Building Responsible AI: Impact, Transparency, and Inclusivity in Machine Learning

The CAIR Team at Google Research is at the forefront of developing artificial intelligence (AI) and machine learning (ML) technologies that impact every aspect of our lives. The team focuses on the entire pipeline of building an AI system, from data collection to end-user feedback.

Understanding the data on which ML systems are built is a priority for the CAIR team. They have developed frameworks to improve the transparency of ML datasets and have created health-specific adaptations to ensure ethical data analysis in the healthcare industry. The team is also part of international partnerships aiming to create standards for health data documentation.

When ML systems are deployed in the real world, unexpected behavior can occur, especially in healthcare. The CAIR team focuses on identifying and mitigating unexpected model behavior, particularly when models are underspecified. They have demonstrated how causal structure knowledge can diagnose and mitigate fairness and robustness issues in new contexts.

In the deployment stage, the CAIR team aims to build technology that improves people’s lives, particularly through mobile device technology. They have worked on extending open-source platforms and developing new data collection mechanisms and predictions that can revolutionize patient chronic disease management, clinical trials, and drug development.

Finally, the CAIR team is dedicated to building more inclusive ML models and ensuring that all voices are heard. They have developed participatory systems that allow individuals to choose whether to disclose sensitive attributes when an ML system makes predictions.

The work of the CAIR team demonstrates their commitment to developing responsible AI systems that have a positive impact on society, individuals, and the future of technology.

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