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Mitigating Framing Bias with Polarity Minimization Loss: Limitations, Ethics Statement & Referencesby@mediabias
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Mitigating Framing Bias with Polarity Minimization Loss: Limitations, Ethics Statement & References

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In this paper, researchers address framing bias in media, a key driver of political polarization. They propose a new loss function to minimize polarity differences in reporting, reducing bias effectively.
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This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license.

Authors:

(1) Yejin Bang, Centre for Artificial Intelligence Research (CAiRE), The Hong Kong University of Science and Technology;

(2) Nayeon Lee, Centre for Artificial Intelligence Research (CAiRE), The Hong Kong University of Science and Technology;

(3) Pascale Fung, Centre for Artificial Intelligence Research (CAiRE), The Hong Kong University of Science and Technology.


6.1. Limitations

The study is limited by its adherence to the benchmark’s English-based task setup. The analysis is constrained to political ideologies in the United States and the English language. Additionally, the BART model’s 1024 sub-token input limit restricts the number of biased source articles that can be included as an input. It is important to note that these limitations, while potentially impacting the scope of the study’s findings, are not uncommon in natural language processing research. Nonetheless, future research may benefit from addressing these limitations by exploring alternative methods for a broader range of political ideologies (NonU.S. political ideologies) and languages, as well as incorporating longer input texts to capture a more comprehensive range of source articles.

6.2. Ethics Statement

The issue of biased articles with framing has been extensively studied, as it can lead to polarization by influencing readers’ opinions toward a certain person, group, or topic. To address this problem, our research focuses on introducing a loss function that can be incorporated to enable the model to reduce framing bias in the generated summary.


However, it is important to recognize that automatic technologies can also have unintended negative consequences if not developed with careful consideration of their broader impacts. For example, machine learning models can introduce bias in their output, replacing known source bias with another form of bias (Lee et al., 2022). To mitigate this risk, Lee et al. (2022) have suggested including explicit mention of the source articles alongside automatically generated neutral summaries. Furthermore, while our work aims to remove framing bias in human-generated articles, there is the potential for hallucination in the generation, which is a well-known problem of generative models (Ji et al., 2023). Thus, it is important to equip a guardrail (e.g., a provision of source reference) if such automatic technology is implemented for actual use cases.


Despite these challenges, our research can contribute to the effort of mitigating human-generated framing bias in order to reduce polarization in society. One of the use cases can be to aid human experts in the process of providing multi-view synthesized articles without framing bias. In terms of broader societal impact, we hope our work can help online users access more depolarized information online.

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