Amazon Technologies, Inc.

Michele Donini

Krishnaram Kenthapadi

Muhammad Bilal Zafar

Cedric Philippe Archambeau

Xinyu Liu

Tyler Stephen Hill

Pinar Altin Yilmaz

Valerio Perrone

Sanjiv Ranjan Das

Michaela Hardt

Keerthan Harish Vasist

Pedro L Larroy

Ashish Rajendra Rathi

Explanation jobs may be performed for computer vision tasks. A request to execute an explanation job for a computer vision machine learning model may be received. The execution job may be performed, including extracting different features from the image, determining the respective relative importance values of the different features on inferences generated by the computer vision machine learning model. The result of the explanation job, including the generated heat maps may be provided.

Performing explanation jobs for computer vision tasks

A determination is made that an explanatory data set for a common set of predictions generated by a machine learning model for records containing text tokens is to be provided. Respective groups of related tokens are identified from the text attributes of the records, and record-level prediction influence scores are generated for the token groups. An aggregate prediction influence score is generated for at least some of the token groups from the record-level scores, and an explanatory data set based on the aggregate scores is presented.

Global explanations of machine learning model predictions for input containing text attributes

Hyperparameters for tuning a machine learning system may be optimized for fairness using Bayesian optimization with constraints for accuracy and bias. Hyperparameter optimization may be performed for a received training set and received accuracy and fairness constraints. Respective probabilistic models for accuracy and bias of the machine learning system may be initialized, then hyperparameter optimization may include iteratively identifying respective values for hyperparameters using analysis of the respective models performed using an acquisition function implementing constrained expected improvement on the respective models, training the machine learning system using the identified values to determine measures of accuracy and bias, and updating the respective models using the determined measures.

Hyperparameter optimization using fairness constraints

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Berlin, Germany

Michele Donini