4th Workshop on Gender Bias in Natural Language Processing (in conjunction with NAACL 2022)

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 workshop 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.

4th Workshop on Gender Bias in Natural Language Processing

At NAACL in Seattle, USA, during July 10-15, 2022

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 workshop 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.