Responsible use of artificial intelligence and machine learning
Resources and tools to guide your development and application of AI and ML technologies
Artificial intelligence (AI) applied through machine learning (ML) will be one of the most transformational technologies of our generation, tackling some of humanity’s most challenging problems, augmenting human performance, and maximizing productivity. Responsible use of these technologies is key to fostering continued innovation. AWS is committed to developing fair and accurate AI and ML services and providing you with the tools and guidance needed to build AI and ML applications responsibly.
As you adopt and increase your use of AI and ML, AWS offers several resources based on our experience to assist you in the responsible development and use of AI
The Responsible Use of Machine Learning guide provides considerations and recommendations for responsibly developing and using ML systems across three major phases of their lifecycles: (1) design and development; (2) deployment; and (3) ongoing use.
Work with experts in responsible ML within our AWS Professional Services organization to create an operational approach encompassing people, processes, and technology that maximizes benefit and minimizes risk. The engagement includes development, deployment, and operationalization of responsible ML principles.
Continuous education on the latest developments in ML is an important part of responsible use. AWS offers the latest in ML education across your learning journey through programs like the Machine Learning University, Training and Certification program, and AWS ML Embark.
AWS services help you better detect bias in data sets and models, provide insights into model predictions, and better monitor and review model predictions through automation and human oversight.
Biases are imbalances in data or disparities in the performance of a model across different groups. Amazon SageMaker Clarify helps you mitigate bias by detecting potential bias during data preparation, after model training, and in your deployed model by examining specific attributes.
Explaining model predictions
Understanding a model’s behavior is important to developing more accurate models and making better decisions informed by model predictions. Amazon SageMaker Clarify provides greater visibility into model behavior, both overall and for individual predictions, so you can provide transparency to stakeholders, more deeply inform humans making decisions, and track whether a model is performing as intended.
Monitoring and human review
Monitoring is important to maintaining high quality ML models and ensuring accurate predictions. Amazon SageMaker Model Monitor automatically detects and alerts you to inaccurate predictions from models deployed in production. Also with Amazon Augmented AI you can easily implement human review of ML predictions when human oversight is needed.
Listen in as Jack Berkowitz, ADP, and Diya Wynn, Amazon Web Services discuss how the right data, the right technology and responsible use come together to deliver insights that can help accelerate and enhance business decision making.
Zopa, a UK-based digital bank and peer to peer lender, is using Amazon SageMaker Clarify to produce model explanations for its fraud detection application more quickly and seamlessly.
The Deutsche Fußball Liga (DFL) uses Amazon SageMaker Clarify to explain what led an ML model to predict a certain outcome with its Bundesliga Match Facts digital platform.
Community contribution and collaboration
AWS is committed to working with others to share best practices, accelerate research, and responsibly develop AI and ML technology. This collaboration across industry, academia, government and community groups, will help spur innovation for all.
AWS collaborates with academia and other stakeholders through strategic partnerships with universities including University of California, Berkeley, MIT, California Institute of Technology, the University of Washington, and others. We are also active members of multi-stakeholder organizations such as the OECD AI working groups and The Partnership on AI.
To spur research in responsible use, AWS provides research grants through Amazon Research Awards and the joint Amazon and National Science Foundation Fairness in AI Grants program.
Diversity, equity, and inclusion
We are cultivating the next generation of ML leaders by increasing accessibility to ML skills training for all, including those from backgrounds that are underrepresented in tech with programs like the new AI & ML Scholarship program. Through We Power Tech, AWS is collaborating with professional organizations such as Girls In Tech and the National Society of Black Engineers.
Research and innovation
The scientific field of artificial intelligence is constantly evolving, with new advancements being discovered and published every day. AWS is well represented in this community and is deeply invested in ongoing and rigorous scientific research in this field, with thousands of researchers and applied scientists innovating across every part of the company.
Machine learning making a positive impact on society
When used responsibly, ML has the potential to positively impact every industry and business process. Today, machine learning is also helping tackle our world’s hardest problems from better diagnosis of disease to protection of endangered species.