How can AI Explanations be used in conjunction with the What-If Tool?
AI Explanations and the What-If Tool are two powerful features offered by Google Cloud AI Platform that can be used in conjunction to gain a deeper understanding of AI models and their predictions. AI Explanations provide insights into the reasoning behind a model's decisions, while the What-If Tool allows users to explore different scenarios and understand how changes in input data affect model predictions. By combining these two tools, users can not only interpret model behavior but also evaluate the impact of different inputs on model outcomes.
To start using AI Explanations with the What-If Tool, it is necessary to have a trained AI model deployed on AI Platform that supports AI Explanations. These models utilize the Explainable AI (XAI) framework, which allows for generating explanations for individual predictions. Once the model is deployed, the What-If Tool can be used to interactively explore and analyze the model's behavior.
To enable AI Explanations in the What-If Tool, the user needs to specify the explainable AI metadata when creating an instance of the WhatIfTool class. This metadata includes the model name, version, and the feature names and types. The feature names are used to map input data to their corresponding features in the model, while the feature types indicate the data types of the features (e.g., numerical, categorical).
Once the What-If Tool instance is created with the explainable AI metadata, the user can load data into the tool for analysis. The tool provides a user-friendly interface that allows for modifying input data and observing the resulting model predictions. Additionally, the tool displays AI Explanations for each prediction, providing insights into the factors that influenced the model's decision.
The What-If Tool offers various features that can be used in conjunction with AI Explanations. For example, users can create custom scenarios by modifying input data and observe how these changes affect the model's predictions. This allows for understanding the model's sensitivity to different inputs and identifying potential biases or limitations. Users can also compare multiple models side by side in the tool, enabling them to compare their predictions and explanations. This can be particularly useful when evaluating the performance of different models or when assessing the impact of model updates.
AI Explanations and the What-If Tool are complementary tools that can be used together to gain a comprehensive understanding of AI models. AI Explanations provide insights into the reasoning behind model predictions, while the What-If Tool allows for interactive exploration of model behavior and analysis of different scenarios. By combining these two tools, users can interpret model decisions, evaluate the impact of input changes, and gain confidence in the reliability and fairness of AI models.
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