Causal effect of racial bias in data and machine learning algorithms on user persuasiveness & discriminatory decision making: An Empirical Study. (arXiv:2202.00471v1 [cs.CL])

Language data and models demonstrate various types of bias, be it ethnic,
religious, gender, or socioeconomic. AI/NLP models, when trained on the
racially biased dataset, AI/NLP models instigate poor model explainability,
influence user experience during decision making and thus further magnifies
societal biases, raising profound ethical implications for society. The
motivation of the study is to investigate how AI systems imbibe bias from data
and produce unexplainable discriminatory outcomes and influence an individual's
articulateness of system outcome due to the …

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