TY - GEN
T1 - Adhering, Steering, and Queering
T2 - 2020 ACM CHI Conference on Human Factors in Computing Systems, CHI 2020
AU - Strengers, Yolande
AU - Qu, Lizhen
AU - Xu, Qiongkai
AU - Knibbe, Jarrod
N1 - Publisher Copyright:
© 2020 ACM.
PY - 2020/4/21
Y1 - 2020/4/21
N2 - Natural Language Generation (NLG) supports the creation of personalized, contextualized, and targeted content. However, the algorithms underpinning NLG have come under scrutiny for reinforcing gender, racial, and other problematic biases. Recent research in NLG seeks to remove these biases through principles of fairness and privacy. Drawing on gender and queer theories from sociology and Science and Technology studies, we consider how NLG can contribute towards the advancement of gender equity in society. We propose a conceptual framework and technical parameters for aligning NLG with feminist HCI qualities. We present three approaches: (1) adhering to current approaches of removing sensitive gender attributes, (2) steering gender differences away from the norm, and (3) queering gender by troubling stereotypes. We discuss the advantages and limitations of these approaches across three hypothetical scenarios; newspaper headlines, job advertisements, and chatbots. We conclude by discussing considerations for implementing this framework and related ethical and equity agendas.
AB - Natural Language Generation (NLG) supports the creation of personalized, contextualized, and targeted content. However, the algorithms underpinning NLG have come under scrutiny for reinforcing gender, racial, and other problematic biases. Recent research in NLG seeks to remove these biases through principles of fairness and privacy. Drawing on gender and queer theories from sociology and Science and Technology studies, we consider how NLG can contribute towards the advancement of gender equity in society. We propose a conceptual framework and technical parameters for aligning NLG with feminist HCI qualities. We present three approaches: (1) adhering to current approaches of removing sensitive gender attributes, (2) steering gender differences away from the norm, and (3) queering gender by troubling stereotypes. We discuss the advantages and limitations of these approaches across three hypothetical scenarios; newspaper headlines, job advertisements, and chatbots. We conclude by discussing considerations for implementing this framework and related ethical and equity agendas.
KW - feminist hci
KW - natural language generation
UR - http://www.scopus.com/inward/record.url?scp=85091274963&partnerID=8YFLogxK
U2 - 10.1145/3313831.3376315
DO - 10.1145/3313831.3376315
M3 - Conference contribution
AN - SCOPUS:85091274963
T3 - Conference on Human Factors in Computing Systems - Proceedings
BT - CHI 2020 - Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems
PB - Association for Computing Machinery
Y2 - 25 April 2020 through 30 April 2020
ER -