Broadening Research Collaborations in ML

Scope

This workshop aims to discuss challenges and opportunities given the changing landscape of where, how and by whom research is produced. Progress toward democratizing AI research has been centered around making knowledge (e.g. class materials), established ideas (e.g. papers), and technologies (e.g. code, compute) more accessible. However, open, online resources are only part of the equation. Growth as a researcher requires not only learning by consuming information individually, but hands-on practice whiteboarding, coding, plotting, debugging, and writing collaboratively, with either mentors or peers. Of course, making "collaborators" more universally accessible is fundamentally more difficult than, say, ensuring all can access arXiv papers because scaling people and research groups is much harder than scaling websites. Can we nevertheless make access to collaboration itself more open?

From the renaissance until the late 19th century, individuals often pursued science without being affiliated with an institution. The professional scientist only emerged in the 19th century with the development of national research agendas and funding. Now, we are again in the midst of a change in the paths and institutions that spur scientific progress. The research landscape today is increasingly heterogeneous and characterized by a mix of traditional academic institutions, industry research labs, and community research groups (Masakhane, ML Collective, EleutherAI, MD4SG, and others). Often the boundary blurs, as labs collaborate across academic boundaries on cross-institutional research efforts over long time horizons (e.g. Cohere For AI, Big Science Workshop). What has driven this change? The question of how we collaborate is intrinsic to understanding and accelerating scientific progress. Often, how we collaborate is a result of both "how we would ideally like to collaborate" and "how we are incentivized to collaborate". Sometimes, changes in how we collaborate can be a catalyst for evolving the incentives and institutions themselves. This workshop provides an important forum for discussion on this topic, by drawing from the perspectives not only of researchers inside machine learning but also from the experience of other disciplines such as philosophy and history.

Accessibility: We are committed to making this workshop accessible to everybody. Please notify the organizers in advance of the workshop if you require any accommodations or if there is anything we can do to make this workshop more accessible to you.

Contact: Please email broadening.research.collabs@gmail.com for more information.

Call for Papers

We invite perspectives (up to 4 pages) and research papers (up to 8 pages) on topics related to broadening research collaborations in machine learning. These topics may include:

• Research studies in open research communities/cross-institutional projects.

• Incentives for broadening collaboration :

  • How to improve equity in computational resources across different research institutions.

  • Cultural changes for open collaboration

and much more. Please see Call for papers for more details about submission guidelines, formatting, and deadlines.

Key dates and deadlines

• Submission Start Date Time: August 15th, 2022 11: 59 PM UTC-0

• Submission End Date Time: October 2nd, 2022 11: 59 PM UTC-0

Poster and Discussion Sessions

All accepted papers will be presented as posters. We do not set the length of a pre-recorded talk, but recommend 5 minutes for a concise introduction and up to 10 minutes for a full discussion.

Code of Conduct

Everyone who participates in this workshop is required to conform to the NeurIPS Code of Conduct.