Interpretability

Links

1. Interpretability Importance

Interpretability

We want to know the WHAT and more importantly the WHY of a prediction.

Definition of Interpretability: Interpretability is the extent to which a human can comprehend the reasoning behind a model’s decision.

Interpretability in machine learning encompasses various dimensions, from understanding the algorithm’s workings to comprehending complex model behaviours.

Importance:

• Trust: Stakeholders trust models they understand. Do we know why it made a decision?
• Debugging: Easier issue identification in interpretable models.

Interpretability:

• makes it possible to extract this additional knowledge captured by the model.
• can detect bias within a dataset.
• allows for social acceptance of models.
• allows to check the model for fairness, privacy, reliability, causality and trust.
• is not needed if the model has no significant impact or problem is well studied.

Levels of Interpretability

• Global, Holistic Model Interpretability: Involves comprehending the entire model at once, including feature importance and interactions. This level of interpretability is challenging, especially for models with many parameters.

• Global Model Interpretability on a Modular Level: Focuses on understanding parts of the model (e.g., weights in linear models or splits in decision trees). While individual parameters may be interpretable, their interdependence complicates interpretation.

• Local Interpretability for a Single Prediction: Allows for detailed examination of why a model made a specific prediction for an individual instance. This can provide clearer insights as local predictions may exhibit simpler relationships than the global model.

• Local Interpretability for a Group of Predictions: Involves explaining model predictions for multiple instances, using either global methods applied to the group or aggregating individual explanations.

Challenges in Achieving Interpretability

Models with high dimensionality or complex structures (e.g., deep learning) are often difficult for humans to interpret comprehensively.

• Effective interpretation requires context; for instance, understanding the significance of weights in linear models is often conditional on other feature values.
• Trade-offs: Users must weigh the need for predictions against the need for understanding the rationale, particularly in contexts where decisions have significant consequences.
• Human Learning: Interpretability supports human curiosity, facilitating updates to mental models based on new information.
• Safety and Bias Detection: Essential for high-risk applications (e.g., self-driving cars) and for identifying biases in decision-making.
• Social Acceptance: Machines that explain their decisions tend to be more accepted.
• When Not Needed: Less critical in low-risk environments or well-studied problems, though essential in high-impact contexts.

Properties of Explanations

These properties provide a framework for evaluating and comparing explanation methods in interpretable machine learning, ensuring they are effective and useful for understanding model predictions.

Properties of Explanation Methods

1. Expressive Power: Refers to the types of explanations generated (e.g., rules, decision trees, natural language).

2. Translucency: Measures the extent to which an explanation method examines the model’s internal parameters. High translucency allows for more informative explanations, while low translucency enhances portability.

3. Portability: Indicates the range of models compatible with the explanation method. Methods that treat models as black boxes (e.g., surrogate models) are more portable.

4. Algorithmic Complexity: Reflects the computational demands of generating explanations, which is crucial when processing time is a concern.

Properties of Explanations

1. Accuracy: Assesses how well the explanation predicts unseen data. High accuracy is vital if the explanation is used in place of the model.

2. Fidelity: Evaluates how closely the explanation matches the black box model’s predictions. High fidelity is essential; otherwise, the explanation is ineffective.

3. Consistency: Measures how similar explanations are across models trained on the same task. High consistency is desirable when models rely on similar relationships.

4. Stability: Examines how consistent explanations are for similar instances. High stability is preferred to avoid erratic changes due to minor variations in input features.

5. Comprehensibility: Assesses how easily humans understand the explanations. This property is challenging to define but is critical for effective communication of model behavior.

6. Certainty: Indicates whether the explanation reflects the model’s confidence in its predictions, adding value by clarifying prediction reliability.

7. Degree of Importance: Evaluates how well the explanation identifies the importance of features involved in a decision.

8. Novelty: Addresses whether the instance to be explained lies outside the training data distribution, affecting prediction accuracy.

9. Representativeness: Measures how many instances an explanation covers, ranging from individual predictions to broader model interpretations.

Understanding an Explanation

Here are the key takeaways on human-friendly explanations in interpretable machine learning:

Need comprehensibility and accuracy in explanations to enhance user understanding and trust in machine learning models.

Importance of Human-Friendly Explanations

1. Preference for Short Explanations: Humans favor concise explanations (1-2 causes) that contrast current situations with hypothetical scenarios where the event did not occur.

2. Nature of Explanations: An explanation answers “why” questions, focusing primarily on everyday situations rather than general scientific queries.

Characteristics of Good Explanations

1. Contrastive Nature: Good explanations highlight differences between predicted outcomes, aiding comprehension. For instance, explaining why a loan was rejected by comparing it to a hypothetical accepted application is more effective.

2. Selective Focus: People tend to prefer explanations that identify one or two key causes rather than exhaustive lists. This selective approach aligns with the “Rashomon Effect,” where multiple valid explanations can exist for the same event.

3. Social Context: Explanations are influenced by the social context and audience. Tailoring explanations to the audience’s knowledge level enhances understanding.

4. Emphasis on Abnormal Causes: Humans focus on rare or abnormal causes to explain events. Including these in explanations can significantly enhance clarity.

5. Truthfulness vs. Selectivity: While truthfulness is important, selectivity often takes precedence. A concise, selective explanation is more impactful than a comprehensive but complex one.

6. Consistency with Prior Beliefs: Explanations that align with the explainee’s existing beliefs are more readily accepted, highlighting the challenge of integrating complex model behaviors that contradict common intuitions.

7. Generality: Good explanations should be generalizable, but abnormal causes can sometimes provide more compelling insights.

Implications for Interpretable Machine Learning

• Design Considerations: Create explanations that are short, contrastive, and tailored to the audience’s background.

• Methodology: Incorporate techniques that can produce contrastive explanations while maintaining accuracy and fidelity to the model’s predictions.

• Audience Awareness: Understanding the audience’s social context and prior beliefs is crucial for effective communication of model outcomes.

• Understand how the model makes predictions.

• Use techniques like feature importance scores or LIME to explain individual predictions.

• How can we design machine learning models that are both accurate and interpretable? While deep learning models often achieve high accuracy, their complexity can make them difficult to interpret. This raises questions about how to balance accuracy and interpretability. Exploring techniques for visualizing and understanding the internal representations learned by deep networks, or developing inherently interpretable models that still achieve high performance, could lead to greater trust and adoption of machine learning in critical applications like healthcare and finance.