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						Empowering data scientists and policymakers with practical tools to improve AI outcomes
					


					

						
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The Data Nutrition Project team:

								
    
									
  • 1. Creates tools and practices that encourage responsible AI development
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  • 2. Partners across disciplines to drive broader change
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  • 3. Builds inclusion and equity into our work
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Want to get involved? Contact Us!

					

				


				

					


						

							
手机如何设置van

							Garbage in, Garbage out
						


						


							


								

									
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 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.
								


							


							

								
							


						


					

				



				


					

						
手机如何设置van

						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.

							


						


						

							
						


					


				


				

					

						

							
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							Workshops and Conversations
						


						

							

								

									We believe that building artificial intelligence is as much about learning as it is about technical implementation. Through our workshop series, the Data Nutrition Project brings a curriculum of awareness to organizations of all sizes and types - from small technical teams to larger, non-technical communities.
									

									

									
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									Our first workshop in the series is a brief, non-technical overview of how Artificial Intelligence (AI) algorithms work. Participants participate in an experiential activity in which you get to “be the algorithm”, and afterwards reflect on how bias is perpetuated in the stages of algorithm development you experienced. We also tie this experience into current industry themes and examples and discuss the complexities of building tools that mitigate the issue.
									

									

									This workshop is great for community groups looking to better understand how AI works, and how it is used in tools that we all use on a daily basis. It's also helpful for tech professionals who do not code, such as designers, project managers, etc. Contact Us to find out more!
								

							

						


						

							
							

								Photo Credit: w加速器app安装包
							

						

					

				


				

					
				

				

					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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										chmielinski_kasia
									

									

										

											
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.

										
									

								

							


							

								

									

										Mary Jane
									

									

										

											
Sarah Newman
Research & Strategy
										

										
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										josh_joseph
									

									

										

											
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
									

									

										

											
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.

										
										
										
									

								

							


							


							

								

									

										w加速器官网地址
									

									

										

											
w加速器安卓下载
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.

										
										
									

								

							


							

								

									

										kemi_thomas
									

									

										

											
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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
									

									

										

											
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

						

						

							

								

									

										holland_sarah
									

									

										

											
Sarah Holland
Research & Public Policy
										

										
										
									

								

							

							

								

									

										hosny_ahmed
									

									

										

											
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Data Science
										

										
										
										
										
									

								

							

						

					

				


				

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					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.
						

						

							

								

									
									

										

										    

												

													
													
												

												

													
													
												
    
										    	
										    	

										    		
										    	

										    

										

									

									
									
								

							

							

								
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