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The Data Nutrition Project team:
- 1. Creates tools and practices that encourage responsible AI development
- 2. Partners across disciplines to drive broader change
- 3. Builds inclusion and equity into our work
Want to get involved? Contact Us!
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Garbage in, Garbage out
自动加速器
Algorithms matter, and so does the data they’re trained on. To improve the accuracy and fairness of algorithms that determine everything from navigation directions to mortgage approvals, we need to make it easier for practitioners to quickly
assess the viability and fitness of datasets they intend to train AI algorithms on.
There’s a missing step in the AI development pipeline: assessing datasets based on standard quality measures that are both qualitative and quantitative.
We are working on packaging up these measures into an easy to use vs加速器下载.
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Standard interactive reports
A "nutrition label" for datasets.
The Data Nutrition Project aims to create a standard label for interrogating datasets for measures that will ultimately drive the creation of better, more inclusive algorithms.
Our current prototype includes a highly-generalizable interactive
data diagnostic label that allows for exploring any number of domain-specific aspects in datasets. Similar to a nutrition label on food, our Dataset Nutrition Label aims to highlight the key ingredients in a dataset such as meta-data and populations, as well as
unique or anomalous features regarding distributions, missing data, and comparisons to other ‘ground truth’ datasets. We are currently testing our label on several datasets, with an eye towards open sourcing this effort and gathering community
feedback.
The design utilizes a ‘modular’ framework that can be leveraged to add or remove areas of investigation based on the domain of the dataset. For example, Dataset Nutrition Labels for data about people may include modules about the representation
of race and gender, while Nutrition Labels for data about trees may not require that module.
To learn more, check out our live prototype built
on the Dollars for Docs dataset from vs加速器下载. A first draft of our paper can be found here.
Photo Credit: Jess Benjamin
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We are a group of researchers and technologists working together to tackle the challenges of ethics and governance of Artificial Intelligence as a part of the Assembly program at the Berkman Klein Center at Harvard University & MIT Media Lab.
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Kasia Chmielinski
Project Lead
Technologist at McKinsey working to drive impact in the healthcare industry through advanced analytics. Previously at The US Digital Service (The White House) and the Scratch project at the MIT Media Lab. Ex-Googler, native Bostonian. Dabbled in architecture at the Chinese University of Hong Kong before graduating with a degree in physics from Harvard University. Avid bird-watcher.
Sarah Newman
Research & Strategy
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Josh Joseph
AI Research
Chief Intelligence Architect for MIT's Quest for Intelligence. Previously, Chief Science Officer at Alpha Features, an alternative data distribution platform, and co-founded a proprietary trading company based on machine learning driven strategy discovery and fully autonomous trading. Has done a variety of consulting work across finance, life sciences, and robotics. Aero/Astro PhD on modeling and planning in the presence of complex dynamics from MIT. BS in Applied Mathematics and Mechanical Engineering from RIT. Spends too much time arguing about consciousness. Terrible improviser.
Matt Taylor
Data Science & Workshop Facilitation
Freelance learning experience designer and facilitator, with a background in AI implementation. Previously worked as an engineer in natural language processing, moderation tool development, and creative coding platform development. Currently creating learning experiences in STEAM for young people, and demystifying AI for all people. Seasoned pun specialist.
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Research Collaborator
Researcher at metaLAB at Harvard, architect in training. Previous research focused on the co-inhabitation of human and machines. Work explores the intersection of spaces, technology, and senses through physical and digital means. Teaches the integrated process of design and fabrication. M.Arch from MIT. Fascinated by the human brain and enjoys puzzles of all kinds.
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Software Engineering Collaborator
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Design Research Collaborator
Designer, technologist, and librarian focused on visual communication and experimental pedagogy. Principal at Harvard's metaLAB, with a background in Sociology and Urban Planning. Lives in the woods in Vermont. Dedicated builder of cardboard models and drawer of cartoons.
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Humanity Innovation Labs
User Experience Research & Design Collaborator
HIL is an agile consultancy that offers exploratory research and design services for ingenious proof of concepts in wearables, such as digital experiences and physical devices. We work in the ambiguous space of emerging technologies and use qualitative and quantitative methods in order to drive design. The sectors we work within are health and fitness, medical and industrial applications.
Alums
Sarah Holland
Research & Public Policy
Photo Credit: Jess Benjamin
Frequently Asked Questions
A few questions you might have
Q. Do you have a prototype or more information?
Yes, we do! You can take a look at a live protoype of the Dataset Nutrition Label for the Dollars for Docs dataset that our friends at w加速器ios官网 have made available to our group. We are also currently working on a paper describing our work, the protoype, and future directions.
Q. What inspired this project?
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Q. Whom have you been speaking with?
We have been speaking with researchers in academia, practitioners at large technology companies, individual data scientists, organizations, and government institutions that host or open datasets to the public. If you’re interested in getting involved,
please contact us.
Q. Is your work open source?
Yes. You can view our live protoype here, and the code behind the prototype on Github.
Q. Who is the intended beneficiary of this work?
Our primary audience for the Dataset Nutrition Label is primarily the data science and developer community who are building algorithmic AI models. However, we believe that a larger conversation must take place in order to shift the industry. Thus,
we are also engaging with educators, policymakers, and researchers on best ways to amplify and highlight the potential of the Dataset Nutrition Label and the importance of data interrogation before model creation. If you’re interested in getting
involved, please contact us.
Q. How will this project scale?
We believe that the Data Nutrition Project addresses a broad need in the model development ecosystem, and that the project will scale to address that need. Feedback on our prototype and opportunities to build additional prototypes on more datasets
will certainly help us make strides.
Q. Is this a Harvard/MIT project?
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Contact
The DNP project is a cross-industry collective. We are happy to welcome more into the fold, whether you are a policymaker, scientist, engineer, designer, or just a curious member of the public. We’d love to hear from you.
