Amazon SageMaker Clarify

Machine learning is widely used in every field to improve decision-making and enhance customer experiences. It also improves employees’ operational efficiency by analyzing vast amounts of data and uncovering patterns that drive better predictions and automation.

Consider an example of a bank that uses an ML model to evaluate a loan application. The ML model evaluates the applicant’s profile and predicts the probability of repaying the loan. Let’s see the challenges that can appear in this context:

• Fairness: The model might favor or disadvantage specific demographic groups.

• Bias: The model might overly rely on patterns from historical loan decisions, which may not apply to current economic conditions. For example, high inflation rates and economic recessions might make the model’s predictions less reliable.

• Explainability: The model predicts loan approval but does not provide clear reasons for its decision.

To make accurate predictions, it is very important to evaluate the model for its fairness, bias, and explainability. Amazon SageMaker Clarify is a tool that helps make AI models fairer and more understandable. With Clarify, data scientists and machine learning practitioners can evaluate large language models (LLMs) using automatic or human-in-the-loop evaluation jobs.

Features of the SageMaker Clarify

SageMaker Clarify offers a range of capabilities for analyzing data and models in both training and production environments. These capabilities make model predictions ...