add up to social impact and biases

README.md

@@ -290,4 +290,18 @@ We kept preprocessing minimal so that the process was as language agnostic as po
 
 ## Considerations for Using the Data
 
-### Social Impact of Dataset
+### Social Impact of Dataset
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+This dataset covers a number of low-resourced languages. This makes it a potentially useful resource, but due to the limited amount of data and domains, care must be taken not to overclaim performance or coverage.
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+### Discussion of Biases
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+Our work aims to broaden natural language processing coverage by allowing practitioners to identify relevant data in more languages. However, we note that language identification is inherently a normative activity that risks excluding minority dialects, scripts, or entire microlanguages from a macrolanguage. Choosing which languages to cover may reinforce power imbalances, as only some groups gain access to language processing technologies.
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+In addition, errors in language identification can have a significant impact on downstream performance, particularly (as is often the case) when a system is used as a `black box'. The performance of our classifier is not equal across languages which could lead to worse downstream performance for particular groups. We mitigate this by providing metrics by class.
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+### Licensing Information
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+### Citation Information
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+### Contributions