Aviv Ben Arie
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Data Science Manager
Intuit |
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Aviv is a Data Science Manager (until recently a Principal Data Scientist) at Intuit, previously a Lead Data Scientist at PayPal. She specializes in fraud prevention and cybersecurity, and in the past, worked at the Prime Minister’s Office in the Cyber Security field, focusing on protocol analysis. Aviv graduated from Tel Aviv University with a double BSc in Computer Science and Life Science while specializing in Bioinformatics and continues to collaborate with Tel Aviv University on research areas revolving around Explainable AI. Aviv is a passionate volunteer (mentor and lecturer) and advocates for multiple Israeli organizations dedicated to promoting women in technology. |
Workshop: Counterfactual Explanations: The Future of Explainable AI
May 25, 2022; 9:30-10:15am, PST
May 25, 2022; 9:30-10:15am, PST
As data scientists, the ability to understand our models’ decisions is important, especially for models that could have a high impact on people’s lives. This may pose several challenges, as most models used in the industry are not inherently explainable. Today, the most popular explainability methods are SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-Agnostic Explanation). Each method offers convenient APIs, backed by solid mathematical foundations, but falls short in intuitiveness and actionability.
In this workshop/article, I will introduce a relatively new model explanation method - Counterfactual Explanations (CFs). CFs are explanations based on minimal changes to a model’s input features that lead the model to output a different (mostly opposite) predicted class. CFs have been shown to be more intuitive for humans to comprehend and provide actionable feedback, compared to traditionalSHAP and LIME methods.. I will review the challenges in this novel field (such as how to ensure that the CF proposes changes which are feasible), provide a birds-eye view of the latest research and give my perspective, based on my research in collaboration with Tel Aviv University, on the various aspects in which CFs can transform the way data science practitioners understand their ML models.
In this workshop/article, I will introduce a relatively new model explanation method - Counterfactual Explanations (CFs). CFs are explanations based on minimal changes to a model’s input features that lead the model to output a different (mostly opposite) predicted class. CFs have been shown to be more intuitive for humans to comprehend and provide actionable feedback, compared to traditionalSHAP and LIME methods.. I will review the challenges in this novel field (such as how to ensure that the CF proposes changes which are feasible), provide a birds-eye view of the latest research and give my perspective, based on my research in collaboration with Tel Aviv University, on the various aspects in which CFs can transform the way data science practitioners understand their ML models.
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