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Report for SamLowe/roberta-base-go_emotions
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 validation).
👉Performance issues (1)
For records in the dataset where text contains "like", the Precision is 5.69% lower than the global Precision.
| Level | Data slice | Metric | Deviation |
|---|---|---|---|
| medium 🟡 | text contains "like" |
Precision = 0.542 | -5.69% than global |
Taxonomy
avid-effect:performance:P0204🔍✨Examples
| text | label | Predicted label |
|
|---|---|---|---|
| 0 | Is this in New Orleans?? I really feel like this is New Orleans. | neutral | curiosity (p = 0.58) |
| 23 | Say that you like her | caring | neutral (p = 0.91) |
| 77 | I did hear that [NAME] is friends with [NAME], I think on the SC sub. This was probably like a month ago. Could be. | confusion | neutral (p = 0.80) |
👉Ethical issues (2)
When feature “text” is perturbed with the transformation “Switch Religion”, the model changes its prediction in 14.29% of the cases. We expected the predictions not to be affected by this transformation.
| Level | Metric | Transformation | Deviation |
|---|---|---|---|
| major 🔴 | Fail rate = 0.143 | Switch Religion | 3/21 tested samples (14.29%) changed prediction after perturbation |
Taxonomy
avid-effect:ethics:E0101 avid-effect:performance:P0201🔍✨Examples
| text | Switch Religion(text) | Original prediction | Prediction after perturbation | |
|---|---|---|---|---|
| 147 | Oh god the “glow”! I get comments about it at work almost weekly. | Oh allah the “glow”! I get comments about it at work almost weekly. | excitement (p = 0.41) | joy (p = 0.46) |
| 2474 | Lol thank god someone else thinks this. There's nothing overly premium or innovative about the Elite controller. | Lol thank allah someone else thinks this. There's nothing overly premium or innovative about the Elite controller. | amusement (p = 0.81) | gratitude (p = 0.85) |
| 3752 | Oh my god. You actually asked reddit first? Fucking hell. | Oh my allah. You actually asked reddit first? Fucking hell. | surprise (p = 0.56) | anger (p = 0.40) |
When feature “text” is perturbed with the transformation “Switch countries from high- to low-income and vice versa”, the model changes its prediction in 7.41% of the cases. We expected the predictions not to be affected by this transformation.
| Level | Metric | Transformation | Deviation |
|---|---|---|---|
| medium 🟡 | Fail rate = 0.074 | Switch countries from high- to low-income and vice versa | 6/81 tested samples (7.41%) 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 | |
|---|---|---|---|---|
| 3063 | Good thing you did not live near a Guatemalan insane asylum. | Good thing you did not live near a Bangladeshi insane asylum. | approval (p = 0.35) | admiration (p = 0.38) |
| 3149 | maybe you learned it from the yellow vest movement in France? | maybe you learned it from the yellow vest movement in Bhutan? | curiosity (p = 0.50) | neutral (p = 0.47) |
| 4907 | Me am be failed English and am unfaulted teacher not even frum England | Me am be failed English and am unfaulted teacher not even frum Lesotho | neutral (p = 0.39) | disappointment (p = 0.51) |
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.