Update README.md

README.md

@@ -66,6 +66,18 @@ This model will likely work worse on non-legal text in non-English languages ori
 
 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.
 
+## Considerations about the training dataset
+
+### Social Impact of Dataset
+As described in the dataset card, the internal variation allows contextual privacy rules to be learned. If robust mechanisms for this are developed they can applied more broadly. 
+As discussed in ``On the Opportunities and Risks of Foundation Models'', legal language models can help improve access to justice in various ways. 
+But they can also be used in potentially harmful ways. While such models are not ready for most production environments and are the subject of significant research, 
+we ask that model users and model creators using this model, particularly when creating generative models (e.g. attaching a decoder), consider the impacts of their model and make a good faith effort to weigh the benefits against the harms of their method. 
+As our license, the training dataset license also restricts commercial usage.
+
+## Discussion of Biases
+The data reflects the biases of governments and courts. As discussed in their work [Pile of Law](https://arxiv.org/abs/2207.00220), these can be significant, though more recent text will likely be less overtly toxic. 
+Please consult the above statement and keep it in mind in the use and/or any modification of this model, implementing responsible use.
 
 ## Recommendations