Update README.md

README.md

@@ -42,27 +42,15 @@ BanglaClickBERT can be directly used for clickbait detection in Bengali (Bangla)
 
 # Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+- Data Bias: One of the primary challenges with models like BanglaClickBERT is data bias. If the model&#39;s pretraining data is collected from clickbait-prone news websites, there is a risk that the model may exhibit biases present in those sources. This can result in the model being more sensitive to certain types of clickbait and less accurate in detecting others. Efforts should be made to mitigate and address data bias.
 
-Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.
+- Contextual Limitations: While BanglaClickBERT is designed for clickbait detection in Bengali news headlines, it may not perform as effectively in contexts or languages different from the one it was trained on. It may not be suitable for detecting clickbait in non-Bengali languages or in different cultural contexts.
 
+- False Positives and Negatives: Like any clickbait detection model, BanglaClickBERT may produce false positives (genuine content incorrectly identified as clickbait) and false negatives (clickbait content that goes undetected). Users and organizations should be aware of these limitations and consider additional checks.
 
-## Recommendations
+- Evolving Clickbait Techniques: Clickbait techniques are constantly evolving. The model may not be immediately effective at identifying new or sophisticated clickbait strategies. Continuous model updates and monitoring are necessary to keep pace with changing tactics.
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-
-Data Bias: One of the primary challenges with models like BanglaClickBERT is data bias. If the model&#39;s pretraining data is collected from clickbait-prone news websites, there is a risk that the model may exhibit biases present in those sources. This can result in the model being more sensitive to certain types of clickbait and less accurate in detecting others. Efforts should be made to mitigate and address data bias.
-
-Contextual Limitations: While BanglaClickBERT is designed for clickbait detection in Bengali news headlines, it may not perform as effectively in contexts or languages different from the one it was trained on. It may not be suitable for detecting clickbait in non-Bengali languages or in different cultural contexts.
-
-False Positives and Negatives: Like any clickbait detection model, BanglaClickBERT may produce false positives (genuine content incorrectly identified as clickbait) and false negatives (clickbait content that goes undetected). Users and organizations should be aware of these limitations and consider additional checks.
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-Evolving Clickbait Techniques: Clickbait techniques are constantly evolving. The model may not be immediately effective at identifying new or sophisticated clickbait strategies. Continuous model updates and monitoring are necessary to keep pace with changing tactics.
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-Limited Context: The model processes individual headlines and may not consider the broader context of the entire news article or website. Some clickbait may rely on the content within the article itself, which may not be fully addressed by headline analysis alone.
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-Ethical Challenges: While the model is intended to promote accurate and trustworthy information, it may be used unethically, for example, to suppress dissenting voices or censor content. Ethical guidelines and oversight are necessary to prevent misuse.
+- Limited Context: The model processes individual headlines and may not consider the broader context of the entire news article or website. Some clickbait may rely on the content within the article itself, which may not be fully addressed by headline analysis alone.
 
 
 # Training Details