Tuesday, July 23, 2024
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Google Releases MCT Library For Model Explainability

Google, on Wednesday, released the Model Card Toolkit (MCT) to bring explainability in machine learning models. The information provided by the library will assist developers in making informed decisions while evaluating models for its effectiveness and bias.

MCT provides a structured framework for reporting on ML models, usage, and ethics-informed evaluation. It gives a detailed overview of models’ uses and shortcomings that can benefit developers, users, and regulators.

To demonstrate the use of MCT, Google has also released a Colab tutorial that has leveraged a simple classification model trained on the UCI Census Income dataset.

You can use the information stored in ML Metadata (MLMD) for explainability with JSON schema that is automatically populated with class distributions and model performance statistics. “We also provide a ModelCard data API to represent an instance of the JSON schema and visualize it as a Model Card,” note the author of the blog. You can further customize the report by selecting and displaying the metrics, graphs, and performance deviations of models in Model Card.

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The detailed reports such as limitations, trade-offs, and other information from Google’s MCT can enhance explainability for users and developers. Currently, there is only one template for representing the critical information about explainable AI, but you can create numerous templates in HTML according to your requirement.

Anyone using TensorFlow Extended (TFX) can avail of this open-source library to get started with explainable machine learning. For users who do not utilize TFX, they can leverage through JSON schema and custom HTML templates. 

Over the years, explainable AI has become one of the most discussed topics in technology as today, artificial intelligence has penetrated in various aspects of our lives. Explainability is essential for organizations to bring trust in AI models among stakeholders. Notably, in finance and healthcare, the importance of explainability is immense as any deviation in the prediction can afflict users. Google’s MCT can be a game-changer in the way it simplifies the model explainability for all.

Read more here.

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Ratan Kumar
Ratan Kumar
Ratan is a tech content writer who amasses inspiration from science fiction, cartoons, and psychology. Apart from writing, you can find him playing mobile games and depicting humans.


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