Unveiling the Power of AI Explanations

Table of Contents

1. Introduction
2. Cloud AI Platform's Prediction Service
3. AI Explanations
   • Feature Attributions
   • Limitations of AI Explanations
4. Methods of Feature Attribution
   • Sampled Shapley
   • Integrated Gradients
5. Differential Models and Non-differential Models
6. Resources on Explainability Methods
7. Trying out AI Explanations
   • Guides for Tabular Data and Image Data
8. AI Explanations with the What-If Tool
9. Conclusion

AI Explanations: Understanding Predictions with Cloud AI Platform

Introduction

In the world of machine learning, getting explanations for predictions is becoming increasingly important. Cloud AI Platform's Prediction service offers a way to generate explanations for your predictions. In this article, we will explore the features and capabilities of AI Explanations, which is now built into Cloud AI Platform's Prediction service. This new feature allows you to have feature attributions for each and every one of your predictions, providing insights into your model's outputs for classification and regression tasks.

Cloud AI Platform's Prediction Service

Cloud AI Platform's Prediction service is a powerful tool for deploying and serving machine learning models. It allows You to make predictions on new data using your trained models. With the integration of AI Explanations, you can now not only get predictions but also understand the underlying factors that contribute to those predictions.

AI Explanations

AI Explanations is a feature of Cloud AI Platform's Prediction service that integrates feature attributions into the prediction process. Feature attributions help you understand how much each feature in the data contributed to the predicted results. This is especially valuable in verifying if the model is behaving as expected, recognizing bias in real-time, and getting ideas for improving the training data and model.

Feature Attributions

Feature attributions are available for both tabular data and image data. They provide specific insights into individual predictions, allowing you to examine the contribution of each feature to the final predicted outcome.

Limitations of AI Explanations

Currently, there is a limitation in AI Explanations – it only supports models trained on TensorFlow 1.x. If you are using Keras to specify your model, you will need to convert it into an estimator using the model to estimator utility.

Methods of Feature Attribution

AI Explanations offers two methods of feature attribution: sampled Shapley and integrated gradients. Both methods are Based on the concept of Shapley values, which is a cooperative game theory algorithm that assigns credit to each player in a game for a particular outcome. In the case of AI Explanations, each feature is treated as a player, and proportional credit is assigned to each feature for the outcome of a prediction.

Sampled Shapley

Sampled Shapley assigns credit for the outcome of each feature and considers different permutations of those features. This method is most suitable for non-differential models, such as ensembles of trees.

Integrated Gradients

Integrated gradients, on the other HAND, are best suited for differential models like neural networks. It computes the gradients of the output with respect to the input, multiplied element-wise with the input itself. This method is especially useful for models with large feature spaces.

Differential Models and Non-differential Models

Differential models, like neural networks, have continuous inputs and can compute gradients. On the other hand, non-differential models, such as ensembles of trees, have discrete and limited set of inputs. The choice of the feature attribution method depends on the nature of the model being used.

Resources on Explainability Methods

For those interested in diving deeper into the explainability methods used by AI Explanations, there are numerous articles and research papers available that provide in-depth discussions on the topic. These resources are well-written and offer super interesting insights. Links to these resources can be found below.

Trying out AI Explanations

If you're ready to try out AI Explanations for your deployed model, head on over to the guides provided in the documentation. There are separate guides available for tabular data and image data. The best part is that these guides are presented as Colab notebooks, making it extremely easy to try out the feature.

AI Explanations with the What-If Tool

In a previous episode, we discussed the What-If Tool, an open-source tool for understanding your model's predictions. AI Explanations can be used in concert with the What-If Tool to gain even more in-depth understanding of your predictions. The process for incorporating AI Explanations into the What-If Tool is detailed in the Colab notebooks Mentioned earlier, and the links to those notebooks can be found in the description below.

Conclusion

In conclusion, AI Explanations is an essential tool for understanding the predictions made by machine learning models. With its ability to provide feature attributions for each prediction, it allows users to gain insights into the factors influencing the model's outputs. By incorporating AI Explanations into the Cloud AI Platform's Prediction service, Google has made explainability a seamless part of the machine learning workflow. So, why not try out AI Explanations today and unravel the mysteries behind your model's predictions?

Highlights

• Cloud AI Platform's Prediction service now includes AI Explanations for generating explanations for predictions.
• AI Explanations provide feature attributions for each prediction, helping users understand the model's outputs.
• Feature attributions are available for both tabular and image data, and can be used to verify the model's behavior, recognize bias, and improve training data and model.
• AI Explanations offer two methods of feature attribution: sampled Shapley and integrated gradients.
• Sampled Shapley is suitable for non-differential models, while integrated gradients are best for differential models.
• AI Explanations can be used in conjunction with the What-If Tool for even more in-depth understanding of predictions.

FAQ

Q: What is AI Explanations? A: AI Explanations is a feature of Cloud AI Platform's Prediction service that provides feature attributions for each prediction, helping users understand why a particular prediction was made.

Q: What are feature attributions? A: Feature attributions are insights into how much each feature in the data contributed to the predicted results. It helps users verify the model's behavior, recognize bias, and improve the training data and model.

Q: Can AI Explanations be used with any model? A: Currently, AI Explanations only support models trained on TensorFlow 1.x. If using Keras, the model needs to be converted into an estimator using the model to estimator utility.

Q: What are the limitations of AI Explanations? A: One limitation of AI Explanations is its compatibility only with models trained on TensorFlow 1.x. Additionally, the feature attribution methods used differ for differential and non-differential models.

Q: Are there resources available to learn more about explainability methods? A: Yes, there are numerous articles and research papers available that delve deeper into the explainability methods used by AI Explanations. Links to these resources can be found in the article.