Source-linked AI summary
Model-Agnostic Interpretability of Machine Learning
Marco Tulio Ribeiro, Sameer Singh, Carlos Guestrin
TL;DR
Machine learning needs explanations that support trust, design, and use without forcing practitioners to restrict models to interpretable forms. The paper argues for model-agnostic explanations, reviews LIME’s locally faithful interpretable modeling, and identifies important limitations and open challenges.
Problem
Restricting machine learning to interpretable models can limit model flexibility, while users and designers still need understandable rationales for predictions.
Method
The paper treats machine learning models as black boxes, separates explanations from models, and reviews LIME’s locally faithful interpretable explanation approach.
Results
Model-agnostic explanations provide flexibility in model, representation, explanation type, and user expertise, with LIME supporting locally faithful explanations for complex classifiers.
Takeaways & Limitations
Model-agnostic interpretability can make machine learning more trustworthy and useful while preserving freedom to use complex models and varied explanations.
Takeaways & Limitations
Model-agnostic explanations face challenges with global understanding, inconsistent local explanations, domain generalization, and settings requiring exact explanations.
Abstract
from arXiv · showhide
Understanding why machine learning models behave the way they do empowers both system designers and end-users in many ways: in model selection, feature engineering, in order to trust and act upon the predictions, and in more intuitive user interfaces. Thus, interpretability has become a vital concern in machine learning, and work in the area of interpretable models has found renewed interest. In some applications, such models are as accurate as non-interpretable ones, and thus are preferred for their transparency. Even when they are not accurate, they may still be preferred when interpretability is of paramount importance. However, restricting machine learning to interpretable models is often a severe limitation. In this paper we argue for explaining machine learning predictions using model-agnostic approaches. By treating the machine learning models as black-box functions, these approaches provide crucial flexibility in the choice of models, explanations, and representations, improving debugging, comparison, and interfaces for a variety of users and models. We also outline the main challenges for such methods, and review a recently-introduced model-agnostic explanation approach (LIME) that addresses these challenges.
1. Introduction
Machine learning explanations help users trust predictions and help designers improve, compare, and present models. The paper argues that separating explanations from models preserves flexibility while supporting post-hoc, model-agnostic interpretation.
- Interpretability matters because users need to understand and trust model predictions, while designers need explanations for improving and evaluating systems.Explanations can provide rationales using textual or visual data components and counterfactual information.
- Interpretable models expose inspectable components such as decision-tree paths, rules, or feature weights, provided the models remain accurate and sufficiently compact.
- Model-agnostic methods treat the original model as a black box and obtain explanations by learning interpretable models, perturbing inputs, or combining both strategies.
- The paper argues that separating explanations from models avoids restricting model choice and reviews LIME as a model-agnostic approach addressing key challenges.
2. A Case for Model Agnosticism
Model-agnostic explanations preserve access to complex, accurate models while allowing explanation types, representations, and granularity to be tailored to users and tasks. They also support switching and comparing models through shared explanation formats.
- 2.1. Model Flexibility: Restricting models to be interpretable can reduce flexibility and accuracy, especially for complex tasks involving large vocabularies, sensory data, or feature interactions.
- 2.1. Model Flexibility: Treating the model as a black box frees it to use arbitrary approaches, including deep neural networks, while permitting explanations to fail gracefully when interpretation is difficult.
- 2.2. Explanation Flexibility: Separating models from explanations allows the same model to support different explanation types and interpretability levels tailored to users’ information needs.
- 2.3. Representation Flexibility: Model-agnostic methods can explain models using interpretable features different from the underlying representation, such as explaining word-embedding models in terms of words.
- 2.4. Model Switching: Keeping explanations separate makes switching underlying models easier because the presentation of explanations can remain unchanged.
- 2.5. Comparing Two Models: Using common explanation techniques and representations makes models easier to compare even when their underlying models or native explanations differ.
3. Challenges for Model-agnostic Explanations
Model-agnostic explanations retain flexibility but face challenges in global understanding, consistency, domain generalization, actionability, and settings requiring exact explanations.
- Complex models can make global understanding difficult, and their local explanations may be inconsistent because feature use can vary with other features.
- The paper reports that extending representative text explanations to images or tabular data remains unclear when the data is not sparse.
- Exact explanations may be required for legal or ethical reasons, making black-box models unacceptable or even illegal in some domains.
- Interpretable models may be preferable when interpretability matters much more than accuracy or carefully engineered interpretable features achieve comparable accuracy.
- Making model-agnostic explanations actionable requires supporting user feedback such as feature labels, rationales, and stronger forms of feature engineering.
- Although removing bad features through explanations has been effective, incorporating more powerful user feedback while remaining model-agnostic is still challenging.
4. Local Interpretable Model-agnostic Explanations (LIME)
LIME explains individual predictions by fitting an interpretable model that is locally faithful to a black-box classifier, while allowing flexibility in explanation families and representations. It supports unified explanations across diverse models and illustrates how local explanations can capture complex behavior.
- LIME formulation: LIME fits an interpretable model over an interpretable representation that is locally faithful to the classifier being explained.The original and interpretable representations can differ, such as word embeddings versus bag-of-words or raw pixels versus super-pixels.
- LIME formulation: The explanation balances local fidelity against model complexity by minimizing unfaithfulness while keeping the explanation interpretable.Complexity may be controlled softly or through hard constraints, such as tree depth or the number of nonzero linear-model weights.
- LIME procedure: LIME estimates local fidelity from perturbed samples, black-box predictions, and proximity-based weighting around the instance being explained.Figure 1 depicts a globally complex decision function approximated by a locally faithful linear explanation.
- Flexibility: Practitioners can choose explanation families, complexity measures, and representations, and can compare multiple explanation families by their faithfulness.LIME supports sparse linear explanations, controls how many words are shown, and can select among explanation families.
- Applications: The same explanation type can describe random forests, SVMs, neural networks, linear models, and nearest neighbors, enabling model comparison by non-experts.The paper reports that Mechanical Turk users used explanations to select which of two competing models would generalize better.
- Applications: For sentiment prediction, LIME gives unified word-level explanations for classifiers with different complexity and representations, including a logistic regression and an LSTM.For “This is not bad.”, the LSTM explanation assigns positive weight to both “not” and “bad” because their conjunction drives the positive prediction.
5. Conclusion
Model-agnostic explanation systems offer a flexible interpretability framework, while LIME addresses some of their challenges and others remain future work. The paper concludes that this approach can make machine learning more trustworthy and useful.
- Model-agnostic explanation systems provide flexibility in choosing models, representations, and the expertise required of users.
- The paper outlines challenges for model-agnostic approaches, with some addressed by LIME and others left for future work.
- The paper concludes that model-agnostic interpretability is a key component in making machine learning more trustworthy and ultimately more useful.