The Double-Edged Sword of Ai and Big Data in Interpreting Interpretability: Tensions and Opportunities

Shan Shan, Zhejiang University

This paper primarily focuses on the trustworthiness of AI and big data in the realm of social-historical research. Trust in research is complex, so this paper elaborates on this concept, dividing it into three critical aspects: the integrity and quality of data, the transparency of data processing methods, and ethical considerations in the use and dissemination of findings. This paper argues that the significant challenge of AI arises from the often opaque nature of its algorithms, which can conflict with the emphasis of contextual social-historical research on context and narrative interpretability. Additionally, there is a pressing ethical concern regarding the potential of AI and big data to reinforce societal biases related to gender, race, or socioeconomic status.

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 Presented in Session 194. Implications of New Techniques on Data Infrastructure