Podcast
Questions and Answers
In the context of machine learning, what does Explainable AI (XAI) primarily aim to ensure?
In the context of machine learning, what does Explainable AI (XAI) primarily aim to ensure?
- That algorithmic decisions and their basis are understandable to end-users and stakeholders. (correct)
- That all algorithms are built using open-source code.
- That machine learning models achieve 100% accuracy.
- That AI systems are deployed rapidly, regardless of transparency.
Which of the following is a key legal aspect driving the need for explainability in machine learning?
Which of the following is a key legal aspect driving the need for explainability in machine learning?
- The Patriot Act.
- The Sarbanes-Oxley Act.
- The General Data Protection Regulation (GDPR). (correct)
- The Health Insurance Portability and Accountability Act (HIPAA).
Why can achieving explainability in machine learning be difficult regarding model complexity?
Why can achieving explainability in machine learning be difficult regarding model complexity?
- Complex models always have fewer parameters than simpler ones.
- Complex models are designed to be intentionally opaque for security reasons.
- Complex models establish multiple interactions between input variables, making it hard to explain output as a function of input. (correct)
- Complex models use only categorical data, which is hard to interpret.
What is a primary limitation of relying solely on interpretable models like decision trees for explainability?
What is a primary limitation of relying solely on interpretable models like decision trees for explainability?
In the context of Explainable AI, what does it mean for a model to be 'biased'?
In the context of Explainable AI, what does it mean for a model to be 'biased'?
According to the content, what is one potential consequence of using black box machine learning models for high-stakes decisions?
According to the content, what is one potential consequence of using black box machine learning models for high-stakes decisions?
What is the central idea behind using Shapley Values in Explainable AI?
What is the central idea behind using Shapley Values in Explainable AI?
What does the 'Dummy' axiom in Shapley Value theory imply?
What does the 'Dummy' axiom in Shapley Value theory imply?
If $f(x)$ is a sum of two models $g(x)$ and $h(x)$, according to the additivity axiom of Shapley Values, how is the Shapley value of $f(x)$ determined?
If $f(x)$ is a sum of two models $g(x)$ and $h(x)$, according to the additivity axiom of Shapley Values, how is the Shapley value of $f(x)$ determined?
How does using machine learning to classify X-ray images exemplify the problem of 'right for the wrong reason'?
How does using machine learning to classify X-ray images exemplify the problem of 'right for the wrong reason'?
What does the term 'adversarial attack' refer to in the context of machine learning?
What does the term 'adversarial attack' refer to in the context of machine learning?
What is the main goal of the LIME (Local Interpretable Model-Agnostic Explanations) method?
What is the main goal of the LIME (Local Interpretable Model-Agnostic Explanations) method?
Why is it important to validate the logic of machine learning models?
Why is it important to validate the logic of machine learning models?
According to the content, what scenario exemplifies how machine learning algorithms can perpetuate historical biases?
According to the content, what scenario exemplifies how machine learning algorithms can perpetuate historical biases?
What is a primary goal of Explainable AI (XAI) as defined by DARPA?
What is a primary goal of Explainable AI (XAI) as defined by DARPA?
Which of the following target audience would be interested in XAI to trust the model itself and gain scientific knowledge?
Which of the following target audience would be interested in XAI to trust the model itself and gain scientific knowledge?
Which of the following is NOT considered a benefit of machine learning explanations?
Which of the following is NOT considered a benefit of machine learning explanations?
What best desribes interpretable models?
What best desribes interpretable models?
Post-hoc Explanation
Post-hoc Explanation
Which of the following machine learning explainability frameworks is considered.
Which of the following machine learning explainability frameworks is considered.
Which method approximates an underlying function?
Which method approximates an underlying function?
How is local linear model constructed?
How is local linear model constructed?
In the graphic of LIME explaining a prediction with flu data, what are the key components displayed?
In the graphic of LIME explaining a prediction with flu data, what are the key components displayed?
Which of the following is not a pro of LIME?
Which of the following is not a pro of LIME?
Which of the following is a con of LIME
Which of the following is a con of LIME
Why large number of samples matters in LIME?
Why large number of samples matters in LIME?
Why is LIME not stable?
Why is LIME not stable?
What is the correct order to describe SHAP?
What is the correct order to describe SHAP?
SHAP is related to which game theory?
SHAP is related to which game theory?
If someone never plays a game, how much should he receiv?
If someone never plays a game, how much should he receiv?
Which of the following algorithms supports SHAP value?
Which of the following algorithms supports SHAP value?
Which Kernel exploit LIME in its logic?
Which Kernel exploit LIME in its logic?
SHAP is considered
SHAP is considered
What are the differences in computing SHAP values for deep learning models versus tree-based models?
What are the differences in computing SHAP values for deep learning models versus tree-based models?
What is the purpose of 'force plots' in SHAP?
What is the purpose of 'force plots' in SHAP?
What aspect does an interaction value highlight in the context of TreeExplainer?
What aspect does an interaction value highlight in the context of TreeExplainer?
What is a key factor that makes explainability in machine learning difficult, particularly with complex models?
What is a key factor that makes explainability in machine learning difficult, particularly with complex models?
Why are interpretable models sometimes insufficient for providing complete explainability?
Why are interpretable models sometimes insufficient for providing complete explainability?
In the context of machine learning, what is the potential pitfall of solely relying on interpretable models that Cynthia Rudin warns about?
In the context of machine learning, what is the potential pitfall of solely relying on interpretable models that Cynthia Rudin warns about?
Why is it important to ensure fairness, accountability, and transparency (FAT) in machine learning algorithms?
Why is it important to ensure fairness, accountability, and transparency (FAT) in machine learning algorithms?
How might machine learning models inadvertently learn and perpetuate historical biases?
How might machine learning models inadvertently learn and perpetuate historical biases?
According to the General Data Protection Regulation (GDPR), what rights do affected customers or users have regarding automated decisions?
According to the General Data Protection Regulation (GDPR), what rights do affected customers or users have regarding automated decisions?
What is one reason why explainability in machine learning is crucial for defending against adversarial attacks?
What is one reason why explainability in machine learning is crucial for defending against adversarial attacks?
How could explaining why a model made a certain classification on an X-ray image improve health outcomes?
How could explaining why a model made a certain classification on an X-ray image improve health outcomes?
Why is explainability essential for machine learning models in contexts such as criminal justice?
Why is explainability essential for machine learning models in contexts such as criminal justice?
Why can complex models still be useful despite the difficulties in explaining them?
Why can complex models still be useful despite the difficulties in explaining them?
What is a potential risk when using machine learning models in high-stakes decisions, such as credit lending or criminal risk assessment?
What is a potential risk when using machine learning models in high-stakes decisions, such as credit lending or criminal risk assessment?
What is the purpose of model debugging in the context of machine learning explanations?
What is the purpose of model debugging in the context of machine learning explanations?
In regards to Explainable AI (XAI), what are the questions posed by DARPA (Defense Advanced Research Projects Agency)?
In regards to Explainable AI (XAI), what are the questions posed by DARPA (Defense Advanced Research Projects Agency)?
What benefit does Explainable AI (XAI) provide to managers and executive board members?
What benefit does Explainable AI (XAI) provide to managers and executive board members?
Why is ensuring explainability beneficial for regulatory compliance?
Why is ensuring explainability beneficial for regulatory compliance?
Which of the following does LIME utilize to produce an explanation?
Which of the following does LIME utilize to produce an explanation?
What is the purpose of perturbing samples around an instance in LIME?
What is the purpose of perturbing samples around an instance in LIME?
In the context of SHAP (SHapley Additive exPlanations), what does the Shapley value represent?
In the context of SHAP (SHapley Additive exPlanations), what does the Shapley value represent?
What does the 'Efficiency' axiom in Shapley Value theory state?
What does the 'Efficiency' axiom in Shapley Value theory state?
In the context of machine learning and feature attribution, what real-world concept is Shapley Values based on?
In the context of machine learning and feature attribution, what real-world concept is Shapley Values based on?
Which SHAP explainer is best suited for tree based methods?
Which SHAP explainer is best suited for tree based methods?
Which SHAP explainer uses LIME in its logic?
Which SHAP explainer uses LIME in its logic?
In the SHAP summary plot what do the colors represent?
In the SHAP summary plot what do the colors represent?
In the context of SHAP, what is the purpose of interaction values?
In the context of SHAP, what is the purpose of interaction values?
Flashcards
Explainable AI (XAI)
Explainable AI (XAI)
A field within AI that aims to make machine learning model decisions understandable to humans.
Right for the wrong reason
Right for the wrong reason
Models learn X-ray unit type (portable units, inpatient units, emergency department) instead of the patients health.
Fooling ML Algorithms
Fooling ML Algorithms
Machine learning algorithms can be easily deceived by unrecognizable images into outputting high confidence predictions.
ML Biases
ML Biases
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Equal Credit Opportunity Act 1974
Equal Credit Opportunity Act 1974
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General Data Protection Regulation (GDPR) 2018
General Data Protection Regulation (GDPR) 2018
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Explainability in FAT/ML
Explainability in FAT/ML
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Model Complexity
Model Complexity
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White Box Models
White Box Models
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Black Box Models
Black Box Models
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Local Explanation
Local Explanation
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Post-Hoc Explanations
Post-Hoc Explanations
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LIME (Local Interpretable Model-Agnostic Explanations)
LIME (Local Interpretable Model-Agnostic Explanations)
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Shapley Value
Shapley Value
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SHAP TreeExplainer
SHAP TreeExplainer
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SHAP GradientExplainer
SHAP GradientExplainer
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SHAP KernelExplainer
SHAP KernelExplainer
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Force Plots
Force Plots
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Study Notes
Explainable AI (XAI) Lecture 12
- Explainable AI seeks to provide reasons behind machine learning decisions.
The Need for ML Explanations
- It questions the use of machine learning and it's decision making.
- It questions if ML is actually classifying things correctly and for the "right" reasons.
- Some models can pick up X-ray machine types instead of actual health issues.
Vulnerability and Bias in ML
- Machine learning algorithms can be easily fooled and are vulnerable to adversarial attacks.
- ML algorithms can be biased, as seen in the Correctional Offender Management Profiling for Alternative Sanctions (COMPAS) tool.
- Amazon scrapped an AI recruiting tool due to bias against women.
Legal & Ethical Considerations
- US Equal Credit Opportunity Act of 1974 requires credit agencies to explain factors determining credit score.
- EU General Data Protection Regulation (GDPR) 2018 includes a "right to an explanation" for users affected by automated decisions.
Explainability Defined
- It ensures decisions as well as any data driving those decisions can be explained to stakeholders in non-technical terms.
- DARPA poses key questions for XAI: Why did you do that? Why not something else? When do you succeed/fail? When can I trust you? How can I correct an error?
XAI Target Audience
- It includes experts, users affected by model decisions, regulatory entities, developers, and executive boards.
Benefits of ML Explanations
-
Validating the logic of models
-
Defending against adversarial attacks
-
Detecting bias
-
Ensuring regulatory compliance
-
Debugging
-
Explainability leads to greater trust and adoption of AI systems.
Challenges in Achieving Explainability
- Model complexity makes it difficult to explain the output as a function of the input.
- Interpretable models don't scale well.
- There exists a multiplicity of good models for convex optimization problems.
- It is proposed to use interpretable models whenever you can
White Box vs. Black Box Models
- White Box Models have Self-explanatory structure but Black Box models map user features into a decision class without exposing the process.
Approaches to Explainability
- Local Explanation focuses on explaining individual predictions.
- Post-Hoc Explanations are applied after model training.
LIME (Local Interpretable Model-Agnostic Explanations)
- LIME approximates an underlying function to explain black box model predictions.
- LIME is model-agnostic, easy to understand and implement, and widely cited - however it assumes local linearity.
- LIME can be computationally expensive, requires a large number of samples, and may not be stable.
- Misleading explanations from LIME can lead users to trust biased classifiers, therefore please don't use it blindly
SHAP (SHapley Additive exPlanations)
- SHAP explains predictions based on Shapley Values from cooperative game theory.
- Shapley Values distribute gain in a coalition game fairly among players based on their contribution.
- SHAP has axioms of dummy, symmetry, efficiency and additivity
- SHAP can be used to determine credit decisions such as income, credit history, late payments, products etc
- Compute SHAP values for trees and ensembles of trees - models like XGBoost, LightGBM, CatBoost and Random Forest by using TreeExplainer
- Compute SHAP values for deep learning models by using DeepLIFT and Shapley values using DeepExplainer.
- KernelExplainer (Kernel SHAP) incorporates LIME into Logic but are model agnostics and support combination of LIME and Shapley values
- SHAP offers various visualization plots like force plots, dependence plots and summary plots
SHAP Pros and Cons
- It is widely cited, based on theory, easy to implement and has a lot of visualization Plots.
- It is a great and popular Tool but can be expensive to run.
Assigned Reading & Resources
- "Why should I trust you?" Explaining the predictions of any classifier.
- A Unified Approach to Interpreting Model Predictions.
- Data Camp Article - An Introduction to SHAP Values and Machine Learning Interpretability.
- Data Camp – Explainable Artificial Intelligence (XAI)
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