Update README.md

README.md

@@ -44,7 +44,7 @@ The same model is provided in two different formats: PyTorch and ONNX.
 | ----------- | ----------- | 
 | Primary intended uses | Inference for sentiment classification (classifying whether a statement is positive or negative) | 
 | Primary intended users | Anyone | 
-| Out-of-scope uses | This model is already fine-tuned and quantized to INT8. It is not suitable for further fine-tuning in this form. To fine-tune your own model, you can start with [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english). |
+| Out-of-scope uses | This model is already fine-tuned and quantized to INT8. It is not suitable for further fine-tuning in this form. To fine-tune your own model, you can start with [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english). The model should not be used to intentionally create hostile or alienating environments for people. |
 
 #### Load PyTorch model with Optimum
 ```python
@@ -94,12 +94,12 @@ model = ORTModelForSequenceClassification.from_pretrained(
 | Data | The data that make up the model are movie reviews from authors on the internet. |
 | Human life | The model is not intended to inform decisions central to human life or flourishing. It is an aggregated set of movie reviews from the internet. | 
 | Mitigations | No additional risk mitigation strategies were considered during model development. |
-| Risks and harms | The data are biased toward the particular reviewers' opinions and the judges (labelers) of the data. The extent of the risks involved by using the model were considered but remain unknown.|
+| Risks and harms | The data are biased toward the particular reviewers' opinions and the judges (labelers) of the data. 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. Beyond this, the extent of the risks involved by using the model remain unknown.|
 | Use cases | - | 
 
 | Caveats and Recommendations |
 | ----------- | 
-| There are no additional caveats or recommendations for this model. |
+| Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. There are no additional caveats or recommendations for this model. |
 
 # BibTeX Entry and Citation Info
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