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+# Big Science 🌸 Data Governance
+
+## 🐠 Overview
+
+The [Hugging Face](https://huggingface.co/) "[Big Science](pages/index.md)"
+project is a year-long workshop that brings together nearly 600 researchers from
+50 countries to work together on better understanding Large Language Models (
+LLMs)
+-- a family of deep learning systems that are trained on considerable amounts of
+data to learn statistical properties of language. LLMS have been increasingly
+adopted in contemporary language technologies, from Internet search to automatic
+translation.
+
+Our goal is to improve the scientific understanding of the capabilities and
+limitations of large-scale neural network models in NLP. In doing so, we seek to
+create both a multilingual language corpus and an open-source large language
+model, open to the scientific community. The
+May [MIT Tech Review article by Karen Hao](https://www.technologyreview.com/2021/05/20/1025135/ai-large-language-models-bigscience-project/)
+provides an excellent overview of the project in its broader context, and
+further details can be found in our recent article in
+VentureBeat: [NLP needs to be open. 500+ researchers are trying to make it happen](https://venturebeat.com/2021/07/14/nlp-needs-to-be-open-500-researchers-are-trying-to-make-it-happen/)
+.
+
+A core part of this project focuses on creating ethical protocols for Data
+Governance, including the collection and management of a training dataset. There
+are a wealth of language data sources to draw from to meet the growing needs of
+these technologies, but bringing those together to train large models while
+giving due consideration to ethical concerns such as **anonymization** (no PII)
+, **consent** (if the dataset does include PII), and **contestation** (
+individuals can request their information be removed from the dataset), remains
+an open problem.
+
+To this end, we are exploring the feasibility of working with a small network of
+organizations who are themselves interested in working on aspects of ethical
+data governance and can help develop tools and protocols for data collection,
+hosting, and management. Together, these organizations serve as “Data
+Custodians” for the Big Science project.
+
+### Data Custodians: Overview
+
+We are starting to implement a global collaborative data governance
+structure ([as described in this poster](https://drive.google.com/file/d/10pgOzQllHe-EXe5G6t-0pKlkaGJdZSqC/view))
+, where “Data Custodians” help to collect and serve the data. Data Custodians
+can be further broken down into Data Providers -- institutions collecting and
+providing the data -- and Data Hosts -- institutions serving the data. Data
+Providers and Data Hosts may be one and the same entity, or may collaborate
+together, for example, on data curation
+
+#### Data Providers
+
+Data Providers will work with Data Hosts and other members of Big Science,
+including pro-bono legal scholars, on the data collection process. A Data
+Provider’s main role is to provide access to data they own or have access to.
+This data may come in the form of e.g. web content, digitized books or
+scientific articles, audio files such as radio shows or podcasts that can be
+transcribed, images of text to be OCR’ed, or any other media containing language
+data.
+
+Collaborations between Data Providers and the rest of the Big Science project
+will focus on mechanisms for extracting specific kinds of data at a large scale,
+and brainstorming on how to share the processed data beyond the Big Science
+language model training run. The collaboration is also intended to catalyze the
+sharing of all of the processing tools, which may also be useful for Data
+Providers’ indexing (e.g., new audio transcripts), or training models of their
+own.
+
+We are also working on a long form paper on approaches to data governance in the
+context of the emergence of data-driven technology, and are hoping to focus on
+Data Providers’ existing models of global data governance.
+
+#### Data Hosts
+
+In collaboration with Big Science Workshop participants, the role of Data Hosts
+is to serve their data for at least 1 year. Each data host will serve their
+different data, which together will form the input training data for a large
+language model. Data Hosts are selected to help serve a diversity of languages.
+
+Additionally, Data Hosts are welcome to work with us on refining the data
+governance structure, including:
+
+1. Collecting, obtaining, and/or curating appropriate language data from a
+   variety of sources and providers
+2. Developing tools to manage, index, and visualize these data sources
+3. Developing protocols to make the data available to users and other host
+   organizations subject to the rights and requirements of the data creators,
+   subjects, and providers
+
+---
+
+In addition to being part of a new approach to handling data in Machine 
+Learning, a key benefit of participation is access to the shared 
+infrastructure for data
+governance.
+
+The questions we still need to iron out are:
+
+4. How can we manage access to the input datasets to most efficiently achieve
+   the above goals?
+5. How might contestation protocols work, for asking that one’s data be removed?
+
+
+For more information, please email: meg@huggingface.co, yacine@huggingface.co
+