Papers by Akihiro Nishi
On Measures of Biases and Harms in NLP (2022.findings-aacl)
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Sunipa Dev, Emily Sheng, Jieyu Zhao, Aubrie Amstutz, Jiao Sun, Yu Hou, Mattie Sanseverino, Jiin Kim, Akihiro Nishi, Nanyun Peng, Kai-Wei Chang
| Challenge: | Recent studies show that natural language processing (NLP) technologies propagate societal biases about demographic groups associated with attributes such as gender, race, and nationality. |
| Approach: | They propose a framework for harms and questions to help practitioners understand biases . they propose measurable measures to detect and mitigate biased groups . |
| Outcome: | The proposed framework provides a framework for harms and questions for practitioners to answer to guide the development of bias measures. |
Socially Aware Bias Measurements for Hindi Language Representations (2022.naacl-main)
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| Challenge: | Language representations are an efficient tool used across NLP, but they are strife with encoded societal biases. |
| Approach: | They investigate the encoded biases in Hindi language representations based on cultural and historical contexts . they emphasize the necessity of social-awareness along with linguistic and grammatical artefacts when modeling language representation . |
| Outcome: | The proposed model reflects the cultural and cultural diversity of the region in which it is used . the model is based on the language and culture of the language being used based upon the study . |