Call For Papers
*************************************
First Call for Papers
Second Workshop on Ethics in Natural Language Processing
NAACL 2018, New Orleans June 5 or 6 2018
************************************
************************************
NLP is a rapidly maturing field. NLP technologies now play a role in business applications and decision processes that affect billions of people on a daily basis.
However, increasing amounts of data and computational power also mean increased responsibility and new questions for researchers and practitioners.
This one-day, interdisciplinary workshop will bring together researchers and practitioners in NLP with researchers in the humanities, social sciences, public policy, and law to identify and discuss some of the most pressing issues surrounding ethics in NLP, for example:
This one-day, interdisciplinary workshop will bring together researchers and practitioners in NLP with researchers in the humanities, social sciences, public policy, and law to identify and discuss some of the most pressing issues surrounding ethics in NLP, for example:
• Are we inadvertently building unfair biases into our
data sets and models?
• What information is ethical to infer from user data?
• How can we prioritize accountability and transparency
• What are the big-picture ethical consequences and
implications of our work?
The workshop consists of:
The workshop consists of:
• invited talks (given by researchers in NLP but also in
AI, philosophy, the social sciences, or law)
• contributed talks and posters
• panel discussions with NLP researchers, ethicists,
• contributed talks and posters
• panel discussions with NLP researchers, ethicists,
lawyers, and industry practitioners.
We invite submissions on any area of NLP that touches on the following topics:
• Bias in NLP models (e.g., reporting bias, implicit bias).
• Exclusion and inclusion (e.g., exclusion of certain
We invite submissions on any area of NLP that touches on the following topics:
• Bias in NLP models (e.g., reporting bias, implicit bias).
• Exclusion and inclusion (e.g., exclusion of certain
groups or beliefs, how/when to include stakeholders
and representatives for the user population to be
served).
• Overgeneralization (e.g., making false classifications
• Overgeneralization (e.g., making false classifications
on tasks including authorship attribution, NER,
knowledge base population).
• Exposure (e.g., underrepresentation/
overrepresentation of languages or groups).
• Dual use (e.g., the positive and negative aspects of
• Exposure (e.g., underrepresentation/
overrepresentation of languages or groups).
• Dual use (e.g., the positive and negative aspects of
NLP applications, the close relationship between
government and industry interests and NLP research).
• Privacy protection (e.g., anonymization of biomedical documents, best practices for researchers in industry to ensure the privacy of their users’ data, educating the public about how much industry and government may know about them, privacy protection for data
• Privacy protection (e.g., anonymization of biomedical documents, best practices for researchers in industry to ensure the privacy of their users’ data, educating the public about how much industry and government may know about them, privacy protection for data
annotated with non-linguistic features such as
emotion).
• Any other topic which concerns ethical considerations in NLP.
Organizing Committee:
Dirk Hovy, Bocconi University
Margaret Mitchell, Google Research and Machine Intelligence
Shannon Spruit, Technical University Delft
Michael Strube, Heidelberg Institute for Theoretical Studies
• Any other topic which concerns ethical considerations in NLP.
Organizing Committee:
Dirk Hovy, Bocconi University
Margaret Mitchell, Google Research and Machine Intelligence
Shannon Spruit, Technical University Delft
Michael Strube, Heidelberg Institute for Theoretical Studies