Gender bias, among other demographic biases (e.g. race, nationality, religion), in machine-learned models is 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. Webster et al., 2018), and to change the learning algorithms themselves (e.g. Bolukbasi et al., 2016). 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.
Our proposal follows up three successful previous editions of the Workshop collocated with ACL 2019, COLING 2020, and ACL-IJCNLP 2021, respectively. As in the two previous years (2020 and 2021), special efforts will be made this year to encourage a careful and reflective approach to gender bias by the means of separately reviewed bias statements (Blodgett et al., 2020; Hardmeier et al., 2021). This helps to make clear (a) what system behaviors are considered as bias in the work, and (b) why those behaviors are harmful, in what ways, and to whom. 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. We will keep pushing the integration of several communities such as social sciences as well as a wider representation of approaches dealing with bias.
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, identifying and assessing 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 NAACL 2022 guidelines. Supplementary material can be added, but should not be central to the argument of the paper. Blind submission is required.
Each paper should include a 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)). More information on this requirement, which was successfully introduced at GeBNLP 2020, can be found on the workshop website. We also 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.Non-archival option
The authors have the option of submitting research as non-archival, meaning that the paper will not be published in the conference proceedings. We expect these submissions to describe the same quality of work and format as archival submissions.
Important dates Apr 8 , Apr 15 2022: Workshop Paper Due Date May 6, 2022: Notification of Acceptance May 20, 2022: Camera-ready papers due July 15, 2022: Workshop Dates
Kellie Webster, Research Scientist at Google Research Kai-Wei Chang, Assistant Professor, University of California
Marta R. Costa-jussà, Meta AI, Paris Christian Hardmeier, Uppsala University Hila Gonen, FAIR and University of Washington Christine Basta, Universitat Politècnica de Catalunya, Barcelona Gabriel Stanovsky, Hebrew University of Jerusalem -------------- next part -------------- A non-text attachment was scrubbed... Name: not available Type: text/html Size: 9136 bytes Desc: not available URL: <https://mailman.uib.no/public/corpora/attachments/20220407/4585537a/attachment.txt>