The concept of fairness is gaining popularity in academia and industry. Social media is especially vulnerable to media biases and toxic language and comments. We propose a fair ML pipeline that takes a text as input and determines whether it contains biases and toxic content. Then, based on pre-trained word embeddings, it suggests a set of new words by substituting the biased words, the idea is to lessen the effects of those biases by replacing them with alternative words. We compare our approach to existing fairness models to determine its effectiveness. The results show that our proposed pipeline can detect, identify, and mitigate biases in social media data.
CITATION STYLE
Raza, S., Bashir, S. R., Sneha, & Qamar, U. (2023). Addressing Biases in the Texts Using an End-to-End Pipeline Approach. In Communications in Computer and Information Science (Vol. 1840 CCIS, pp. 100–107). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-37249-0_8
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