Podcast
Questions and Answers
Why is explainability in machine learning crucial for high-stakes decisions?
Why is explainability in machine learning crucial for high-stakes decisions?
- It allows models to generalize to unseen data more effectively.
- It reduces the computational resources required for model deployment.
- It speeds up the model training process.
- It helps in understanding the logic behind decisions, ensuring fairness and accountability. (correct)
According to the principles of Fairness, Accountability, and Transparency in Machine Learning (FAT/ML), what does explainability ensure?
According to the principles of Fairness, Accountability, and Transparency in Machine Learning (FAT/ML), what does explainability ensure?
- That algorithms are free from errors.
- That algorithmic decisions and the data driving them can be understood by end-users and stakeholders in non-technical terms. (correct)
- That all machine learning models are deployed in a transparent environment.
- That algorithmic decisions are made solely by technical experts.
How does explainability contribute to building trust in machine learning models?
How does explainability contribute to building trust in machine learning models?
- By increasing the complexity of the models.
- By reducing the need for regulatory compliance.
- By providing insights into how the model works, enabling users to validate the logic and identify potential biases. (correct)
- By ensuring the models are always correct.
What is one reason explainability in machine learning can be difficult to achieve?
What is one reason explainability in machine learning can be difficult to achieve?
What is a key difference between white box and black box models in the context of explainability?
What is a key difference between white box and black box models in the context of explainability?
What is the primary goal of Local Interpretable Model-agnostic Explanations (LIME)?
What is the primary goal of Local Interpretable Model-agnostic Explanations (LIME)?
What is a limitation of LIME?
What is a limitation of LIME?
What is the main concept behind SHAP (SHapley Additive exPlanations) values?
What is the main concept behind SHAP (SHapley Additive exPlanations) values?
Which of the following is a SHAP explainer designed for tree-based models?
Which of the following is a SHAP explainer designed for tree-based models?
How do SHAP force plots help in understanding model predictions?
How do SHAP force plots help in understanding model predictions?
What is the legal importance of explainability in machine learning, as exemplified by the EU's General Data Protection Regulation (GDPR)?
What is the legal importance of explainability in machine learning, as exemplified by the EU's General Data Protection Regulation (GDPR)?
In the context of explainable AI (XAI), what does it mean for a model to be 'interpretable by design'?
In the context of explainable AI (XAI), what does it mean for a model to be 'interpretable by design'?
Why is it important for machine learning algorithms used in criminal justice to be explainable?
Why is it important for machine learning algorithms used in criminal justice to be explainable?
What does the term 'model-agnostic' mean in the context of explainable AI techniques like LIME?
What does the term 'model-agnostic' mean in the context of explainable AI techniques like LIME?
What is a potential risk of relying solely on black box machine learning models for high-stakes decisions?
What is a potential risk of relying solely on black box machine learning models for high-stakes decisions?
What does the Shapley Value Axiom of 'Dummy' imply in the context of feature importance in machine learning?
What does the Shapley Value Axiom of 'Dummy' imply in the context of feature importance in machine learning?
How does explainability contribute to model debugging?
How does explainability contribute to model debugging?
Considering the trade-offs between model complexity and explainability, what is a strategy to improve explainability without sacrificing too much accuracy?
Considering the trade-offs between model complexity and explainability, what is a strategy to improve explainability without sacrificing too much accuracy?
Which of the following techniques can help defend against adversarial attacks on machine learning models?
Which of the following techniques can help defend against adversarial attacks on machine learning models?
Why is it important to assess regulatory compliance using Explainable AI (XAI)?
Why is it important to assess regulatory compliance using Explainable AI (XAI)?
What is the key idea behind using surrogate models in post-hoc explanations?
What is the key idea behind using surrogate models in post-hoc explanations?
In the context of Shapley values, what does the axiom of 'efficiency' state?
In the context of Shapley values, what does the axiom of 'efficiency' state?
How do dependence plots enhance the interpretability of machine learning models when using SHAP values?
How do dependence plots enhance the interpretability of machine learning models when using SHAP values?
What kind of information can you directly obtain from a SHAP summary plot?
What kind of information can you directly obtain from a SHAP summary plot?
Why might a credit agency be required to provide the main factors determining a credit score, according to the Equal Credit Opportunity Act?
Why might a credit agency be required to provide the main factors determining a credit score, according to the Equal Credit Opportunity Act?
How does the use of explainable AI (XAI) affect the software development lifecycle for machine learning projects?
How does the use of explainable AI (XAI) affect the software development lifecycle for machine learning projects?
Why should the use of explainable AI consider the target audience?
Why should the use of explainable AI consider the target audience?
Which question, posed by DARPA's Explainable Artificial Intelligence (XAI) program, directly addresses the need to correct errors in AI decision-making?
Which question, posed by DARPA's Explainable Artificial Intelligence (XAI) program, directly addresses the need to correct errors in AI decision-making?
Explainability in ML helps with
Explainability in ML helps with
What kind of bias was found in Amazon's AI recruiting tool?
What kind of bias was found in Amazon's AI recruiting tool?
For Shapley values, what type of feature would have a Shapley value of zero?
For Shapley values, what type of feature would have a Shapley value of zero?
Which of the following is NOT a benefit of explainable AI?
Which of the following is NOT a benefit of explainable AI?
Which of the following is considered to be a 'black box' model?
Which of the following is considered to be a 'black box' model?
Which of the following is true of 'Explainable AI'?
Which of the following is true of 'Explainable AI'?
Which is NOT considered a 'Pro' of explainable AI?
Which is NOT considered a 'Pro' of explainable AI?
What does LIME stand for?
What does LIME stand for?
SHAP values are derived from what concept?
SHAP values are derived from what concept?
Why can high model complexity make explainability difficult?
Why can high model complexity make explainability difficult?
In the context of machine learning, what is a major limitation of relying solely on intrinsically interpretable models like decision trees?
In the context of machine learning, what is a major limitation of relying solely on intrinsically interpretable models like decision trees?
What does it mean for a machine learning model to be 'interpretable by design'?
What does it mean for a machine learning model to be 'interpretable by design'?
What's a potential consequence of using machine learning algorithms without proper explainability in criminal justice?
What's a potential consequence of using machine learning algorithms without proper explainability in criminal justice?
According to the Equal Credit Opportunity Act in the US, why might a bank be required to explain to a customer why their loan application was denied?
According to the Equal Credit Opportunity Act in the US, why might a bank be required to explain to a customer why their loan application was denied?
Which of the following is a key benefit of machine learning explainability related to identifying issues in training data?
Which of the following is a key benefit of machine learning explainability related to identifying issues in training data?
In the context of explainable AI (XAI), what does it mean to 'validate the logic' of a machine learning model?
In the context of explainable AI (XAI), what does it mean to 'validate the logic' of a machine learning model?
How does incorporating explainability techniques affect the software development lifecycle of a machine learning project?
How does incorporating explainability techniques affect the software development lifecycle of a machine learning project?
Which question addresses the need to verify whether a machine learning algorithm is making correct decisions?
Which question addresses the need to verify whether a machine learning algorithm is making correct decisions?
What distinguishes 'White Box' models from 'Black Box' models concerning explainability?
What distinguishes 'White Box' models from 'Black Box' models concerning explainability?
According to the lecture, what is a significant limitation of LIME (Local Interpretable Model-agnostic Explanations)?
According to the lecture, what is a significant limitation of LIME (Local Interpretable Model-agnostic Explanations)?
What is the primary purpose of 'perturbing' the data around an instance when using LIME (Local Interpretable Model-agnostic Explanations)?
What is the primary purpose of 'perturbing' the data around an instance when using LIME (Local Interpretable Model-agnostic Explanations)?
What does it mean for LIME (Local Interpretable Model-agnostic Explanations) to be 'model-agnostic'?
What does it mean for LIME (Local Interpretable Model-agnostic Explanations) to be 'model-agnostic'?
Why is LIME considered 'not stable'?
Why is LIME considered 'not stable'?
Why should a user avoid blindly using LIME?
Why should a user avoid blindly using LIME?
What concept from game theory are SHAP (SHapley Additive exPlanations) values based on?
What concept from game theory are SHAP (SHapley Additive exPlanations) values based on?
Per Shapley Value Axioms, what will a feature that never contributes to the game receive?
Per Shapley Value Axioms, what will a feature that never contributes to the game receive?
In SHAP (SHapley Additive exPlanations), what do the 'players' represent in the context of explaining a model output?
In SHAP (SHapley Additive exPlanations), what do the 'players' represent in the context of explaining a model output?
What is the purpose of SHAP dependence plots?
What is the purpose of SHAP dependence plots?
What kind of overall insights can be derived from a SHAP summary plot?
What kind of overall insights can be derived from a SHAP summary plot?
Which of the following statements best describes the 'efficiency' axiom in the context of Shapley values?
Which of the following statements best describes the 'efficiency' axiom in the context of Shapley values?
For what type of machine learning models is shap.TreeExplainer designed?
For what type of machine learning models is shap.TreeExplainer designed?
Which SHAP explainer is best used for deep learning models?
Which SHAP explainer is best used for deep learning models?
In brief, how does Kernel SHAP estimate feature contributions?
In brief, how does Kernel SHAP estimate feature contributions?
Flashcards
Wrong Reasoning in ML
Wrong Reasoning in ML
Machine learning algorithms can sometimes make decisions for the wrong reasons based on unintended data correlations.
Fooling ML Algorithms
Fooling ML Algorithms
Machine learning algorithms can be easily fooled with imperceptible changes to their inputs.
Adversarial ML Attacks
Adversarial ML Attacks
Machine learning algorithms are vulnerable to adversarial attacks where carefully crafted inputs can cause incorrect classifications.
Algorithmic Bias
Algorithmic Bias
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XAI Explainability
XAI Explainability
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XAI Target Audience
XAI Target Audience
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ML Explanation Benefits
ML Explanation Benefits
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Explainability Adoption
Explainability Adoption
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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
LIME
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Local Interpretable Model-Agnostic Explanation (LIME)
Local Interpretable Model-Agnostic Explanation (LIME)
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LIME Drawbacks
LIME Drawbacks
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SHAP (SHapley Additive exPlanations)
SHAP (SHapley Additive exPlanations)
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Shapley Value - Lloyd Shapley
Shapley Value - Lloyd Shapley
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SHAP TreeExplainer
SHAP TreeExplainer
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SHAP DeepExplainer
SHAP DeepExplainer
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SHAP KernelExplainer (Kernel SHAP)
SHAP KernelExplainer (Kernel SHAP)
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SHAP Force Plots
SHAP Force Plots
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Study Notes
Explainable AI (XAI)
- Explainable AI enhances trust and understanding in machine-learning models
- XAI makes the rationales behind AI decision-making more transparent
Reasons to Use Machine Learning
- Machine learning algorithms can be vulnerable to adversarial attacks.
- Machine learning algorithms can be biased
- Historical biases can be replicated by models, such as penalizing women applicants.
Explainability as a Legal Requirement
- The United States Equal Credit Opportunity Act of 1974 mandates that credit agencies provide the main determinants of credit scores.
- The European Union General Data Protection Regulation (GDPR) 2018 includes a "Right to an Explanation," offering affected customers meaningful information about the logic behind automated decisions.
Fairness, Accountability, Transparency in Machine Learning
- Explainability ensures algorithmic decisions can be understood by end-users and stakeholders in non-technical terms.
DARPA's Goals for Explainable AI
- Why did you do that?
- Why not something else?
- When do you succeed?
- When do you fail?
- When can I trust you?
- How do I correct an error?
Target Audience for XAI
- Domain experts need to trust the model and gain scientific knowledge.
- People affected by model decisions need to verify fairness
- Regulatory entities need to certify model compliance
- Data scientists need to improve product efficiency.
- Managers and executive boards need to assess regulatory compliance.
Benefits of Machine Learning Explanation
- Increased adoption derives from explainability, leading to trust.
- Logic validation
- Defense against adversarial attacks
- Bias detection
- Regulatory compliance
- Model debugging
The Challenge of Explainability
- Greater model complexity makes it difficult to explain output in terms of input because Machine Learning establishes many interactions between input variables.
- Models learn complex functions to achieve better accuracy
Decision Trees and Explainability
- Decision trees are intrinsically explainable by design.
- The multiplicity of good models makes interpretability difficult for convex optimization problems.
Interpretable Models Limitations
- Interpretable model explanations do not scale well.
- Black box machine learning models are currently being used for high stakes decision-making problems.
GPT-3 Parameters
- OpenAI’s GPT-3 has 175 billion parameters.
Options for Explainability
- White Box Models are self-explanatory and have interoperable output.
- Black-Box Models map user features to decisions without explaining the "how" and "why."
- Focus can be shifted to local, model-specific or model-agnostic explanations
LIME
- LIME (Local Interpretable Model-Agnostic Explanations) approximates the underlying function of a model, for explanations
- LIME is a local interpretable model-agnostic explanation.
- The ML model approximates an underlying function.
- LIME then provides the explanation.
LIME Image Classifier
- LIME can be used to classify images
LIME Text Classifier
- LIME can classify text.
LIME Pros
- Widely cited and easy to understand
- The method is easy to implement.
LIME Cons
- Approximation means not an exact replica
- Fidelity is an open research question.
- LIME assumes local linearity.
- It is computationally expensive.
- LIME requires a large number of samples.
- It is not always stable
LIME Final Notes
- LIME can mislead and be used to fool people into trusting biased classifiers.
- LIME is a great and popular tool if used correctly
SHAP
- SHAP (SHapley Additive exPlanations) provides insights into model predictions indicating the impact of each feature.
Origin of SHAP
- The Shapley Value is from Lloyd Shapley concept in cooperative game theory, which ensures members receive payments/shares proportional to marginal contributions.
- Shapley introduced his theory in 1951, and won the Nobel Prize in Economics in 2012.
SHAP and Cooperative Game Theory
- Cooperative game theory distributes gain in a coalition game
- Shapley Values attribute the total gain to the players based on their contribution.
Shapley Value Axioms
- Dummy: Zero attribution if a player never contributes.
- Symmetry: Equal attribution to symmetric players.
- Efficiency: Attributions must add to the total gain.
- Additivity: Sum model Shapley value can be calculated
SHAP Use Case
- SHAP is suitable for additive feature attribution, as in determining critical factors for credit decisions.
- SHAP assigns each feature a value for a particular prediction.
TreeExplainer
- SHAP can compute, using SHAP values, trees and ensembles of trees
- XGBoost, LightGBM, and CatBoost are supported
DeepExplainer
- SHAP is able to compute, using DeepLIFT and Shapely values, deep learning models.
- TensorFlow and Keras models are supported.
GradientExplainer
- SHAP is able to use TensorFlow and Keras models to approximate SHAP values.
- SHAP is able to implement expected gradients to approximate values for deep learning models.
KernelExplainer
- Kernel SHAP is model agnostic and uses a combination of LIME and Shapley values.
Force Plots
- Force plots are used for model output explanation.
Dependence Plots
- Dependence plots display the relationship between a feature and the SHAP value for that feature.
Summary Plots
- Summary plots are able to highlight key features.
Interaction Values
- Interaction values include pairwise interaction.
SHAP Pros
- Has wide usage and implementations
- SHAP is "Based on Theory"
- SHAP produces visualization plots
SHAP Conclusion
- SHAP should not be considered as an alternative to LIME, as KernelSHAP integrates LIME into its logic
- SHAP supports many useful visualization charts.
- SHAP implementation is an expensive run.
- It is a tool and has a basis in Game Theory
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