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 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 AWS Machine Learning University (Bias and Fairness Course) Training and Certification program, and AWS ML Embark.
Approach to responsible AI
Hear from Diya Wynn, a senior practice manager at AWS, on why it’s important to develop AI in a responsible way and what it means to create human-centered, inclusive AI
Tech Talk on responsible AI
Listen to a tech talk on responsible AI and face matching technologies from Nashlie Sephus, a tech evangelist at AWS, where you can learn how to make responsible AI part of the entire ML lifecycle.
Bias and fairness course
Take Machine Learning University’s hands-on course on responsible AI to learn how to use fairness criteria to identify and mitigate unwanted bias in the ML lifecycle.
AWS services help you better detect bias in datasets 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. And with Amazon Augmented AI, you can implement human review of ML predictions when human oversight is needed.
AWS AI Service Cards
AI Service Cards provide transparency and document the intended use cases and fairness considerations for our AWS AI services. They’re part of a comprehensive development process we undertake to build our services in a responsible way with fairness, robustness, explainability, governance, privacy, and security in mind. AI Service Cards provide a single place to find information on the intended use cases, responsible AI design choices, best practices, and performance for a set of AI service use cases.
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.
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 the University of California, Berkeley, MIT, the 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, the Partnership on AI, and the Responsible AI Institute.
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 AI 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, ML is also helping tackle our world’s hardest problems, from better diagnosis of disease to protection of endangered species.