Does the group representation in the training data reflect the real world?.women) which metrics should I use to answer the following questions: You might ask, given the groups of interest in the training data (for example, men vs. The list consists of some general questions that may be relevant for several ML applications, as well as questions about specific applications like document retrieval. To ground the discussion, the following are some sample questions that ML builders and their stakeholders may have regarding bias. SageMaker Clarify is a part of Amazon SageMaker, which is a fully managed service to build, train, and deploy ML models. To learn more about explainability, check out Explaining Bundesliga Match Facts xGoals using Amazon SageMaker Clarify. SageMaker Clarify provides tools for both of these needs, but in this post we only focus on the bias detection functionality. In many situations, it’s important to understand why the ML model made a specific prediction and also whether the predictions were impacted by bias. ML models are being increasingly used to help make decisions across a variety of domains, such as financial services, healthcare, education, and human resources. We also develop a framework to help you decide which metrics matter for your application. In this post, we use an income prediction use case (predicting user incomes from input features like education and number of hours worked per week) to demonstrate different types of biases and the corresponding metrics in SageMaker Clarify. For example, at the time of this writing, Amazon SageMaker Clarify offers 21 different metrics to choose from. To detect bias, you must have a thorough understanding of different types of bias and the corresponding bias metrics. Bias may also be introduced by the ML algorithm itself-even with a well-balanced training dataset, the outcomes might favor certain subsets of the data as compared to the others. For instance, an imbalanced sampling of the training data may result in a model that is less accurate for certain subsets of the data. Unfortunately, detecting bias isn’t an easy task for the vast majority of practitioners due to the large number of ways in which it can be measured and different factors that can contribute to a biased outcome. Bias detection in data and model outcomes is a fundamental requirement for building responsible artificial intelligence (AI) and machine learning (ML) models.
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