Stanford Seminar - ML Explainability Part 5 I Future of Model Understanding | Summary and Q&A

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November 6, 2022
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Stanford Seminar - ML Explainability Part 5 I Future of Model Understanding

TL;DR

The content discusses open problems and future directions in the field of explainable AI, including improving post-hoc explanation methods, exploring intersections with other pillars of trustworthy ML, and developing new tools and interfaces for model understanding.

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Questions & Answers

Q: What are the limitations of existing post-hoc explanation methods?

Existing methods suffer from instability, inconsistency, fragility, and lack of faithfulness. They can generate drastically different explanations for the same point and require a large number of perturbations, which is computationally intensive. There is a need for more reliable methods.

Q: How can Bayesian versions of Lime and SHAP help address these limitations?

Bayesian versions provide uncertainty intervals for feature importance, giving insights into the algorithm's confidence in its explanations. They allow for consolidated and comparative analysis of different explanations, reducing the ambiguity caused by changing the number of perturbations.

Q: What are the open problems in theoretical analysis of model interpretations?

One open problem is characterizing when post-hoc explanation methods successfully or unsuccessfully capture the behavior of underlying models. Understanding the nature and meaningfulness of prototypes and attention weights learned by deep nets with added layers is another challenge.

Q: What are the privacy implications of model interpretations?

Model interpretations can potentially expose sensitive information from datasets, posing privacy risks. There is a need to assess the vulnerabilities and privacy attacks enabled by providing explanations. Exploring the effectiveness of differentially private explanations can help mitigate these risks.

Summary & Key Takeaways

  • New methods are needed to enhance the reliability of post-hoc explanations by addressing limitations such as instability, inconsistency, fragility, and lack of faithfulness.

  • Bayesian versions of Lime and SHAP can provide uncertainty intervals for feature importance, helping to consolidate and compare explanations, as well as estimate the required number of perturbations for user-defined levels of confidence.

  • Exploring intersections between interpretability and other pillars of trustworthy ML, such as robustness, fairness, and privacy, is crucial to understanding their implications and potential trade-offs.

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