TY  - JOUR
TI  - Gender Bias in Occupation Classification from the New York Times Obituaries
AB  - Technological developments such as artificial intelligence can strengthen social prejudices prevailing in society, regardless of the developer's intention. Therefore, researchers should be aware of the ethical issues that may arise from a developed product/solution. In this study, we investigate the effect of gender bias on occupational classification. For this purpose, a new dataset was created by collecting obituaries from the New York Times website and is provided in two different versions: With and without gender indicators. Category distributions from this dataset show that gender and occupation variables have dependence. Thus, gender affects occupation classification. To test the effect, we perform occupation classification using SVM (Support Vector Machine), HAN (Hierarchical Attention Network), and DistilBERT-based classifiers. Moreover, to get further insights into the relationship of gender and occupation in classification problems, a multi-tasking model in which occupation and gender are learned together is evaluated. Experimental results reveal that there is a gender bias in job classification. Keywords: Gender Bias, Occupation Classification, Multi-task Learning, Obituaries.
AU  - Atik, Ceren
AU  - Tekir, Selma
DO  - 10.21205/deufmd.2022247109
PY  - 2022
JO  - Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi
VL  - 24
IS  - 71
SN  - 1302-9304
SP  - 425
EP  - 436
DB  - TRDizin
UR  - http://search/yayin/detay/1110196
ER  -