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Rsqrd AI: Incorporating Priors with Feature Attribution on Text Classification

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Sanjana Chowdhury

The document discusses incorporating priors with feature attribution in text classification using machine learning techniques, emphasizing explainability. Various metrics are mentioned, including classification and fairness metrics, alongside references to research papers on the subject. The document outlines results related to toxicity assessments driven by these models, specifically referencing applications like content related to 'gay pride'.

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Proprietary + Confidential Frederick Liu 7/17/19 @ Robust AI Incorporating priors with feature attribution on text classification
Proprietary + ConfidentialProprietary + Confidential Machine learning .6 .2 .1 .3 .7 .1 .8 .7 .5.3 .1 .4 .9 .2 .0 .6.8 .2.1.6Toxic … … … … … … … Neutral … … … … … … Toxic … … … … … … … Toxic … … … … … … Neutral … … … … … … Training Inference Gay pride is in June. .6 .2 .1 .3 .7 .1 .8 .7 .5.3 .1 .4 .9 .2 .0 .6.8 .2.1.6 95% Toxic
Proprietary + ConfidentialProprietary + Confidential Machine learning + Explainability .6 .2 .1 .3 .7 .1 .8 .7 .5.3 .1 .4 .9 .2 .0 .6.8 .2.1.6Toxic … … … … … … … Neutral … … … … … … Toxic … … … … … … … Toxic … … … … … … Neutral … … … … … … Training Inference Gay pride is in June. .6 .2 .1 .3 .7 .1 .8 .7 .5.3 .1 .4 .9 .2 .0 .6.8 .2.1.6 95% Toxic Gay Pride is in June 90% 1% 1% 1% 2%
Proprietary + ConfidentialProprietary + Confidential Machine learning + Regularization Toxic … … … … … … … Neutral … … … … … … Toxic … … … … … … … Toxic … … … … … … Neutral … … … … … … Training Inference Gay pride is in June. .5 .2 .1 .3 .5 .1 .5 .5 .5.3 .1 .4 .5 .2 .0 .5.5 .2.1.5 85% Toxic .5 .2 .1 .3 .5 .1 .5 .5 .5.3 .1 .4 .5 .2 .0 .5.5 .2.1.5
Proprietary + ConfidentialProprietary + Confidential Machine learning + Regularization + Explainability Toxic … … … … … … … Neutral … … … … … … Toxic … … … … … … … Toxic … … … … … … Neutral … … … … … … Training Inference Gay pride is in June. 15% Toxic Gay Pride is in June He is an impolite gay 0% .7 .2 .1 .3 .7 .1 .7 .5.3.4 .9 .2 .1 .6.8 .20. 1 .1 .2 .5 .7 .2 .1 .3 .7 .1 .7 .5.3.4 .9 .2 .1 .6.8 .20. 1 .1 .2 .5 + person
Proprietary + ConfidentialProprietary + Confidential Regularizing + Explainability → Controllability .6 .2 .1 .3 .7 .1 .8 .7 .5.3 .1 .4 .9 .2 .0 .6.8 .2 .1.6 Explanation
Proprietary + ConfidentialProprietary + Confidential Regularizing + Explainability → Controllability .6 .2 .1 .3 .7 .1 .3 .7 .5.3 .9 .4 .9 .2 .0 .6.8 .4 .1.7 Explanation More Red! Less Green!
Proprietary + ConfidentialProprietary + Confidential Explainability - Integrated Gradients Link to paper - https://arxiv.org/pdf/1703.01365.pdf
Proprietary + ConfidentialProprietary + Confidential Explainability + Regularization
Proprietary + ConfidentialProprietary + Confidential Results - Classification Metric
Proprietary + ConfidentialProprietary + Confidential Results - Fairness Metric
Proprietary + ConfidentialProprietary + Confidential Results - Shift in embedding
Proprietary + Confidential Thank You Link to paper - https://arxiv.org/pdf/1906.08286.pdf Sign up if you want to know more: bit.ly/model-interpret-interest

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Rsqrd AI: Incorporating Priors with Feature Attribution on Text Classification

  • 1. Proprietary + Confidential Frederick Liu 7/17/19 @ Robust AI Incorporating priors with feature attribution on text classification
  • 2. Proprietary + ConfidentialProprietary + Confidential Machine learning .6 .2 .1 .3 .7 .1 .8 .7 .5.3 .1 .4 .9 .2 .0 .6.8 .2.1.6Toxic … … … … … … … Neutral … … … … … … Toxic … … … … … … … Toxic … … … … … … Neutral … … … … … … Training Inference Gay pride is in June. .6 .2 .1 .3 .7 .1 .8 .7 .5.3 .1 .4 .9 .2 .0 .6.8 .2.1.6 95% Toxic
  • 3. Proprietary + ConfidentialProprietary + Confidential Machine learning + Explainability .6 .2 .1 .3 .7 .1 .8 .7 .5.3 .1 .4 .9 .2 .0 .6.8 .2.1.6Toxic … … … … … … … Neutral … … … … … … Toxic … … … … … … … Toxic … … … … … … Neutral … … … … … … Training Inference Gay pride is in June. .6 .2 .1 .3 .7 .1 .8 .7 .5.3 .1 .4 .9 .2 .0 .6.8 .2.1.6 95% Toxic Gay Pride is in June 90% 1% 1% 1% 2%
  • 4. Proprietary + ConfidentialProprietary + Confidential Machine learning + Regularization Toxic … … … … … … … Neutral … … … … … … Toxic … … … … … … … Toxic … … … … … … Neutral … … … … … … Training Inference Gay pride is in June. .5 .2 .1 .3 .5 .1 .5 .5 .5.3 .1 .4 .5 .2 .0 .5.5 .2.1.5 85% Toxic .5 .2 .1 .3 .5 .1 .5 .5 .5.3 .1 .4 .5 .2 .0 .5.5 .2.1.5
  • 5. Proprietary + ConfidentialProprietary + Confidential Machine learning + Regularization + Explainability Toxic … … … … … … … Neutral … … … … … … Toxic … … … … … … … Toxic … … … … … … Neutral … … … … … … Training Inference Gay pride is in June. 15% Toxic Gay Pride is in June He is an impolite gay 0% .7 .2 .1 .3 .7 .1 .7 .5.3.4 .9 .2 .1 .6.8 .20. 1 .1 .2 .5 .7 .2 .1 .3 .7 .1 .7 .5.3.4 .9 .2 .1 .6.8 .20. 1 .1 .2 .5 + person
  • 6. Proprietary + ConfidentialProprietary + Confidential Regularizing + Explainability → Controllability .6 .2 .1 .3 .7 .1 .8 .7 .5.3 .1 .4 .9 .2 .0 .6.8 .2 .1.6 Explanation
  • 7. Proprietary + ConfidentialProprietary + Confidential Regularizing + Explainability → Controllability .6 .2 .1 .3 .7 .1 .3 .7 .5.3 .9 .4 .9 .2 .0 .6.8 .4 .1.7 Explanation More Red! Less Green!
  • 8. Proprietary + ConfidentialProprietary + Confidential Explainability - Integrated Gradients Link to paper - https://arxiv.org/pdf/1703.01365.pdf
  • 9. Proprietary + ConfidentialProprietary + Confidential Explainability + Regularization
  • 10. Proprietary + ConfidentialProprietary + Confidential Results - Classification Metric
  • 11. Proprietary + ConfidentialProprietary + Confidential Results - Fairness Metric
  • 12. Proprietary + ConfidentialProprietary + Confidential Results - Shift in embedding
  • 13. Proprietary + Confidential Thank You Link to paper - https://arxiv.org/pdf/1906.08286.pdf Sign up if you want to know more: bit.ly/model-interpret-interest