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Documents authored by Strubell, Emma

Document
DOI: 10.4230/DagMan.11.1.1
Climate Change: What is Computing’s Responsibility? (Dagstuhl Perspectives Workshop 25122)

Authors: Bran Knowles, Vicki L. Hanson, Christoph Becker, Mike Berners-Lee, Andrew A. Chien, Benoit Combemale, Vlad Coroamă, Koen De Bosschere, Yi Ding, Adrian Friday, Boris Gamazaychikov, Lynda Hardman, Simon Hinterholzer, Mattias Höjer, Lynn Kaack, Lenneke Kuijer, Anne-Laure Ligozat, Jan Tobias Muehlberg, Yunmook Nah, Thomas Olsson, Anne-Cécile Orgerie, Daniel Pargman, Birgit Penzenstadler, Tom Romanoff, Emma Strubell, Colin Venters, and Junhua Zhao

Published in: Dagstuhl Manifestos, Volume 11, Issue 1 (2025)

Abstract
This Manifesto was produced from the Perspectives Workshop 25122 entitled "Climate Change: What is Computing’s Responsibility?" held March 16-19, 2025 at Schloss Dagstuhl, Germany. The Workshop provided a forum for world-leading computer scientists and expert consultants on environmental policy and sustainable transition to engage in a critical and urgent conversation about computing’s responsibilities in addressing climate change - or more aptly, climate crisis. The resulting Manifesto outlines commitments and directions for future action which, if adopted as a basis for more responsible computing practices, will help ensure that these technologies do not threaten the long-term habitability of the planet. We preface our Manifesto with a recognition that humanity is on a path that is not in agreement with international global warming targets and explore how computing technologies are currently hastening the overshoot of these boundaries. We critically assess the vaunted potential for harnessing computing technologies for the mitigation of global warming, agreeing that, under current circumstances, computing is contributing to negative environmental impacts in other sectors. Computing primarily improves efficiency and reduces costs which leads to more consumption and more negative environmental impact. Relying solely on efficiency gains in computing has thus far proven to be insufficient to curb global greenhouse gas emissions. Therefore, computing’s purpose within a strategy for tackling climate change must be reimagined. Our recommendations cover changes that need to be urgently made to the design priorities of computing technologies, but also speak to the more systemic shift in mindset, with sustainability and human rights providing a necessary moral foundation for developing the kinds of computing technologies most needed by society. We also stress the importance of digital policy that accounts for both the direct material impacts of computing and the detrimental indirect impacts arising from computing-enabled efficiencies, and the role of computing professionals in informing policy making.
Cite as

Bran Knowles, Vicki L. Hanson, Christoph Becker, Mike Berners-Lee, Andrew A. Chien, Benoit Combemale, Vlad Coroamă, Koen De Bosschere, Yi Ding, Adrian Friday, Boris Gamazaychikov, Lynda Hardman, Simon Hinterholzer, Mattias Höjer, Lynn Kaack, Lenneke Kuijer, Anne-Laure Ligozat, Jan Tobias Muehlberg, Yunmook Nah, Thomas Olsson, Anne-Cécile Orgerie, Daniel Pargman, Birgit Penzenstadler, Tom Romanoff, Emma Strubell, Colin Venters, and Junhua Zhao. Climate Change: What is Computing’s Responsibility? (Dagstuhl Perspectives Workshop 25122). In Dagstuhl Manifestos, Volume 11, Issue 1, pp. 1-18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@Article{knowles_et_al:DagMan.11.1.1,
  author =	{Knowles, Bran and Hanson, Vicki L. and Becker, Christoph and Berners-Lee, Mike and Chien, Andrew A. and Combemale, Benoit and Coroam\u{a}, Vlad and De Bosschere, Koen and Ding, Yi and Friday, Adrian and Gamazaychikov, Boris and Hardman, Lynda and Hinterholzer, Simon and H\"{o}jer, Mattias and Kaack, Lynn and Kuijer, Lenneke and Ligozat, Anne-Laure and Muehlberg, Jan Tobias and Nah, Yunmook and Olsson, Thomas and Orgerie, Anne-C\'{e}cile and Pargman, Daniel and Penzenstadler, Birgit and Romanoff, Tom and Strubell, Emma and Venters, Colin and Zhao, Junhua},
  title =	{{Climate Change: What is Computing’s Responsibility? (Dagstuhl Perspectives Workshop 25122)}},
  pages =	{1--18},
  journal =	{Dagstuhl Manifestos},
  ISSN =	{2193-2433},
  year =	{2025},
  volume =	{11},
  number =	{1},
  editor =	{Knowles, Bran and Hanson, Vicki L. and Becker, Christoph and Berners-Lee, Mike and Chien, Andrew A. and Combemale, Benoit and Coroam\u{a}, Vlad and De Bosschere, Koen and Ding, Yi and Friday, Adrian and Gamazaychikov, Boris and Hardman, Lynda and Hinterholzer, Simon and H\"{o}jer, Mattias and Kaack, Lynn and Kuijer, Lenneke and Ligozat, Anne-Laure and Muehlberg, Jan Tobias and Nah, Yunmook and Olsson, Thomas and Orgerie, Anne-C\'{e}cile and Pargman, Daniel and Penzenstadler, Birgit and Romanoff, Tom and Strubell, Emma and Venters, Colin and Zhao, Junhua},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagMan.11.1.1},
  URN =		{urn:nbn:de:0030-drops-250724},
  doi =		{10.4230/DagMan.11.1.1},
  annote =	{Keywords: sustainability, climate change, efficiency, supply chain management, climate modelling}
}
Document
DOI: 10.4230/DagRep.12.6.14
Efficient and Equitable Natural Language Processing in the Age of Deep Learning (Dagstuhl Seminar 22232)

Authors: Jesse Dodge, Iryna Gurevych, Roy Schwartz, Emma Strubell, and Betty van Aken

Published in: Dagstuhl Reports, Volume 12, Issue 6 (2023)

Abstract
This report documents the program and the outcomes of Dagstuhl Seminar 22232 "Efficient and Equitable Natural Language Processing in the Age of Deep Learning". Since 2012, the field of artificial intelligence (AI) has reported remarkable progress on a broad range of capabilities including object recognition, game playing, speech recognition, and machine translation. Much of this progress has been achieved by increasingly large and computationally intensive deep learning models: training costs for state-of-the-art deep learning models have increased 300,000 times between 2012 and 2018 [1]. Perhaps the epitome of this trend is the subfield of natural language processing (NLP) that over the past three years has experienced even sharper growth in model size and corresponding computational requirements in the word embedding approaches (e.g. ELMo, BERT, openGPT-2, Megatron-LM, T5, and GPT-3, one of the largest models ever trained with 175B dense parameters) that are now the basic building blocks of nearly all NLP models. Recent studies indicate that this trend is both environmentally unfriendly and prohibitively expensive, raising barriers to participation in NLP research [2,3]. The goal of this seminar was to mitigate these concerns and promote equity of access in NLP. References. [1] D. Amodei and D. Hernandez. 2018. AI and Compute. https://openai.com/blog/ai-and-compute [2] R. Schwartz, D. Dodge, N. A. Smith, and O. Etzioni. 2020. Green AI. Communications of the ACM (CACM) [3] E. Strubell, A. Ganesh, and A. McCallum. 2019. Energy and Policy Considerations for Deep Learning in NLP. In Proc. of ACL.
Cite as

Jesse Dodge, Iryna Gurevych, Roy Schwartz, Emma Strubell, and Betty van Aken. Efficient and Equitable Natural Language Processing in the Age of Deep Learning (Dagstuhl Seminar 22232). In Dagstuhl Reports, Volume 12, Issue 6, pp. 14-27, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@Article{dodge_et_al:DagRep.12.6.14,
  author =	{Dodge, Jesse and Gurevych, Iryna and Schwartz, Roy and Strubell, Emma and van Aken, Betty},
  title =	{{Efficient and Equitable Natural Language Processing in the Age of Deep Learning (Dagstuhl Seminar 22232)}},
  pages =	{14--27},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2023},
  volume =	{12},
  number =	{6},
  editor =	{Dodge, Jesse and Gurevych, Iryna and Schwartz, Roy and Strubell, Emma and van Aken, Betty},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.12.6.14},
  URN =		{urn:nbn:de:0030-drops-174549},
  doi =		{10.4230/DagRep.12.6.14},
  annote =	{Keywords: deep learning, efficiency, equity, natural language processing (nlp)}
}
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