Bias in, Bias out
Standard interactive reports
Workshops and Conversations
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.
Please note: This project is the work of individuals who participated in the Assembly program. If named, participants' employers are provided for identification purposes only.
Kasia ChmielinskiProject 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 NewmanResearch & Strategy
Senior Researcher at metaLAB at Harvard, Fellow at the Berkman Klein Center for Internet & Society, AI Grant Fellow. Studies new technologies and their effects on people. Creates interactive art installations that explore social and cultural dimensions of new tech, runs research workshops with creative materials. Leads metaLAB's work on AI + Art. Persuaded by the power of metaphors.
Josh JosephAI 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 TaylorData 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.
Sarah HollandResearch & Public Policy
Frequently Asked Questions
A few questions you might have
Q. Do you have a protoype or more information?
Yes, we do! You can take a look at a live protoype of the Nutrition Label for the Dollars for Docs dataset that our friends at ProPublica 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?
We believe that algorithm developers want to build responsible and smart AI models, but that there is a key step missing in the standard way these models are built. This step is to interrogate the dataset for a variety of imbalances or problems
it could have and ascertain if it is the right dataset for the model. We are inspired by the FDA's Nutrition Facts label in that it provides basic yet powerful facts that highlight issues in an accessible way. We aspire to do the same for datasets.
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 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 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?
This is a project of Assembly, a program run by the MIT Media Lab and the Berkman Klein Center.
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.