A screenshot from SageMaker Clarify
SageMaker Clarify is integrated with Amazon SageMaker Data Wrangler, making it easier to identify bias during data preparation. You specify attributes of interest, such as gender or age, and SageMaker Clarify runs a set of algorithms to detect any presence of bias in those attributes.
Credit: AWS

How Clarify helps machine learning developers detect unintended bias

Learn why the science team behind Clarify turned to a concept from 1951 to address a modern complexity.

In his machine learning keynote at re:Invent on Tuesday, Swami Sivasubramanian, vice president of machine learning, Amazon Web Services (AWS), announced Amazon SageMaker Clarify, a new service that helps customers detect statistical bias in their data and machine learning models, and helps explain why their models are making specific predictions. Clarify saves developers time and effort by providing them the ability to better understand and explain how their machine learning models arrive at their predictions.

Understanding the predictions made by machine learning (ML) models and their potential biases remains a challenging and labor-intensive task that depends on the application, the dataset, and the specific model. We present Amazon SageMaker Clarify, an explainability feature for Amazon SageMaker that launched in December 2020, providing insights into data and ML models by identifying biases and explaining

Developers today contend with both increasingly large volumes of data, as well as more complex machine learning models. In order to detect bias in those complex models and data sets, developers must rely on open-source libraries replete with custom code recipes that are inconsistent across machine learning frameworks. This tedious approach requires a lot of manual effort and often arrives too late to correct unintended bias.

“If you care about this stuff, it's pretty much a roll-your-own situation right now,” said University of Pennsylvania computer science professor and Amazon Scholar Michael Kearns, who provided guidance to the team of scientists that developed SageMaker Clarify. “If you want to do some practical bias detection, you either need to implement it yourself or go to one of the open-source libraries, which vary in quality. They're frequently not well-maintained or documented. In many cases, it's just, ‘Here is the code we used to run our experiments for this academic paper, good luck.’”

SageMaker Clarify helps address the challenges of relying on multiple open-source libraries by offering robust, reliable code in an integrated, cloud-based framework.

Increasingly complex networks

The efficacy of machine learning models depends in part on understanding how much influence a given input has on the output.

AWS on Air 2020: AWS What’s Next ft. Amazon SageMaker Clarify

“A lending model for consumer loans might include credit history, employment history, and how long someone has lived at their current address,” Kearns explained. “It might also utilize variables that aren't specifically financial, such as demographic variables. One thing you might naturally want to know is which of these variables is more important in the model’s predictions, which may be used in lending decisions, and which are less important.”

With linear models, each variable is assigned some weight, positive or negative, and the overall decision is a sum of those weighted inputs. In those cases, the inputs with the bigger weights clearly have more influence on the output.

However, that approach falls short with neural networks or more complicated, non-linear models. “When you get to models like neural networks, it's no longer a simple matter of determining or measuring the influence of an input on the output,” Kearns said.

To help account for the growing complexity of modern machine learning models, the Amazon science team looked to the past — specifically to an idea from 1951.

Shapley values

The team wanted to design a solution to help machine learning pros be able to better explain their models’ decisions in the face of growing complexity. They found inspiration in a popular scientific method called Shapley values.

Shapley values were named in honor of Lloyd Shapley, who introduced the idea in 1951 and who won the Nobel Prize in Economics for it in 2012. The Shapley value approach, which is rooted in game theory, considers a wide range of possible inputs and outputs and offers “the average marginal contribution of a feature value across all possible coalitions”.  The comprehensive nature of the approach means it can help provide a framework for understanding the relative weight of a set of inputs, even across complex models and multiple inputs.

“SageMaker Clarify utilizes Shapley values to essentially take your model and run a number of experiments on it or on your data set,” Kearns said. “It then uses that to help come up with a visualization and quantification of which of those inputs is more or less important.”

Nor does it matter which kind of model a developer uses. “One of the nice things about this approach is it is model agnostic,” Kearns said. “It performs input-output experiments and gives you some sense of the relative importance of the different inputs to the output decision.”

The science team also worked to be certain SageMaker Clarify had a comprehensive view. They designed it so everyday developers and data scientists can detect bias across the entire machine learning workflow — including data preparation, training, and inference. SageMaker Clarify is able to achieve that comprehensive view, Kearns explained, because (again) it is model agnostic. “Each of these steps has been designed to avoid making strong assumptions about the type of model that the user is building.”

Bias detection and explainability

Model builders who learn that their models are making predictions that are strongly correlated to a specific input may find those predictions fall short of their definition of fairness. Kearns offered the example of a lending company that discovers its model’s predictions are skewed. “That company will want to understand why its model is making predictions that might lead to decisions to give loans at a lower rate to group A than to group B, even if they're equally credit worthy.”

SageMaker Clarify can examine tabular data and help the modelers spot where gaps might exist. “This company would upload a spreadsheet of data showing who they gave loans to, what they knew about them, et cetera,” Kearns said. “What the data bias detection part does is say, ‘For these columns, there may be over or underrepresentation of certain features, which could lead to a discriminatory outcome if not addressed.’”

A screenshot from SageMaker Clarify
SageMaker Clarify is integrated with SageMaker Model Monitor, enabling you to configure alerting systems like Amazon CloudWatch to notify you if your model exceeds certain bias metric thresholds. 
Credit: AWS

That can be influenced by a number of factors, including simply lacking the correct data to build accurate predictions. For example, SageMaker Clarify can indicate whether modelers have enough data on certain groups of applicants to expect an accurate prediction. The metrics provided by SageMaker Clarify can then be used to correct unintended bias in machine learning models, and automatically monitor model predictions in production to help ensure they are not trending toward biased outcomes.

Future applications

The SageMaker Clarify science team is already looking to the future.

Their research areas include algorithmic fairness and machine learning, as well as explainable AI. Team members have published widely in the academic literature on these topics, and worked hard in the development of SageMaker Clarify to balance the science of fairness with engineering solutions and practical product design. Their approaches are both statistical and causal, and focus not only on bias measurement in trained models, but also bias mitigation. It is that last part that has Kearns particularly excited about the future.

“The ability to not just identify problems in your models, but also have the tools to train them in a different way would go a long way toward mitigating that bias,” he said. “It’s good to know that you have a problem, but it's even better to have a solution to your problem.”

Best practices

The notions of bias and fairness are highly application dependent and the choice of the attributes for which bias is to be measured, as well as the choice of the bias metrics, may need to be guided by social, legal, and other non-technical considerations,” said principal applied scientist Krishnaram Kenthapadi, who led the scientific effort behind SageMaker Clarify. “For successful adoption of fairness-aware machine learning and explainable AI approaches in practice, it’s important to build consensus and achieve collaboration across key stakeholders such as product, policy, legal, engineering, and AI/ML teams, as well as end users and communities,” he said. “Further, it’s good to take into account fairness and explainability considerations during each stage of the ML lifecycle, for example, Problem Formation, Dataset Construction, Algorithm Selection, Model Training Process, Testing Process, Deployment, and Monitoring/Feedback.

Find more best practices on the AWS website.

Research areas

Related content

US, WA, Seattle
Job description: We are reimagining Amazon Search with an interactive conversational experience that helps you find answers to product questions, perform product comparisons, receive personalized product suggestions, and so much more, to easily find the perfect product for your needs. We’re looking for the best and brightest across Amazon to help us realize and deliver this vision to our customers right away. This will be a once in a generation transformation for Search, just like the Mosaic browser made the Internet easier to engage with three decades ago. If you missed the 90s—WWW, Mosaic, and the founding of Amazon and Google—you don’t want to miss this opportunity.
US, WA, Bellevue
We are looking for detail-oriented, organized, and responsible individuals who are eager to learn how to work with large and complicated data sets. Knowledge of econometrics, (Bayesian) time series, macroeconomic, as well as basic familiarity with Matlab, R, or Python is necessary, and experience with SQL 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, scientists, and product managers. 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 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
Do you want to join an innovative team of scientists who use machine learning to help Amazon provide the best experience to our Selling Partners by automatically understanding and addressing their challenges, needs and opportunities? Do you want to build advanced algorithmic systems that are powered by state-of-art ML, such as Natural Language Processing, Large Language Models, Deep Learning, Computer Vision and Causal Modeling, to seamlessly engage with Sellers? Are you excited by the prospect of analyzing and modeling terabytes of data and creating cutting edge algorithms to solve real world problems? Do you like to build end-to-end business solutions and directly impact the profitability of the company and experience of our customers? Do you like to innovate and simplify? If yes, then you may be a great fit to join the Selling Partner Experience Science team. Key job responsibilities Use statistical and machine learning techniques to create the next generation of the tools that empower Amazon's Selling Partners to succeed. Design, develop and deploy highly innovative models to interact with Sellers and delight them with solutions. Work closely with teams of scientists and software engineers to drive real-time model implementations and deliver novel and highly impactful features. Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation. Research and implement novel machine learning and statistical approaches. Lead strategic initiatives to employ the most recent advances in ML in a fast-paced, experimental environment. Drive the vision and roadmap for how ML can continually improve Selling Partner experience. About the team Selling Partner Experience Science (SPeXSci) is a growing team of scientists, engineers and product leaders engaged in the research and development of the next generation of ML-driven technology to empower Amazon's Selling Partners to succeed. We draw from many science domains, from Natural Language Processing to Computer Vision to Optimization to Economics, to create solutions that seamlessly and automatically engage with Sellers, solve their problems, and help them grow. Focused on collaboration, innovation and strategic impact, we work closely with other science and technology teams, product and operations organizations, and with senior leadership, to transform the Selling Partner experience.
US, WA, Seattle
The AWS AI Labs team has a world-leading team of researchers and academics, and we are looking for world-class colleagues to join us and make the AI revolution happen. Our team of scientists have developed the algorithms and models that power AWS computer vision services such as Amazon Rekognition and Amazon Textract. As part of the team, we expect that you will develop innovative solutions to hard problems, and publish your findings at peer reviewed conferences and workshops. AWS is the world-leading provider of cloud services, has fostered the creation and growth of countless new businesses, and is a positive force for good. Our customers bring problems which will give Applied Scientists like you endless opportunities to see your research have a positive and immediate impact in the world. You will have the opportunity to partner with technology and business teams to solve real-world problems, have access to virtually endless data and computational resources, and to world-class engineers and developers that can help bring your ideas into the world. Our research themes include, but are not limited to: few-shot learning, transfer learning, unsupervised and semi-supervised methods, active learning and semi-automated data annotation, large scale image and video detection and recognition, face detection and recognition, OCR and scene text recognition, document understanding, 3D scene and layout understanding, and geometric computer vision. For this role, we are looking for scientist who have experience working in the intersection of vision and language. We are located in Seattle, Pasadena, Palo Alto (USA) and in Haifa and Tel Aviv (Israel).
RO, Iasi
Amazon’s mission is to be earth’s most customer-centric company and our team is the guardian of our customer’s privacy. Amazon SDO Privacy engineering operates in Austin – TX, US and Iasi, Bucharest – Romania. Our mission is to develop services which will enable every Amazon service operating with personal data to satisfy the privacy rights of Amazon customers. We are working backwards from the customers and world-wide privacy regulations, think long term, and propose solutions which will assure Amazon Privacy compliance. Our external customers are world-wide customers of Amazon Retail Website, Amazon B2B services (e.g. Seller central, App / Skill Developers), and Amazon Subsidiaries. Our internal customers are services within Amazon who operate with personal data, Legal Representatives, and Customer Service Agents. You can opt-in for being part of one of the existing or newly formed engineering teams who will contribute to Amazon mission to meet external customers’ privacy rights: Personal Data Classification, The Right to be forgotten, The right of access, or Digital Markets Act – The Right of Portability. The ideal candidate has a great passion for data and an insatiable desire to learn and innovate. A commitment to team work, hustle and strong communication skills (to both business and technical partners) are absolute requirements. Creating reliable, scalable, and high-performance products requires a sound understanding of the fundamentals of Computer Science and practical experience building large-scale distributed systems. Your solutions will apply to all of Amazon’s consumer and digital businesses including but not limited to Amazon.com, Alexa, Kindle, Amazon Go, Prime Video and more. Key job responsibilities As an data scientist on our team, you will apply the appropriate technologies and best practices to autonomously solve difficult problems. You'll contribute to the science solution design, run experiments, research new algorithms, and find new ways of optimizing customer experience. Besides theoretical analysis and innovation, you will work closely with talented engineers and ML scientists to put your algorithms and models into practice. You will collaborate with partner teams including engineering, PMs, data annotators, and other scientists to discuss data quality, policy, and model development. Your work will directly impact the trust customers place in Amazon Privacy, globally.
JP, 13, Tokyo
The JP Economics team is a central science team working across a variety of topics in the JP Retail business and beyond. We work closely with JP business leaders to drive change at Amazon. We focus on solving long-term, ambiguous and challenging problems, while providing advisory support to help solve short-term business pain points. Key topics include pricing, product selection, delivery speed, profitability, and customer experience. We tackle these issues by building novel economic/econometric models, machine learning systems, and high-impact experiments which we integrate into business, financial, and system-level decision making. Our work is highly collaborative and we regularly partner with JP- EU- and US-based interdisciplinary teams. In this role, you will build ground-breaking, state-of-the-art causal inference models to guide multi-billion-dollar investment decisions around the global Amazon marketplaces. You will own, execute, and expand a research roadmap that connects science, business, and engineering and contributes to Amazon's long term success. As one of the first economists outside North America/EU, you will make an outsized impact to our international marketplaces and pioneer in expanding Amazon’s economist community in Asia. The ideal candidate will be an experienced economist in empirical industrial organization, labour economics, econometrics, or related structural/reduced-form causal inference fields. You are a self-starter who enjoys ambiguity in a fast-paced and ever-changing environment. You think big on the next game-changing opportunity but also dive deep into every detail that matters. You insist on the highest standards and are consistent in delivering results. Key job responsibilities Work with Product, Finance, Data Science, and Data Engineering teams across the globe to deliver data-driven insights and products for regional and world-wide launches. Innovate on how Amazon can leverage data analytics to better serve our customers through selection and pricing. Contribute to building a strong data science community in Amazon Asia.
GB, London
Are you excited about applying economic models and methods using large data sets to solve real world business problems? Then join the Economic Decision Science (EDS) team. EDS is an economic science team based in the EU Stores business. The teams goal is to optimize and automate business decision making in the EU business and beyond. An internship at Amazon is an opportunity to work with leading economic researchers on influencing needle-moving business decisions using incomparable datasets and tools. It is an opportunity for PhD students and recent PhD graduates in Economics or related fields. We are looking for detail-oriented, organized, and responsible individuals who are eager to learn how to work with large and complicated data sets. Knowledge of econometrics, as well as basic familiarity with Stata, R, or Python is necessary. Experience with SQL would be a plus. As an Economics Intern, you will be working in a fast-paced, cross-disciplinary team of researchers who are pioneers in the field. You will take on complex problems, and work on solutions that either leverage existing academic and industrial research, or utilize your own out-of-the-box pragmatic thinking. In addition to coming up with novel solutions and prototypes, you may even need to deliver these to production in customer facing products. Roughly 85% of previous intern cohorts have converted to full time economics employment at Amazon.
US, CA, Cupertino
We're looking for an Applied Scientist to help us secure Amazon's most critical data. In this role, you'll work closely with internal security teams to design and build AR-powered systems that protect our customers' data. You will build on top of existing formal verification tools developed by AWS and develop new methods to apply those tools at scale. You will need to be innovative, entrepreneurial, and adaptable. We move fast, experiment, iterate and then scale quickly, thoughtfully balancing speed and quality. Inclusive Team Culture Here at AWS, 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. Work/Life Balance 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. Mentorship & Career Growth 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 Deeply understand AR techniques for analyzing programs and other systems, and keep up with emerging ideas from the research community. Engage with our customers to develop understanding of their needs. Propose and develop solutions that leverage symbolic reasoning services and concepts from programming languages, theorem proving, formal verification and constraint solving. Implement these solutions as services and work with others to deploy them at scale across Payments and Healthcare. Author papers and present your work internally and externally. Train new teammates, mentor others, participate in recruiting and interviewing, and participate in our tactical and strategic planning. About the team Our small team of applied scientists works within a larger security group, supporting thousands of engineers who are developing Amazon's payments and healthcare services. Security is a rich area for automated reasoning. Most other approaches are quite ad-hoc and take a lot of human effort. AR can help us to reason deliberately and systematically, and the dream of provable security is incredibly compelling. We are working to make this happen at scale. We partner closely with our larger security group and with other automated reasoning teams in AWS that develop core reasoning services.
US, NY, New York
Search Thematic Ad Experience (STAX) team within Sponsored Products is looking for a leader to lead a team of talented applied scientists working on cutting-edge science to innovate on ad experiences for Amazon shoppers!. You will manage a team of scientists, engineers, and PMs to innovate new widgets on Amazon Search page to improve shopper experience using state-of-the-art NLP and computer vision models. You will be leading some industry first experiences that has the potential to revolutionize how shopping looks and feels like on Amazon, and e-commerce marketplaces in general. You will have the opportunity to design the vision on how ad experiences look on Amazon search page, and use the combination of advanced techniques and continuous experimentation to realize this vision. Your work will be core to Amazon’s advertising business. You will be a significant contributor in building the future of sponsored advertising, directly impacting the shopper experience for our hundreds of millions of shoppers worldwide, while delivering significant value for hundreds of thousands of advertisers across the purchase journey with ads on Amazon. Key job responsibilities * Be the technical leader in Machine Learning; lead efforts within the team, and collaborate and influence across the organization. * Be a critic, visionary, and execution leader. Invent and test new product ideas that are powered by science that addresses key product gaps or shopper needs. * Set, plan, and execute on a roadmap that strikes the optimal balance between short term delivery and long term exploration. You will influence what we invest in today and tomorrow. * Evangelize the team’s science innovation within the organization, company, and in key conferences (internal and external). * Be ruthless with prioritization. You will be managing a team which is highly sought after. But not all can be done. Have a deep understanding of the tradeoffs involved and be fierce in prioritizing. * Bring clarity, direction, and guidance to help teams navigate through unsolved problems with the goal to elevate the shopper experience. We work on ambiguous problems and the right approach is often unknown. You will bring your rich experience to help guide the team through these ambiguities, while working with product and engineering in crisply defining the science scope and opportunities. * Have strong product and business acumen to drive both shopper improvements and business outcomes. A day in the life * Lead a multidisciplinary team that embodies “customer obsessed science”: inventing brand new approaches to solve Amazon’s unique problems, and using those inventions in software that affects hundreds of millions of customers * Dive deep into our metrics, ongoing experiments to understand how and why they are benefitting our shoppers (or not) * Design, prototype and validate new widgets, techniques, and ideas. Take end-to-end ownership of moving from prototype to final implementation. * Be an advocate and expert for STAX science to leaders and stakeholders inside and outside advertising. About the team We are the Search thematic ads experience team within Sponsored products - a fast growing team of customer-obsessed engineers, technologists, product leaders, and scientists. We are focused on continuous exploration of contexts and creatives to drive value for both our customers and advertisers, through continuous innovation. We focus on new ads experiences globally to help shoppers make the most informed purchase decision while helping shortcut the time to discovery that shoppers are highly likely to engage with. We also harvest rich contextual and behavioral signals that are used to optimize our backend models to continually improve the shopper experience. We obsess about our customers and are continuously seeking opportunities to delight them.
US, CA, Palo Alto
Amazon is the 4th most popular site in the US. Our product search engine, one of the most heavily used services in the world, indexes billions of products and serves hundreds of millions of customers world-wide. We are working on a new initiative to transform our search engine into a shopping engine that assists customers with their shopping missions. We look at all aspects of search CX, query understanding, Ranking, Indexing and ask how we can make big step improvements by applying advanced Machine Learning (ML) and Deep Learning (DL) techniques. We’re seeking a thought leader to direct science initiatives for the Search Relevance and Ranking at Amazon. This person will also be a deep learning practitioner/thinker and guide the research in these three areas. They’ll also have the ability to drive cutting edge, product oriented research and should have a notable publication record. This intellectual thought leader will help enhance the science in addition to developing the thinking of our team. This leader will direct and shape the science philosophy, planning and strategy for the team, as we explore multi-modal, multi lingual search through the use of deep learning . We’re seeking an individual that can enhance the science thinking of our team: The org is made of 60+ applied scientists, (2 Principal scientists and 5 Senior ASMs). This person will lead and shape the science philosophy, planning and strategy for the team, as we push into Deep Learning to solve problems like cold start, discovery and personalization in the Search domain. Joining this team, you’ll experience the benefits of working in a dynamic, entrepreneurial environment, while leveraging the resources of Amazon [Earth's most customer-centric internet company]. We provide a highly customer-centric, team-oriented environment in our offices located in Palo Alto, California.