Unveiling Interpretability: Intrinsic vs. Post-hoc

#Intrinsic Interpretability

• Intrinsic interpretability is inherent in models due to their simple structures. Linear regression and short decision trees are prime examples. The transparency of these models arises from their straightforward architecture.

#Post-hoc Interpretability

• Post-hoc interpretability involves interpreting models after they have been trained. Techniques like permutation feature importance disrupt the relationship between features and output, providing insights into the importance of each feature post-training.

Model Specific vs. Model Agnostic: Tools for Interpretation

#Model Specific Interpretation

• Tools like interpreting weights in linear or logistic regression are specific to particular models and may not translate well across different model classes.

#Model Agnostic Interpretation

• Model agnostic tools, such as Partial Dependence Plots (PDP), LIME, and Shapley Values, operate on any machine learning model post-training. These tools, lacking access to internal model structures, offer insights into model behavior without model-specific dependencies.

Global vs. Local Interpretability: Navigating the Interpretation Landscape

#Global Interpretability

• Global interpretability provides a holistic understanding of how a model makes decisions across its entire structure. Achieving global interpretability is challenging, especially for large and complex models.

#Local Interpretability

• Local interpretability tools focus on explaining a single prediction or a subset of predictions. Understanding individual predictions or groups of outcomes may be more feasible than comprehending the entire model at a global level.

Evaluating Interpretability: Real Tasks, Simplified Tasks, and Proxy Tasks

#Testing on Real Tasks

• Integrating interpretability tools into real applications and evaluating their performance with end-users, particularly domain experts. Human performance serves as a baseline for comparison.

#Testing on Simplified Tasks

• Assessing interpretation on simplified tasks using laypeople instead of domain experts. This approach provides insights into the generalizability of explanations and is often quicker and more cost-effective.

#Testing using Proxy Tasks

• Employing formal definitions of interpretability as proxies for explanation quality. Proxy tasks are useful when human subject experiments are inaccessible or when a method is too new for validation through human evaluation.

The Essence of a Good Explanation

#Contrastive and Selective

• Good explanations are contrastive, allowing users to compare differences between instances. They are selective, offering a few key reasons from a variety of possibilities, crafting a compelling narrative amid the noise.

#Embedded in Social Context

• Context matters. Explanations should be tailored to the specific domain where they are applied, aligning with the social context of their application.

#Accounts for Abnormal Relationships

• Good explanations incorporate abnormal or unexpected relationships between input features and outcomes, ensuring a comprehensive understanding.

#Truthful and Consistent:

• Balancing selectivity and truthfulness, good explanations maintain high fidelity. They address confirmation bias, aligning with users' prior beliefs, and present a truthful narrative.

#General and Probable

• A good explanation can typically explain many events, unless influenced by abnormal causes. It should possess a level of generality and probability, ensuring applicability across various scenarios.