13th December, Barcelona
Gender and other demographic biases in machine-learned models are of increasing interest to the scientific community and industry. Models of natural language are highly affected by such biases, which are present in widely used products and can lead to poor user experiences. There is a growing body of research into improved representations of gender in NLP models. Key example approaches are to build and use balanced training and evaluation datasets (e.g. Reddy & Knight, 2016, Webster et al., 2018, Maadan et al., 2018), and to change the learning algorithms themselves (e.g. Bolukbasi et al., 2016, Chiappa et al., 2018). While these approaches show promising results, there is more to do to solve identified and future bias issues. In order to make progress as a field, we need to create widespread awareness of bias and a consensus on how to work against it, for instance by developing standard tasks and metrics. Our workshop provides a forum to achieve this goal.
Topics of interest
We invite submissions of technical work exploring the detection, measurement, and mediation of gender bias in NLP models and applications. Other important topics are the creation of datasets exploring demographics such as metrics to identify and assess relevant biases or focusing on fairness in NLP systems. Finally, the workshop is also open to non-technical work addressing sociological perspectives, and we strongly encourage critical reflections on the sources and implications of bias throughout all types of work.
Paper Submission Information
Submissions will be accepted as short papers (4-6 pages) and as long papers (8-10 pages), plus additional pages for references, following the *ACL 2020 guidelines*. Supplementary material can be added. Blind submission is required.
This year, *we introduce the requirement that papers include a bias statement *which explicitly defines (a) what system behaviours are considered as bias in the work and (b) why those behaviours are harmful, in what ways, and to whom (cf. Blodgett et al. (2020) <https://arxiv.org/abs/2005.14050>). We encourage authors to engage with definitions of bias and other relevant concepts such as prejudice, harm, discrimination from outside NLP, especially from social sciences and normative ethics, in this statement and in their work in general.
Please refer to this link <https://genderbiasnlp.talp.cat/gebnlp2020/how-to-write-a-bias-statement/> on *how to write a bias statement.*
Paper submission link: https://www.softconf.com/coling2020/GeBNLP/
Important dates
Aug 4. Anonymity period begins
Sep 4. Deadline for submission
Oct 9. Notification of acceptance
Nov 1. Camera-ready submission
Keynote
Natalie Schluter, IT University of Copenhagen, Denmark
Dirk Hovy, Bocconi University, Italy
Programme Committee
Svetlana Kiritchenko, National Research Council of Canada, Canada
Kai-Wei Chang, University of Washington, US
Sharid Loáiciga, University of Gothenburg, Sweden
Zhengxian Gong, Soochow University, China
Marta Recasens, Google, US
Bonnie Webber, University of Edinburgh, UK
Ben Hachey, Harrison.ai Australia
Mercedes García Martínez, Pangeanic, Spain
Sonja Schmer-Galunder, Smart Information Flow Technologies, US
Matthias Gallé, NAVER LABS Europe, France
Sverker Sikström, Lund University, Sweden
Dorna Behdadi, University of Gothenburg, Sweden
Steve Wilson, University of Edinburgh, UK
Kathleen Siminyu, Artificial Intelligence for Development – Africa Network
Dirk Hovy, Bocconi University, Italy
Carla Pérez Almendros, Cardiff University, UK
Jenny Björklund, Uppsala University, Sweden
Organizers
Marta R. Costa-jussà, Universitat Politècnica de Catalunya, Barcelona
Christian Hardmeier, Uppsala University
Kellie Webster, Google AI Language, New York
Will Radford, Canva, Sydney
Contact persons
Marta R. Costa-jussà: marta (dot) ruiz (at) upc (dot) edu -------------- next part -------------- A non-text attachment was scrubbed... Name: not available Type: text/html Size: 23444 bytes Desc: not available URL: <https://mailman.uib.no/public/corpora/attachments/20200823/69f52579/attachment.txt>