Visualizations For Justifying Machine Learning Predictions David - PowerPoint PPT Presentation

Mar 17, 2024 •25 likes •145 views

Visualizations For Justifying Machine Learning Predictions David Johnson 1 Motivation Strengths of ML allowed expansion to diverse fields Fields and contexts far removed from traditional ML Users not trained in ML Eg. Medical

Visualizations For Justifying Machine Learning Predictions David Johnson 1
Motivation • Strengths of ML allowed expansion to diverse fields • Fields and contexts far removed from traditional ML • Users not trained in ML • Eg. Medical field: Doctors use ML to predict disease given symptoms • The ML is a black box to them: Input → ? → Output 2
Previous Work Figure: Biran, O., MckKeown, K. (2014). Justification Narratives for Individual Classifications. AutoML workshop at ICML 2014 . 3
Previous Work Some issues: ● The vis relies on NLG quite a bit ● Vis isn’t very clear for non-experts (what is Y-Prior? What is Slope?) Figure: Biran, O., MckKeown, K. (2014). Justification Narratives for Individual Classifications. AutoML workshop at ICML 2014 . 4
Goals • Justify a ML prediction to a non-expert user • Show features providing evidence for/against the prediction • Select and visualize key features • Focus on interpretable models • Simplicity not complexity... 5 Figure: Munzner, T. (2014). Visualization Analysis and Design. CRC Press.
Goals • Justify a ML prediction to a non-expert user • Show features providing evidence for/against the prediction • Select and visualize key features • Focus on interpretable models • Simplicity not complexity... 6 Figure: Munzner, T. (2014). Visualization Analysis and Design. CRC Press.
Feature Visualizing Vis can show effect and importance 1 • Effect: extent to which a feature contributes toward or against prediction • Importance: Expected effect of the feature for a particular class (mean feature value for the class) 1 Biran, O., MckKeown, K. (2014). Justification Narratives for Individual Classifications. AutoML workshop at ICML 2014 . 7
Abstraction • Some raw data: arbitrary data with training/test sets • Task abstraction: - Analyze: discover, enjoy, derive • Data abstraction: - Items, attributes, values in a table • Two quantitative variables: effect, importance -- scatterplot effective 8
Demo 9
Future Direction NLG implemented Full web app implementation Expanded scope: 10
Thanks! Questions? 11
12

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