Amazon's Machine Learning University debuts responsible AI course

New, free offering provides students of any level practical skills and code examples for every stage, from the machine learning problem all the way to deployment.

Amazon's Machine Learning University (MLU) recently introduced a new course, "Responsible AI — Bias Mitigation & Fairness Criteria."

In the free, publicly accessible online program, students learn various dimensions of responsible AI, how to prepare data, mitigate bias during model training, and many other aspects of bias mitigation and fairness.

The course complements Amazon Web Services' new AI Service Cards, which offer responsible AI documentation on intended use cases and limitations.

Mia Mayer, a data scientist and MLU instructor who developed the responsible AI course, has explored bias issues in her own research. Here, she discusses the curriculum and what students are learning.

  1. Q. 

    Tell us about the responsible AI course. Who can take it, and how is it structured?

    A. 

    Responsible AI is an entry-level course targeted at technical individuals with the goal of explaining where bias in AI systems comes from, how to measure it, and ultimately how to mitigate bias as much as possible.

    Responsible AI - Welcome to Day 1!

    You don't need any machine learning knowledge to take the course, though some familiarity with Python programming and high-school-level math is helpful. In addition to the recorded lectures, we have white papers, code samples that leverage AWS services, and other resources available for students online. For a final project, students implement their own bias mitigation technique of choice to reduce disparity in model outcomes for different subpopulations.

    Responsible AI (2/30) - Machine Learning Fundamentals

    The course provides a lot of foundational material about how to build a machine learning model, so it's a good segue into all of the other courses that MLU offers, from decision trees and ensemble methods to natural language processing.

  2. Q. 

    What led you to add this course to MLU's offerings?

    A. 

    The course was driven by a business need, as well as a personal passion. In my own work, as I was getting more exposure to different machine learning projects, I noticed that a lot of the individuals in the room were men. That started the questions in my mind: “Are other identities being considered to the same extent as men? Do we have enough diversity and representation? What issues could occur when machine learning solutions are developed predominantly by one particular population?”

    Responsible AI (3/30) - Bias in different Machine Learning Tasks

    On the business side, we see a growing number of regulatory requirements, such as General Data Protection Regulation (GDPR) and The AI Act in the European Union, or the Principal Reasons Framework in United States. This has definitely led to more interest in this topic.

    Equally important as being compliant with regulations, it is our goal at AWS to use and develop ML & AI systems responsibly. Ultimately, measuring and mitigating bias is necessary to build trust and evaluate risks of AI systems and models. Failing to mitigate bias can lead to loss of trust and disadvantage subgroups of customers.

  3. Q. 

    Why this topic?

    A. 

    Machine learning is growing so much and so quickly — and it's expected to grow even more, with global spending on AI-based technologies expected to reach $204 billion by 2025. It touches on many aspects of our customers' lives.

    Mia Mayer, a data scientist and MLU instructor who developed the responsible AI course, is seen looking at a student's laptop screen in a classroom
    Mia Mayer is a data scientist and MLU instructor who developed the responsible AI course. She has explored bias issues in her own research.

    We want to make sure that machine learning models and APIs are developed responsibly and also used responsibly. This course complements the new Amazon leadership principle: "Success and scale bring broad responsibility."

  4. Q. 

    How did you go about putting together the course?

    A. 

    I wanted to cover bias aspects at every stage of the machine learning life cycle. When starting to collect material for the course I noticed that there wasn’t any freely available class covering the full ML pipeline in both theory and code.

    Responsible AI (5/30) - Fairness throughout the ML Lifecycle

    Many other courses only focus on one subcomponent, such as measuring bias before you train a model. I wanted to give students practical, hands-on skills and code examples for every stage of the life cycle, from ideation of the machine learning problem all the way to deployment.

  5. Q. 

    What's an example of how bias can create issues in machine learning?

    A. 

    A common machine learning problem that people try to tackle is classification, where the model provides different classes of outcomes, such as "approved" or "denied," or whether somebody will be shown an ad or not shown an ad.

    Mitigating bias
    Test set includes 1,150 text segments, each in nine languages.

    A machine learning model could perform much better for one subpopulation, comprising individuals with different attributes, than for another subpopulation. It's all about having some measure of fairness that we want to enforce across different subpopulations to reduce disparity as much as possible.

  6. Q. 

    What are you hearing from students so far?

    A. 

    Overall, we are getting really positive feedback, and it's one of our most engaged classes in terms of how much discussion is happening.

    Responsible AI (6/30) - Model Formulation and Data Collection

    An "aha" moment for a lot of students is that you can make an algorithm that's fair, but that doesn't mean it's high-performing. For example, you might have a model that denies all applicants. Technically that is fair — everyone receives the same outcome — but it’s obviously also undesirable. Students are often surprised to learn you need to have two metrics to evaluate a machine learning model: performance and fairness. You cannot do one or the other.

  7. Q. 

    What do you want students to take away from this class?

    A. 

    That there is no one right way of doing things. There are many different bias mitigation techniques, and this holds true for every component of the machine learning life cycle. It's all about trying to understand where the bias comes from and not blindly assuming that there isn't any bias.

    The Machine Learning University logo and text are seen on a black wall behind a reception desk in an office
    Machine Learning University (MLU) provides anybody, anywhere, at any time access to the same machine learning courses used to train Amazon’s own developers on machine learning.

    I also want students to realize that there are scientific methods they can use in practice that can help mitigate bias. A lot of the time, people observe and even quantify bias issues, but they don't really know what to do about them. The science is very new, but it's making huge steps forward, and it's already at a point where it can be used in practice to mitigate bias.

Research areas

Related content

US, WA, Seattle
Amazon is seeking an experienced, self-directed data scientist to support the research and analytical needs of Amazon Web Services' Sales teams. This is a unique opportunity to invent new ways of leveraging our large, complex data streams to automate sales efforts and to accelerate our customers' journey to the cloud. This is a high-visibility role with significant impact potential. You, as the right candidate, are adept at executing every stage of the machine learning development life cycle in a business setting; from initial requirements gathering to through final model deployment, including adoption measurement and improvement. You will be working with large volumes of structured and unstructured data spread across multiple databases and can design and implement data pipelines to clean and merge these data for research and modeling. Beyond mathematical understanding, you have a deep intuition for machine learning algorithms that allows you to translate business problems into the right machine learning, data science, and/or statistical solutions. You’re able to pick up and grasp new research and identify applications or extensions within the team. You’re talented at communicating your results clearly to business owners in concise, non-technical language. Key job responsibilities • Work with a team of analytics & insights leads, data scientists and engineers to define business problems. • Research, develop, and deliver machine learning & statistical solutions in close partnership with end users, other science and engineering teams, and business stakeholders. • Use AWS services like SageMaker to deploy scalable ML models in the cloud. • Examples of projects include modeling usage of AWS services to optimize sales planning, recommending sales plays based on historical patterns, and building a sales-facing alert system using anomaly detection.
US, WA, Seattle
We are a team of doers working passionately to apply cutting-edge advances in deep learning in the life sciences to solve real-world problems. As a Senior Applied Science Manager you will participate in developing exciting products for customers. Our team rewards curiosity while maintaining a laser-focus in bringing products to market. Competitive candidates are responsive, flexible, and able to succeed within an open, collaborative, entrepreneurial, startup-like environment. At the leading edge of both academic and applied research in this product area, you have the opportunity to work together with a diverse and talented team of scientists, engineers, and product managers and collaborate with others teams. Location is in Seattle, US Embrace Diversity Here at Amazon, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences. Amazon’s culture of inclusion is reinforced within our 14 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust Balance Work and Life Our team puts a high value on work-life balance. It isn’t about how many hours you spend at home or at work; it’s about the flow you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives Mentor & Grow Careers Our team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. Our senior members enjoy one-on-one mentoring and thorough, but kind, code reviews. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded engineer and enable them to take on more complex tasks in the future. Key job responsibilities • Manage high performing engineering and science teams • Hire and develop top-performing engineers, scientists, and other managers • Develop and execute on project plans and delivery commitments • Work with business, data science, software engineer, biological, and product leaders to help define product requirements and with managers, scientists, and engineers to execute on them • Build and maintain world-class customer experience and operational excellence for your deliverables
US, Virtual
The Amazon Economics Team is hiring Interns in Economics. We are looking for detail-oriented, organized, and responsible individuals who are eager to learn how to work with large and complicated data sets. Some knowledge of econometrics, as well as basic familiarity with Stata, R, or Python is necessary. Experience with SQL, UNIX, Sawtooth, and Spark would be a plus. These are full-time positions at 40 hours per week, with compensation being awarded on an hourly basis. You will learn how to build data sets and perform applied econometric analysis at Internet speed collaborating with economists, data scientists and MBAʼs. These skills will translate well into writing applied chapters in your dissertation and provide you with work experience that may help you with placement. Roughly 85% of interns from previous cohorts have converted to full time economics employment at Amazon. If you are interested, please send your CV to our mailing list at econ-internship@amazon.com.
US, WA, Seattle
Amazon internships are full-time (40 hours/week) for 12 consecutive weeks with start dates in May - July 2023. Our internship program provides hands-on learning and building experiences for students who are interested in a career in hardware engineering. This role will be based in Seattle, and candidates must be willing to work in-person. Corporate Projects (CPT) is a team that sits within the broader Corporate Development organization at Amazon. We seek to bring net-new, strategic projects to life by working together with customers and evolving projects from ZERO-to-ONE. To do so, we deploy our resources towards proofs-of-concept (POCs) and pilot programs and develop them from high-level ideas (the ZERO) to tangible short-term results that provide validating signal and a path to scale (the ONE). We work with our customers to develop and create net-new opportunities by relentlessly scouring all of Amazon and finding new and innovative ways to strengthen and/or accelerate the Amazon Flywheel. CPT seeks an Applied Science intern to work with a diverse, cross-functional team to build new, innovative customer experiences. Within CPT, you will apply both traditional and novel scientific approaches to solve and scale problems and solutions. We are a team where science meets application. A successful candidate will be a self-starter comfortable with ambiguity, strong attention to detail, and the ability to work in a fast-paced, ever-changing environment. As an Applied Science Intern, you will own the design and development of end-to-end systems. You’ll have the opportunity to create technical roadmaps, and drive production level projects that will support Amazon Science. You will work closely with Amazon scientists, and other science interns to develop solutions and deploy them into production. The ideal scientist must have the ability to work with diverse groups of people and cross-functional teams to solve complex business problems.
US, WA, Seattle
Amazon internships are full-time (40 hours/week) for 12 consecutive weeks with start dates in May - July 2023. Our internship program provides hands-on learning and building experiences for students who are interested in a career in hardware engineering. This role will be based in Seattle, and candidates must be willing to work in-person. Corporate Projects (CPT) is a team that sits within the broader Corporate Development organization at Amazon. We seek to bring net-new, strategic projects to life by working together with customers and evolving projects from ZERO-to-ONE. To do so, we deploy our resources towards proofs-of-concept (POCs) and pilot programs and develop them from high-level ideas (the ZERO) to tangible short-term results that provide validating signal and a path to scale (the ONE). We work with our customers to develop and create net-new opportunities by relentlessly scouring all of Amazon and finding new and innovative ways to strengthen and/or accelerate the Amazon Flywheel. CPT seeks an Applied Science intern to work with a diverse, cross-functional team to build new, innovative customer experiences. Within CPT, you will apply both traditional and novel scientific approaches to solve and scale problems and solutions. We are a team where science meets application. A successful candidate will be a self-starter comfortable with ambiguity, strong attention to detail, and the ability to work in a fast-paced, ever-changing environment. As an Applied Science Intern, you will own the design and development of end-to-end systems. You’ll have the opportunity to create technical roadmaps, and drive production level projects that will support Amazon Science. You will work closely with Amazon scientists, and other science interns to develop solutions and deploy them into production. The ideal scientist must have the ability to work with diverse groups of people and cross-functional teams to solve complex business problems.
US, CA, Palo Alto
The Amazon Search team creates powerful, customer-focused search solutions and technologies. Whenever a customer visits an Amazon site worldwide and types in a query or browses through product categories, Amazon Search services go to work. We design, develop, and deploy high performance, fault-tolerant distributed search systems used by millions of Amazon customers every day. We’re seeking a Principal Scientist with a deep expertise in Search Science. Your responsibilities will include everything from developing and prototyping innovative machine learning, and deep learning algorithms to implementing, testing, and supporting full solutions in a production environment. We are looking for innovators who can contribute to advancing search technology on what’s scientifically possible while remaining committed to creating world-class products. Joining this team, you’ll experience the benefits of working in a dynamic, entrepreneurial environment, while leveraging the resources of Amazon.com (AMZN), Earth's most customer-centric company one of the world's leading internet companies. We provide a highly customer-centric, team-oriented environment in our offices located in Palo Alto, California. Key job responsibilities As a hands-on leader of this team, you’ll be responsible for defining key research questions, identifying relevant data, adopting or proposing innovative machine learning solutions conducting rigorous experiments, publishing results and working with the engineering team to deploy these solutions. As a strategic leader, you will identify investment opportunities, develop long term strategies, and propose, prioritize and deliver on goals. You’ll also participate in organizational planning, hiring, mentorship and leadership development. You will be technically fearless and with a passion for building scalable science and engineering solutions. You will serve as a key scientific resource in full-cycle development (conception, design, implementation, testing to documentation, delivery, and maintenance). About the team Starting in 2009, the Visual Search & Augmented Reality team has thus far launched many visual search solutions on the Amazon App that use computer vision and machine learning/deep learning to help customers complete their shopping missions more easily; multiple internal teams at Amazon (devices, Kindle, Seller services, etc.) also use our libraries and APIs to deliver solutions to their own customers. We are a full stack shop, and our team capabilities cover the whole solution spectrum, ranging across applied science, large scale engineering services, product management, UX design, and mobile app development for iOS and Android.
LU, Luxembourg
&ltHire Relocation Requisition - not for posting> Provides insights to leadership on improving Supply Chain cost and Speed by using Data Science and Analytics techniques. Build Dashboards and models to industrialize these findings at scale.
US, VA, Arlington
The People eXperience and Technology Central Science Team (PXTCS) uses economics, behavioral science, statistics, and machine learning to proactively identify mechanisms and process improvements which simultaneously improve Amazon and the lives, wellbeing, and the value of work to Amazonians. We are an interdisciplinary team that combines the talents of science and engineering to develop and deliver solutions that measurably achieve this goal. We are looking for economists who are able to work with business partners to hone complex problems into specific, scientific questions, and test those questions to generate insights. The ideal candidate will work with engineers and computer scientists to estimate models and algorithms on large scale data, design pilots and measure their impact, and transform successful prototypes into improved policies and programs at scale. We are looking for creative thinkers who can combine a strong technical economic toolbox with a desire to learn from other disciplines, and who know how to execute and deliver on big ideas as part of an interdisciplinary technical team. Ideal candidates will work closely with business partners to develop science that solves the most important business challenges. They will work in a team setting with individuals from diverse disciplines and backgrounds. They will serve as an ambassador for science and a scientific resource for business teams, so that scientific processes permeate throughout the HR organization to the benefit of Amazonians and Amazon. Ideal candidates will own the data analysis, modeling, and experimentation that is necessary for estimating and validating models. They will work closely with engineering teams to develop scalable data resources to support rapid insights, and take successful models and findings into production as new products and services. They will be customer-centric and will communicate scientific approaches and findings to business leaders, listening to and incorporate their feedback, and delivering successful scientific solutions. Key job responsibilities Use causal inference methods to evaluate the impact of policies on employee outcomes. Examine how external labor market and economic conditions impact Amazon's ability to hire and retain talent. Use scientifically rigorous methods to develop and recommend career paths for employees. A day in the life Work with teammates to apply economic methods to business problems. This might include identifying the appropriate research questions, writing code to implement a DID analysis or estimate a structural model, or writing and presenting a document with findings to business leaders. Our economists also collaborate with partner teams throughout the process, from understanding their challenges, to developing a research agenda that will address those challenges, to help them implement solutions. About the team We are a multidisciplinary team that combines the talents of science and engineering to develop innovative solutions to make Amazon Earth's Best Employer.
US, WA, Seattle
The People eXperience and Technology Central Science Team (PXTCS) uses economics, behavioral science, statistics, and machine learning to proactively identify mechanisms and process improvements which simultaneously improve Amazon and the lives, wellbeing, and the value of work to Amazonians. We are an interdisciplinary team that combines the talents of science and engineering to develop and deliver solutions that measurably achieve this goal. We are looking for economists who are able to apply economic methods to address business problems. The ideal candidate will work with engineers and computer scientists to estimate models and algorithms on large scale data, design pilots and measure their impact, and transform successful prototypes into improved policies and programs at scale. We are looking for creative thinkers who can combine a strong technical economic toolbox with a desire to learn from other disciplines, and who know how to execute and deliver on big ideas as part of an interdisciplinary technical team. Ideal candidates will work in a team setting with individuals from diverse disciplines and backgrounds. They will work with teammates to develop scientific models and conduct the data analysis, modeling, and experimentation that is necessary for estimating and validating models. They will work closely with engineering teams to develop scalable data resources to support rapid insights, and take successful models and findings into production as new products and services. They will be customer-centric and will communicate scientific approaches and findings to business leaders, listening to and incorporate their feedback, and delivering successful scientific solutions. Key job responsibilities Use causal inference methods to evaluate the impact of policies on employee outcomes. Examine how external labor market and economic conditions impact Amazon's ability to hire and retain talent. Use scientifically rigorous methods to develop and recommend career paths for employees. A day in the life Work with teammates to apply economic methods to business problems. This might include identifying the appropriate research questions, writing code to implement a DID analysis or estimate a structural model, or writing and presenting a document with findings to business leaders. Our economists also collaborate with partner teams throughout the process, from understanding their challenges, to developing a research agenda that will address those challenges, to help them implement solutions. About the team We are a multidisciplinary team that combines the talents of science and engineering to develop innovative solutions to make Amazon Earth's Best Employer.
US, WA, Seattle
Amazon is looking for talented Postdoctoral Scientists to join our global Science teams for a one-year, full-time research position. Postdoctoral Scientists will innovate as members of Amazon’s key global Science teams, including: AWS, Alexa AI, Alexa Shopping, Amazon Style, CoreAI, Last Mile, and Supply Chain Optimization Technologies. Postdoctoral Scientists will join one of may central, global science teams focused on solving research-intense business problems by leveraging Machine Learning, Econometrics, Statistics, and Data Science. Postdoctoral Scientists will work at the intersection of ML and systems to solve practical data driven optimization problems at Amazon scale. Postdocs will raise the scientific bar across Amazon by diving deep into exploratory areas of research to enhance the customer experience and improve efficiencies. Please note: This posting is one of several Amazon Postdoctoral Scientist postings. Please only apply to a maximum of 2 Amazon Postdoctoral Scientist postings that are relevant to your technical field and subject matter expertise. Key job responsibilities * Work closely with a senior science advisor, collaborate with other scientists and engineers, and be part of Amazon’s vibrant and diverse global science community. * Publish your innovation in top-tier academic venues and hone your presentation skills. * Be inspired by challenges and opportunities to invent cutting-edge techniques in your area(s) of expertise.