Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges (2103.11251v2)

Abstract: Interpretability in ML is crucial for high stakes decisions and troubleshooting. In this work, we provide fundamental principles for interpretable ML, and dispel common misunderstandings that dilute the importance of this crucial topic. We also identify 10 technical challenge areas in interpretable machine learning and provide history and background on each problem. Some of these problems are classically important, and some are recent problems that have arisen in the last few years. These problems are: (1) Optimizing sparse logical models such as decision trees; (2) Optimization of scoring systems; (3) Placing constraints into generalized additive models to encourage sparsity and better interpretability; (4) Modern case-based reasoning, including neural networks and matching for causal inference; (5) Complete supervised disentanglement of neural networks; (6) Complete or even partial unsupervised disentanglement of neural networks; (7) Dimensionality reduction for data visualization; (8) Machine learning models that can incorporate physics and other generative or causal constraints; (9) Characterization of the "Rashomon set" of good models; and (10) Interpretable reinforcement learning. This survey is suitable as a starting point for statisticians and computer scientists interested in working in interpretable machine learning.

Interpretability in Machine Learning: Principles and Challenges

The paper discusses the significance of interpretability in ML and provides an elaborate exploration of ten key challenge areas in interpretable ML. These challenges offer a roadmap for researchers pursuing this crucial aspect of model design, particularly for high-stakes decision-making domains such as healthcare and criminal justice.

Core Principles

Interpretable ML models adhere to domain-specific constraints that enhance human understanding of their reasoning processes. This paper emphasizes that interpretability should not be mistaken for a direct trade-off with accuracy; rather, it can facilitate improved model performance by allowing for more effective troubleshooting and refinement. Another crucial principle is that interpretable models enable users to decide on trustworthiness by presenting clarity in decision-making processes.

Ten Challenges in Interpretable ML

1. Sparse Logical Models: Optimizing sparse models, such as decision trees and lists, presents computational challenges due to scalability and handling continuous variables. Recent advancements have sought exact optimization methods, yet questions around efficient scaling and constraint management persist.
2. Scoring Systems: Interpretable scoring systems with integer coefficients leverage linear models for quick decision-making in fields like healthcare. Computational difficulties arise from integrating user constraints into optimization processes. Recent developments have focused on mixed-integer programming and sophisticated rounding techniques.
3. Generalized Additive Models (GAMs): GAMs provide insights into feature contributions in high-dimensional datasets. Challenges remain in balancing model simplicity (sparsity and smoothness) against performance, and leveraging these models for dataset troubleshooting.
4. Modern Case-Based Reasoning: Techniques for structured and raw data—like prototypes in neural networks—emulate human reasoning. Extending to complex data such as video, incorporating human supervision, and troubleshooting prototypes are ongoing research areas.
5. Supervised Disentanglement: Aligning neural network layers with human-understandable concepts can enhance interpretability. The challenge lies in fully disentangling networks to make all neurons interpretable while managing complexity.
6. Unsupervised Disentanglement: Here, the goal is concept discovery without predefined labels. This presents difficulties in quantitatively evaluating disentanglement and extending compositional structures like Capsule Networks to complex tasks.
7. Dimensionality Reduction (DR): DR aids in understanding high-dimensional data by visualizing relationships. Challenges include preserving global and local structures, selecting hyperparameters, and explicating the DR process itself.
8. Physics-Incorporating Models: Fusing ML with physics constraints focuses on PINNs to solve differential equations. Challenges continue in training dynamics and integrating experimental design to refine uncertainty margins.
9. Characterization of the Rashomon Set: This concept involves identifying multiple high-performing models within a given accuracy threshold. Key challenges include measuring the size of this set, visualizing it meaningfully, and identifying suitable models within it.
10. Interpretable Reinforcement Learning (RL): Ensuring RL models' decisions are understandable pertains to policy transparency. Challenges include developing constraints that maintain performance while delivering interpretability, and simplifying complex state spaces.

Implications and Future Directions

The challenges outlined reveal critical avenues for advancing interpretable ML across diverse application contexts. As the field evolves, integrating user preference and domain expertise to guide model design will be crucial. Research will likely continue to blur the lines between optimization and interpretability, driving innovation in model constraints and validation strategies.

Interpretability in ML is integral to gaining trust and ensuring ethical AI deployment. This paper sets a comprehensive agenda for tackling pressing issues, promising substantial contributions to both the theoretical growth and practical application of interpretable models.