2on Workshop on Gender Bias in Natural Language Processing
At COLING 2020 in Barcelona on 13th December 2020
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 a 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..
Marta R. Costa-jussà, Universitat Politècnica de Catalunya, Barcelona
Christian Hardmeier, Uppsala University
Kellie Webster, Google AI Language, New York
Will Radford, Canva, Sydney