Summary of Epistemological Bias As a Means For the Automated Detection Of Injustices in Text, by Kenya Andrews et al.
Epistemological Bias As a Means for the Automated Detection of Injustices in Text
by Kenya Andrews, Lamogha Chiazor
First submitted to arxiv on: 8 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a novel framework for detecting injustice in text, particularly in news media, by combining bias detection models, stereotype detection models, and a lexicon-based approach. The framework uses fine-tuned BERT-based bias detection models to identify instances of implicit biases and prejudice, such as stereotypes, which are often unconscious and pervasive. The authors demonstrate the effectiveness of their approach through an empirical qualitative research study, showing that it can be applied to detect injustices at higher volumes of data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding unfair treatment or discrimination in news articles using a special kind of AI model. It’s like having a superpower to spot when someone is being treated unfairly because of their race, gender, or other characteristics. The researchers used a combination of techniques to develop an AI system that can detect these biases and prejudices, even if they’re not explicitly stated. They tested the system on news articles and showed it could help identify instances of injustice. |
Keywords
» Artificial intelligence » Bert
