Newsletter
Social
Machine learning

National Science Foundation, in collaboration with Amazon, awards 11 Fairness in AI grant projects

Program supports computational research with goal of creating trustworthy AI systems that can address some of society's grand challenges.

By Staff writer
February 10, 2021
Share

  1. In 2019, the National Science Foundation (NSF) and Amazon announced a collaboration to accelerate research on fairness in AI, with each organization committing up to $10 million each in grants over the ensuing three years.

    Last year, NSF announced the first 10 projects to receive grants through the initiative. Thirty-five researchers obtained funds for the projects that addressed four broad research areas:

    1. Ensuring fairness in algorithms and the systems that incorporate them — which begins with the definition and quantification of fairness;
    2. Accountability and transparency in AI algorithms;
    3. Using AI to promote equity in society; and
    4. Ensuring that the benefits of AI are available to everyone.

    This year, NSF has announced the next cohort of 37 researchers focused on 11 projects that cover a range of topics, including:

    1. Theoretical and algoithmic foundations;
    2. Principles for human interaction with AI systems;
    3. Technologies such as natural language understanding and computer vision; and
    4. Applications including hiring decision, education, criminal justice, and human services.

    “We are excited to see NSF select an incredibly talented group of researchers whose research efforts are informed by a multiplicity of perspectives,” said Prem Natarajan, Alexa AI vice president of Natural Understanding. “As AI technologies become more prevalent in our daily lives, AI fairness is an increasingly important area of scientific endeavor. And we are delighted to partner with NSF to accelerate progress in this area by supporting the work of the top research teams in the world.”

    Henry Kautz, NSF
    Henry Kautz, NSF
    Credit: NSF

    “NSF is partnering with Amazon to support this year’s cohort of fairness in AI projects,” said Henry Kautz, director of NSF’s Division of Information and Intelligent Systems. “Understanding how AI systems can be designed on principles of fairness, transparency and trustworthiness will advance the boundaries of AI applications. And it will help us build a more equitable society in which all citizens can be designers of these technologies as well as benefit from them.”

    More information about this Fairness in AI
    program is available on NSF's website, and via their program update. Below is the list of the 2021 awardees, and an overview of their projects.

  2. Fairness in machine learning with human in the loop

    "This project aims to understand the long-term impact of fair decisions made by automated machine learning algorithms via establishing an analytical, algorithmic, and experimental framework that captures the sequential learning and decision process, the actions and dynamics of the underlying user population, and its welfare."

    • Principal investigator: Yang Liu
    • Co-principal investigators: Mingyan Liu, Parinaz Naghizadeh Ardabili, Ming Yin
    • Organization: University of California Santa Cruz
    • Award amount: $625,000

    Project description

  3. End-to-end fairness for algorithm-in-the-loop decision-making in the public sector

    "The goal of this project is to develop methods and tools that assist public sector organizations with fair and equitable policy interventions. In areas such as housing and criminal justice, critical decisions that impact lives, families, and communities are made by a variety of actors, including city officials, police, and court judges..." 

    • Principal investigator: Daniel Neill
    • Co-principal investigators: Constantine Kontokosta, Ravi Shroff, Edward McFowland
    • Organization: New York University
    • Award amount: $625,000

    Project description

  4. Foundations of fair AI in medicine: ensuring the fair use of patient attributes

    "Currently deployed machine learning models in medicine may exhibit fair use violations that undermine health outcomes. This project mitigates fair use violations at key stages in the deployment of machine learning in medicine: verification, model development, and communication..." 

    • Principal investigator: Flavio Calmon
    • Co-principal investigators: Elena Glassman, Berk Ustun
    • Organization: Harvard University
    • Award amount: $625,000

    Project description

  5. Organizing crowd audits to detect bias in machine learning

    "This project will explore three major research questions. The first is investigating new techniques for recruiting and incentivizing participation from a diverse crowd. The second is developing new and effective forms of guidance for crowd workers for finding instances and generalizing instances of bias. The third is designing new ways of synthesizing findings from the crowd so that development teams can understand and productively act on..."

    • Principal investigator: Jason Hong
    • Co-principal investigators: Motahhare Eslami, 
      Ken Holstein, Adam Perer, Nihar Shah
    • Organization: Carnegie-Mellon University
    • Award amount: $625,000

    Project description

  6. Using machine learning to address structural bias in personnel selection

    "Today, personnel selection practitioners in the United States are primarily guided by two streams of knowledge: 1) the development on the legal front pertaining to employment opportunities, and 2) the accumulation of findings in social, behavioral, and economic sciences that guide the accepted professional practices in personnel selection... This research project focuses on bridging the gap to establish machine learning as the third pillar for the design of personnel selection systems in human resource management..."
     

    • Principal investigator: Nan Zhang
    • Co-principal investigators: Heng Xu, Mo Wang
    • Organization: American University
    • Award amount: $624,485
      Project description
  7. Towards adaptive and interactive post hoc explanations

    "This proposal has three key areas of focus. First, this proposal will develop a novel formal framework for generating adaptive explanations which can be customized to account for subgroups of interest and user profiles. Second, this proposal will facilitate the explanations as an interactive communication process by dynamically incorporating user inputs. Finally, this proposal will improve existing automatic evaluation metrics such as sufficiency and comprehensiveness, and develop novel ones, especially for the understudied global explanations..."

    • Principal investigator: Chenhao Tan
    • Co-principal investigators: Yuxin Chen, Himabindu Lakkaraju, Sameer Singh
    • Organization: University of Chicago
    • Award amount: $375,000

    Project description

  8. Using AI to increase fairness by improving access to justice

    "This project applies Artificial Intelligence (AI) to increase social fairness by improving public access to justice. Although many AI tools are already available to law firms and legal departments, these tools do not typically reach members of the public and legal service practitioners except through expensive commercial paywalls. The research team will develop two tools to make legal sources more understandable: Statutory Term Interpretation Support (STATIS) and Case Argument Summarization (CASUM)..."

    • Principal investigator: Kevin Ashley
    • Co-principal investigators: Diane Litman
    • Organization: University of Pittsburgh
    • Award amount: $375,000

    Project description

  9. Fair AI in public policy - achieving fair societal outcomes in ML applications to education, criminal justice, and health and human services

    "This project advances the potential for Machine Learning (ML) to serve the social good by improving understanding of how to apply ML methods to high-stakes, real-world settings in fair and responsible ways..."

    • Principal investigator: Hoda Heidari
    • Co-principal investigators: Olexandra Chouldechova, Rayid Ghani, Zachary Lipton, Christopher Rodolfa
    • Organization: Carnegie-Mellon University
    • Award amount: $375,000

    Project description

  10. Towards holistic bias mitigation in computer vision systems

    "With the increasing use of artificial intelligence (AI) systems in life-changing decisions, such as hiring or firing of individuals or the length of jail sentences, there has been an increasing concern about the fairness of these systems. There is a need to guarantee that AI systems are not biased against segments of the population. This project aims to mitigate AI bias in the domain of computer vision, a driving application for much of the recent advances in a popular form of AI known as deep learning..."

    • Principal investigator: Nuno Vasconcelos
    • Organization: University of California San Diego
    • Award amount: $375,000
    • Project description
  11. Measuring and mitigating biases in generic image representation

    "This project will provide a study of societal biases present in current methods and models for computational visual recognition that are widely used as a source of generic visual representations..."

    • Principal investigator: Vicente Ordonez
    • Co-principal investigators: Baishakhi Ray
    • Organization: University of Virginia
    • Award amount: $375,000

    Project description

  12. Quantifying and mitigating disparities in language technologies

    "In this work we ask a simple question: can we measure the extent to which the diversity of language that we use affects the quality of results that we can expect from language technology systems? This will allow for the development and deployment of fair accuracy measures for a variety of tasks regarding language technology, encouraging advances in the state of the art in these technologies to focus on all, not just a select few..."

    • Principal investigator: Graham Neubig
    • Co-principal investigators: Jeffrey Bigham, Yulia Tsvetkov, Geoff Kaufman, Antonios Anastasopoulos
    • Organization: Carnegie-Mellon University
    • Award amount: $375,000

    Project description

Research areas
Tags
About the Author
Staff writer

Related content

Work with us

See more jobs See more jobs
Senior Applied Scientist, FireTV
US, WA, Seattle
Do you want to re-invent how millions of people consume video content on their TVs, Tablets and Alexa? We are building a free to watch streaming service called Fire TV Channels (https://techcrunch.com/2023/08/21/amazon-launches-fire-tv-channels-app-400-fast-channels/). Our goal is to provide customers with a delightful and personalized experience for consuming content across News, Sports, Cooking, Gaming, Entertainment, Lifestyle and more. You will work closely with engineering and product stakeholders to realize our ambitious product vision. You will get to work with Generative AI and other state of the art technologies to help build personalization and recommendation solutions from the ground up. You will be in the driver's seat to present customers with content they will love. Using Amazon’s large-scale computing resources, you will ask research questions about customer behavior, build state-of-the-art models to generate recommendations and run these models to enhance the customer experience. You will participate in the Amazon ML community and mentor Applied Scientists and Software Engineers with a strong interest in and knowledge of ML. Your work will directly benefit customers and you will measure the impact using scientific tools.
Applied Scientist, Artificial General Intelligence
US, MA, Boston
The Artificial General Intelligence (AGI) team is looking for a passionate, talented, and inventive Applied Scientist with a strong deep learning background, to build industry-leading technology with Large Language Models (LLMs) and multi-modal systems. You will support projects that work on technologies including multi-modal model alignment, moderation systems and evaluation. Key job responsibilities As an Applied Scientist with the AGI team, you will support the development of novel algorithms and modeling techniques, to advance the state of the art with LLMs. Your work will directly impact our customers in the form of products and services that make use of speech and language technology. You will leverage Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances in generative artificial intelligence (GenAI). You are also expected to publish in top tier conferences. About the team The AGI team has a mission to push the envelope in LLMs and multimodal systems. Specifically, we focus on model alignment with an aim to maintain safety while not denting utility, in order to provide the best-possible experience for our customers.
Applied Scientist III, Amzn Shipping-Prd & Tech, Amzn Shipping-Prd & Tech
IN, HR, Gurugram
Our customers have immense faith in our ability to deliver packages timely and as expected. A well planned network seamlessly scales to handle millions of package movements a day. It has monitoring mechanisms that detect failures before they even happen (such as predicting network congestion, operations breakdown), and perform proactive corrective actions. When failures do happen, it has inbuilt redundancies to mitigate impact (such as determine other routes or service providers that can handle the extra load), and avoids relying on single points of failure (service provider, node, or arc). Finally, it is cost optimal, so that customers can be passed the benefit from an efficiently set up network. Amazon Shipping is hiring Applied Scientists to help improve our ability to plan and execute package movements. As an Applied Scientist in Amazon Shipping, you will work on multiple challenging machine learning problems spread across a wide spectrum of business problems. You will build ML models to help our transportation cost auditing platforms effectively audit off-manifest (discrepancies between planned and actual shipping cost). You will build models to improve the quality of financial and planning data by accurately predicting ship cost at a package level. Your models will help forecast the packages required to be pick from shipper warehouses to reduce First Mile shipping cost. Using signals from within the transportation network (such as network load, and velocity of movements derived from package scan events) and outside (such as weather signals), you will build models that predict delivery delay for every package. These models will help improve buyer experience by triggering early corrective actions, and generating proactive customer notifications. Your role will require you to demonstrate Think Big and Invent and Simplify, by refining and translating Transportation domain-related business problems into one or more Machine Learning problems. You will use techniques from a wide array of machine learning paradigms, such as supervised, unsupervised, semi-supervised and reinforcement learning. Your model choices will include, but not be limited to, linear/logistic models, tree based models, deep learning models, ensemble models, and Q-learning models. You will use techniques such as LIME and SHAP to make your models interpretable for your customers. You will employ a family of reusable modelling solutions to ensure that your ML solution scales across multiple regions (such as North America, Europe, Asia) and package movement types (such as small parcel movements and truck movements). You will partner with Applied Scientists and Research Scientists from other teams in US and India working on related business domains. Your models are expected to be of production quality, and will be directly used in production services. You will work as part of a diverse data science and engineering team comprising of other Applied Scientists, Software Development Engineers and Business Intelligence Engineers. You will participate in the Amazon ML community by authoring scientific papers and submitting them to Machine Learning conferences. You will mentor Applied Scientists and Software Development Engineers having a strong interest in ML. You will also be called upon to provide ML consultation outside your team for other problem statements. If you are excited by this charter, come join us!
Applied Scientist, Artificial General Intelligence
US, MA, Boston
The Artificial General Intelligence (AGI) team is looking for a passionate, talented, and inventive Senior Applied Scientist with a strong deep learning background, to build industry-leading technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As a Senior Applied Scientist with the AGI team, you will work with talented peers to lead the development of novel algorithms and modeling techniques, to advance the state of the art with LLMs. Your work will directly impact our customers in the form of products and services that make use of speech and language technology. You will leverage Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances in generative artificial intelligence (GenAI). About the team The AGI team has a mission to push the envelope in LLMs and multimodal systems, in order to provide the best-possible experience for our customers.
Applied Scientist II, Alexa AI
IN, KA, Bengaluru
The Amazon Alexa AI team in India is seeking a talented, self-driven Applied Scientist to work on prototyping, optimizing, and deploying ML algorithms within the realm of Generative AI. Key responsibilities include: - Research, experiment and build Proof Of Concepts advancing the state of the art in AI & ML for GenAI. - Collaborate with cross-functional teams to architect and execute technically rigorous AI projects. - Thrive in dynamic environments, adapting quickly to evolving technical requirements and deadlines. - Engage in effective technical communication (written & spoken) with coordination across teams. - Conduct thorough documentation of algorithms, methodologies, and findings for transparency and reproducibility. - Publish research papers in internal and external venues of repute - Support on-call activities for critical issues Basic Qualifications: - Master’s or PhD in computer science, statistics or a related field - 2-7 years experience in deep learning, machine learning, and data science. - Proficiency in coding and software development, with a strong focus on machine learning frameworks. - Experience in Python, or another language; command line usage; familiarity with Linux and AWS ecosystems. - Understanding of relevant statistical measures such as confidence intervals, significance of error measurements, development and evaluation data sets, etc. - Excellent communication skills (written & spoken) and ability to collaborate effectively in a distributed, cross-functional team setting. - Papers published in AI/ML venues of repute Preferred Qualifications: - Track record of diving into data to discover hidden patterns and conducting error/deviation analysis - Ability to develop experimental and analytic plans for data modeling processes, use of strong baselines, ability to accurately determine cause and effect relations - The motivation to achieve results in a fast-paced environment. - Exceptional level of organization and strong attention to detail - Comfortable working in a fast paced, highly collaborative, dynamic work environment
Applied Scientist II, Advertising Trust
IN, KA, Bengaluru
Amazon is investing heavily in building a world class advertising business and we are responsible for defining and delivering a collection of self-service performance advertising products that drive discovery and sales. Our products are strategically important to our Retail and Marketplace businesses driving long term growth. We deliver billions of ad impressions and millions of clicks daily and are breaking fresh ground to create world-class products. We are highly motivated, collaborative and fun-loving with an entrepreneurial spirit and bias for action. With a broad mandate to experiment and innovate, we are growing at an unprecedented rate with a seemingly endless range of new opportunities. The ATT team, based in Bangalore, is responsible for ensuring that ads are relevant and is of good quality, leading to higher conversion for the sellers and providing a great experience for the customers. We deal with one of the world’s largest product catalog, handle billions of requests a day with plans to grow it by order of magnitude and use automated systems to validate tens of millions of offers submitted by thousands of merchants in multiple countries and languages. In this role, you will build and develop ML models to address content understanding problems in Ads. These models will rely on a variety of visual and textual features requiring expertise in both domains. These models need to scale to multiple languages and countries. You will collaborate with engineers and other scientists to build, train and deploy these models. As part of these activities, you will develop production level code that enables moderation of millions of ads submitted each day.
Applied Scientist , Sponsored Products
US, WA, Seattle
The Search Supply & Experiences team, within Sponsored Products, is seeking an Applied Scientist to solve challenging problems in natural language understanding, personalization, and other areas using the latest techniques in machine learning. In our team, you will have the opportunity to create new ads experiences that elevate the shopping experience for our hundreds of millions customers worldwide. As an Applied Scientist, you will partner with other talented scientists and engineers to design, train, test, and deploy machine learning models. You will be responsible for translating business and engineering requirements into deliverables, and performing detailed experiment analysis to determine how shoppers and advertisers are responding to your changes. We are looking for candidates who thrive in an exciting, fast-paced environment and who have a strong personal interest in learning, researching, and creating new technologies with high customer impact. Key job responsibilities As an Applied Scientist on the Search Supply & Experiences team you will: - Perform hands-on analysis and modeling of enormous datasets to develop insights that increase traffic monetization and merchandise sales, without compromising the shopper experience. - Drive end-to-end machine learning projects that have a high degree of ambiguity, scale, and complexity. - Build machine learning models, perform proof-of-concept, experiment, optimize, and deploy your models into production; work closely with software engineers to assist in productionizing your ML models. - Design and run experiments, gather data, and perform statistical analysis. - Establish scalable, efficient, automated processes for large-scale data analysis, machine-learning model development, model validation and serving. - Stay up to date on the latest advances in machine learning. About the team We are a customer-obsessed team of engineers, technologists, product leaders, and scientists. We are focused on continuous exploration of contexts and creatives where advertising delivers value to shoppers and advertisers. We specifically work on new ads experiences globally with the goal of helping shoppers make the most informed purchase decision. We obsess about our customers and we are continuously innovating on their behalf to enrich their shopping experience on Amazon
Applied Scientist, Translation Services
US, WA, Seattle
Have you ever wondered how Amazon launches and maintains a consistent customer experience across hundreds of countries and languages it serves its customers? Are you passionate about data and mathematics, and hope to impact the experience of millions of customers? Are you obsessed with designing simple algorithmic solutions to very challenging problems? If so, we look forward to hearing from you! At Amazon, we strive to be Earth's most customer-centric company, where both internal and external customers can find and discover anything they want in their own language of preference. Our Translations Services (TS) team plays a pivotal role in expanding the reach of our marketplace worldwide and enables thousands of developers and other stakeholders (Product Managers, Program Managers, Linguists) in developing locale specific solutions. Amazon Translations Services (TS) is seeking an Applied Scientist to be based in our Seattle office. As a key member of the Science and Engineering team of TS, this person will be responsible for designing algorithmic solutions based on data and mathematics for translating billions of words annually across 130+ and expanding set of locales. The successful applicant will ensure that there is minimal human touch involved in any language translation and accurate translated text is available to our worldwide customers in a streamlined and optimized manner. With access to vast amounts of data, cutting-edge technology, and a diverse community of talented individuals, you will have the opportunity to make a meaningful impact on the way customers and stakeholders engage with Amazon and our platform worldwide. Together, we will drive innovation, solve complex problems, and shape the future of e-commerce. Key job responsibilities * Apply your expertise in LLM models to design, develop, and implement scalable machine learning solutions that address complex language translation-related challenges in the eCommerce space. * Collaborate with cross-functional teams, including software engineers, data scientists, and product managers, to define project requirements, establish success metrics, and deliver high-quality solutions. * Conduct thorough data analysis to gain insights, identify patterns, and drive actionable recommendations that enhance seller performance and customer experiences across various international marketplaces. * Continuously explore and evaluate state-of-the-art modeling techniques and methodologies to improve the accuracy and efficiency of language translation-related systems. * Communicate complex technical concepts effectively to both technical and non-technical stakeholders, providing clear explanations and guidance on proposed solutions and their potential impact. About the team We are a start-up mindset team. As the long-term technical strategy is still taking shape, there is a lot of opportunity for this fresh Science team to innovate by leveraging Gen AI technoligies to build scalable solutions from scratch. Our Vision: Language will not stand in the way of anyone on earth using Amazon products and services. Our Mission: We are the enablers and guardians of translation for Amazon's customers. We do this by offering hands-off-the-wheel service to all Amazon teams, optimizing translation quality and speed at the lowest cost possible.
Economist, International Seller Service
US, WA, Seattle
Amazon.com strives to be Earth's most customer-centric company where customers can shop in our stores to find and discover anything they want to buy. We hire the world's brightest minds, offering them a fast paced, technologically sophisticated and friendly work environment. Economists at Amazon partner closely with senior management, business stakeholders, scientist and engineers, and economist leadership to solve key business problems ranging from Amazon Web Services, Kindle, Prime, inventory planning, international retail, third party merchants, search, pricing, labor and employment planning, effective benefits (health, retirement, etc.) and beyond. Amazon Economists build econometric models using our world class data systems and apply approaches from a variety of skillsets – applied macro/time series, applied micro, econometric theory, empirical IO, empirical health, labor, public economics and related fields are all highly valued skillsets at Amazon. You will work in a fast moving environment to solve business problems as a member of either a cross-functional team embedded within a business unit or a central science and economics organization. You will be expected to develop techniques that apply econometrics to large data sets, address quantitative problems, and contribute to the design of automated systems around the company. About the team The International Seller Services (ISS) Economics team is a dynamic group at the forefront of shaping Amazon's global seller ecosystem. As part of ISS, we drive innovation and growth through sophisticated economic analysis and data-driven insights. Our mission is critical: we're transforming how Amazon empowers millions of international sellers to succeed in the digital marketplace. Our team stands at the intersection of innovative technology and practical business solutions. We're leading Amazon's transformation in seller services through work with Large Language Models (LLMs) and generative AI, while tackling fundamental questions about seller growth, marketplace dynamics, and operational efficiency. What sets us apart is our unique blend of rigorous economic methodology and practical business impact. We're not just analyzing data – we're building the frameworks and measurement systems that will define the future of Amazon's seller services. Whether we're optimizing the seller journey, evaluating new technologies, or designing innovative service models, our team transforms complex economic challenges into actionable insights that drive real-world results. Join us in shaping how millions of businesses worldwide succeed on Amazon's marketplace, while working on problems that combine economic theory, advanced analytics, and innovative technology.
Senior Applied Scientist, B2B Payments & Lending
US, WA, Seattle
Are you passionate about leveraging your applied science skills to deliver actionable insights that impact daily business decisions? Do you thrive using causal inference, experimentation, and Machine Learning/AI to answer challenging product and customer behavior questions? Do you want to be a technical leader and build flexible and global solutions for complex financial, risk, and causal problems? If so, here is a great opportunity to consider! Amazon B2B Payments & Lending is seeking a Senior Applied Scientist who will combine their technical expertise with business intuition to generate critical insights that will set the strategic direction of the business. You will be a thought leader on the team, help set the team's strategic focus and roadmaps, and design and build systems/solutions that support financial products, working closely with business/product partners and engineers. You will utilize causal inference/experimentation/ML/AI methodologies, data and coding skills, problem solving and analytical skills, and excellent communication to deliver customer value. As a Senior Applied Scientist on our team, you'll play a pivotal role in uncovering actionable insights that shape the strategic direction of our products and services. You'll work closely with business stakeholders, data scientists, and engineers to tackle complex problems at the intersection of finance, risk modeling, and customer behavior. A day in the life - Collaborate with product, data, and engineering teams to identify key business and customer questions that can be answered through advanced analytics and machine learning - Design and build flexible, scalable solutions that leverage causal inference, experimentation, and applied ML/AI to provide critical insights that drive strategic decisions - Present analyses and recommendations to stakeholders, while also mentoring more junior data scientists and innovating on the team's capabilities About the team The Amazon B2B Payments & Lending team is a fast-paced, highly collaborative group focused on enabling seamless financial experiences for our business customers. We're building innovative solutions that leverage the power of data, AI, and automation to deliver frictionless payment processing, credit decisioning, and financial management tools. Our team culture is one of curiosity, creativity, and a relentless drive to delight our customers. We value bold thinking, data-driven decision making, and a willingness to experiment and learn. If you're passionate about using your technical expertise to drive meaningful business impact, this is an exciting opportunity to make a difference.
See more jobs
View from space of a connected network around planet Earth representing the Internet of Things.
Get more from Amazon Science
Subscribe to our monthly newsletter

Amazon.com | Conditions of Use | Privacy | © 1996-2025 Amazon.com, Inc. or its affiliates

Social