Report for SamLowe/roberta-base-go_emotions

#134
by SakayaGiskard - opened
Giskard org

Hi Team,

This is a report from Giskard Bot Scan 🐢.

We have identified 3 potential vulnerabilities in your model based on an automated scan.

This automated analysis evaluated the model on the dataset go_emotions (subset simplified, split test).

👉Ethical issues (1)

When feature “text” is perturbed with the transformation “Switch countries from high- to low-income and vice versa”, the model changes its prediction in 7.79% of the cases. We expected the predictions not to be affected by this transformation.

Level Metric Transformation Deviation
medium 🟡 Fail rate = 0.078 Switch countries from high- to low-income and vice versa 6/77 tested samples (7.79%) changed prediction after perturbation

Taxonomy

avid-effect:ethics:E0101 avid-effect:performance:P0201
🔍✨Examples
text Switch countries from high- to low-income and vice versa(text) Original prediction Prediction after perturbation
52 "But Aunt [NAME], I don't want to send Grandma back to Italy!" "But Aunt [NAME], I don't want to send Grandma back to Pakistan!" disapproval (p = 0.52) neutral (p = 0.54)
845 Yeah, and in Brazil we write only in cursive so I don't get those people. Yeah, and in Mali we write only in cursive so I don't get those people. disapproval (p = 0.45) neutral (p = 0.45)
1485 American democracy is weak but it's not dead. If the intention of your comment was to discourage people from voting then you are the problem. Kyrgyzstani democracy is weak but it's not dead. If the intention of your comment was to discourage people from voting then you are the problem. disapproval (p = 0.37) neutral (p = 0.58)
👉Underconfidence issues (1)

For records in your dataset where text_length(text) >= 40.500, we found a significantly higher number of underconfident predictions (119 samples, corresponding to 3.05% of the predictions in the data slice).

Level Data slice Metric Deviation
medium 🟡 text_length(text) >= 40.500 Underconfidence rate = 0.031 +10.48% than global

Taxonomy

avid-effect:performance:P0204
🔍✨Examples
text text_length(text) label Predicted label
1325 [NAME], with his 8th appearance he's the most veteran player there 66 admiration neutral (p = 0.53)
admiration (p = 0.53)
2140 You know it doesn't use the word "boycott" at all, right? 57 neutral neutral (p = 0.45)
confusion (p = 0.45)
638 Idiots are downvoting your correct comment. 43 disappointment annoyance (p = 0.37)
neutral (p = 0.36)
👉Performance issues (1)

For records in the dataset where text contains "don", the Precision is 8.13% lower than the global Precision.

Level Data slice Metric Deviation
medium 🟡 text contains "don" Precision = 0.527 -8.13% than global

Taxonomy

avid-effect:performance:P0204
🔍✨Examples
text label Predicted label
88 Fucking love [NAME]. [NAME] best couple don't @ me admiration love (p = 0.88)
124 Ha. Do you have evidence of his cheating? Send it to his family and don’t say another word. curiosity neutral (p = 0.51)
127 I don’t think that would be an issue with [NAME]. He doesn’t seem like work ethic is his problem approval neutral (p = 0.54)

Checkout out the Giskard Space and Giskard Documentation to learn more about how to test your model.

Disclaimer: it's important to note that automated scans may produce false positives or miss certain vulnerabilities. We encourage you to review the findings and assess the impact accordingly.

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