We consider finding a counterfactual explanation for a classification or regression forest, such as a random forest. This requires solving an optimization problem to find the closest input instance to a given instance for which the forest outputs a desired value. Finding an exact solution has a cost that is exponential on the number of leaves in the forest. We propose a simple but very effective approach: we constrain the optimization to input space regions populated by actual data points. The problem reduces to a form of nearest-neighbor search using a certain distance on a certain dataset. This has two advantages: first, the solution can be found very quickly, scaling to large forests and high-dimensional data, and enabling interactive use. Second, the solution found is more likely to be realistic in that it is guided towards high-density areas of input space.

Very Fast, Approximate Counterfactual Explanations for Decision Forests

Classification problems having thousands or more classes naturally occur in NLP, for example language models or document classification. A softmax or one-vs-all classifier naturally handles many classes, but it is very slow at inference time, because every class score must be calculated to find the top class. We propose the ``softmax tree'', consisting of a binary tree having sparse hyperplanes at the decision nodes (which make hard, not soft, decisions) and small softmax classifiers at the leaves. This is much faster at inference because the input instance follows a single path to a leaf (whose length is logarithmic on the number of leaves) and the softmax classifier at each leaf operates on a small subset of the classes. Although learning accurate tree-based models has proven difficult in the past, we are able to overcome this by using a variation of a recent algorithm, tree alternating optimization (TAO). Compared to a softmax and other classifiers, the resulting softmax trees are both more accurate in prediction and faster in inference, as shown in NLP problems having from one thousand to one hundred thousand classes.

Softmax Tree: An Accurate, Fast Classifier When the Number of Classes Is Large

We consider counterfactual explanations, the problem of minimally adjusting features in a source input instance so that it is classified as a target class under a given classifier. This has become a topic of recent interest as a way to query a trained model and suggest possible actions to overturn its decision. Mathematically, the problem is formally equivalent to that of finding adversarial examples, which also has attracted significant attention recently. Most work on either counterfactual explanations or adversarial examples has focused on differentiable classifiers, such as neural nets. We focus on classification trees, both axis-aligned and oblique (having hyperplane splits). Although here the counterfactual optimization problem is nonconvex and nondifferentiable, we show that an exact solution can be computed very efficiently, even with high-dimensional feature vectors and with both continuous and categorical features, and demonstrate it in different datasets and settings. The results are particularly relevant for finance, medicine or legal applications, where interpretability and counterfactual explanations are particularly important.

Counterfactual Explanations for Oblique Decision Trees: Exact, Efficient Algorithms

ML: Bias and Fairness 1

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EMNLP 2021

EMNLP 2021 is planned to be a hybrid event in Punta Cana, Dominican Republic, with both on-site and fully virtual participation possible.

Main Track

technical paper

The purpose of the AAAI conference is to promote research in artificial intelligence (AI) and scientific exchange among AI researchers, practitioners, scientists, and engineers in affiliated disciplines. 

Miguel Ã. Carreira-PerpiÃ±Ã¡n

3

3

Presentations

Very Fast, Approximate Counterfactual Explanations for Decision Forests

Softmax Tree: An Accurate, Fast Classifier When the Number of Classes Is Large

Counterfactual Explanations for Oblique Decision Trees: Exact, Efficient Algorithms

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES

Miguel Ã. Carreira-PerpiÃ±Ã¡n

3

3

Presentations

Very Fast, Approximate Counterfactual Explanations for Decision Forests

Softmax Tree: An Accurate, Fast Classifier When the Number of Classes Is Large

Counterfactual Explanations for Oblique Decision Trees: Exact, Efficient Algorithms

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES

Miguel Ã. Carreira-PerpiÃ±Ã¡n