I’ll share questions to ask during each stage to help root out bias. We will explore Amazon SageMaker Clarify, which detects potential bias throughout the ML lifecycle and helps to add a level of explainability to predictions.

Learning Objectives:
* Objective 1: Understand how bias can present itself across the ML lifecycle.
* Objective 2: Learn techniques on how to root out bias throughout the ML lifecycle.
* Objective 3: Understand how Amazon SageMaker Clarify can detect potential bias during data preparation, after model training, and in deployed models and help explain how input features contribute to model predictions in real time.

***To learn more about the services featured in this talk, please visit: https://aws.amazon.com/sagemaker/clarify/?sagemaker-data-wrangler-whats-new.sort-by=item.additionalFields.postDateTime&sagemaker-data-wrangler-whats-new.sort-order=desc
****To download a copy of the slide deck from this webinar visit: https://pages.awscloud.com/AWS-ML-Heroes-in-15-Detect-Bias-in-ML-Data-Models-and-Explain-Predictions_2023_SN-0504-MCL_OD Subscribe to AWS Online Tech Talks On AWS:
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