Generative AI Development Disclosure

I. Responsible Training Data Practices.

This Generative AI Development Disclosure describes the data Ring, LLC (“Ring”) uses to develop or deploy our generative AI models and services (“generative AI services” or “services”). We develop generative AI services to make customers' lives easier and more productive. Our generative AI services range from customer service voice assistants to product and content recommendations. Our generative AI services may be powered by foundation models, including models built by Ring and its affiliates, such as Amazon.com, as well as third parties. We may use multiple models, and we may select models to optimize performance, select the best model for the relevant task, and incorporate the latest capabilities.

II. Responsible Training Data Practices.

For the generative AI services we make available to our customers, we train and test on a range of data intended to enhance our services’ capabilities. We may train and test on licensed and proprietary datasets, synthetic datasets, open-source datasets, and publicly available content. These datasets may include text, images, audio, video, code, and other types of data relevant to the service’s purpose. These datasets may contain public domain content, rights-protected material, and in some cases, personal information or aggregate consumer information. We train and select the models that power our generative AI services to help deliver more accurate, helpful, and relevant responses, and to help support the features and functionality of the service, such as by responding to natural language queries, recognizing visual content, generating relevant recommendations, or creating useful content.

We use various techniques to curate training data, which may include human and automated annotation, automated quality indicators, preference ranking, and other methods. We also implement multiple safeguards throughout our training data practices, including techniques to help limit the impact of any processing of personal information in connection with training generative AI services. For example, we may use processes like training data deduplication to remove repetitive data that could cause models to overweight certain patterns or reproduce specific content.

The size of our training and testing data varies by model or service, and could range from thousands to millions of data points. We have been collecting data since before 2022, with different models beginning development at different times. Data collection, training, and testing are ongoing processes as we continuously improve our services and incorporate new capabilities.

III. Evaluation for Quality.

We test and evaluate our generative AI services to assess that they meet our quality standards and perform as intended. We assess performance, accuracy, and reliability for our services’ intended uses. Our methodologies may include automated and human evaluation, benchmarking against established industry standards, simulating real-world usage patterns and edge cases, and evaluating outputs across various conditions and contexts. We test using data and modalities relevant to the goals and functionality of the generative AI service. We evaluate generative AI service performance through various methods, such as monitoring metrics, incorporating user feedback, and conducting periodic assessments as appropriate for the service.

As part of our quality evaluation process, we also implement appropriate safeguards for our generative AI services. These safeguards may include output filtering or safety controls designed to enable our generative AI services to provide trustworthy responses. Our approaches are tailored to each service’s purpose and capabilities.

IV. Learn More.

We are committed to building AI responsibly, with appropriate safeguards for safety, accuracy, privacy, and security. For more information about our approach to responsible AI, see our Responsible AI at Amazon page. For more information about specific generative AI services and features, please see the applicable help pages. For more information about how Ring collects and uses personal information, please see the Ring’s Privacy Notice.