2,018 research outputs found
ILMART: Interpretable Ranking with Constrained LambdaMART
Interpretable Learning to Rank (LtR) is an emerging field within the research area of explainable AI, aiming at developing intelligible and accurate predictive models. While most of the previous research efforts focus on creating post-hoc explanations, in this paper we investigate how to train effective and intrinsically-interpretable ranking models. Developing these models is particularly challenging and it also requires finding a trade-off between ranking quality and model complexity. State-of-the-art rankers, made of either large ensembles of trees or several neural layers, exploit in fact an unlimited number of feature interactions making them black boxes. Previous approaches on intrinsically-interpretable ranking models address this issue by avoiding interactions between features thus paying a significant performance drop with respect to full-complexity models. Conversely, ILMART, our novel and interpretable LtR solution based on LambdaMART, is able to train effective and intelligible models by exploiting a limited and controlled number of pairwise feature interactions. Exhaustive and reproducible experiments conducted on three publicly-available LtR datasets show that ILMART outperforms the current state-of-the-art solution for interpretable ranking of a large margin with a gain of nDCG of up to 8%
Interpretable Ranking Using LambdaMART (Abstract)
In this talk we present the main results of a short paper appearing at SIGIR 2022 [1]. Interpretable Learning to Rank (LtR) is an emerging field within the research area of explainable AI, aiming at developing intelligible and accurate predictive models. While most of the previous research efforts focus on creating post-hoc explanations, in this talk we investigate how to train effective and intrinsically-interpretable ranking models. Developing these models is particularly challenging and it also requires finding a trade-off between ranking quality and model complexity. State-of-the-art rankers, made of either large ensembles of trees or several neural layers, exploit in fact an unlimited number of feature interactions making them black boxes. Previous approaches on intrinsically-interpretable ranking models, as Neural RankGAM [2], address this issue by avoiding interactions between features thus paying a significant performance drop with respect to full-complexity models. Conversely, we propose Interpretable LambdaMART, an interpretable LtR solution based on LambdaMART that is able to train effective and intelligible models by exploiting a limited and controlled number of pairwise feature interactions. Exhaustive and reproducible experiments conducted on three publicly-available LtR datasets show that our approach outperforms the current state-of-the-art solution for interpretable ranking of a large margin with a gain of nDCG of up to 8%
Axiomatic Interpretability for Multiclass Additive Models
Generalized additive models (GAMs) are favored in many regression and binary
classification problems because they are able to fit complex, nonlinear
functions while still remaining interpretable. In the first part of this paper,
we generalize a state-of-the-art GAM learning algorithm based on boosted trees
to the multiclass setting, and show that this multiclass algorithm outperforms
existing GAM learning algorithms and sometimes matches the performance of full
complexity models such as gradient boosted trees.
In the second part, we turn our attention to the interpretability of GAMs in
the multiclass setting. Surprisingly, the natural interpretability of GAMs
breaks down when there are more than two classes. Naive interpretation of
multiclass GAMs can lead to false conclusions. Inspired by binary GAMs, we
identify two axioms that any additive model must satisfy in order to not be
visually misleading. We then develop a technique called Additive
Post-Processing for Interpretability (API), that provably transforms a
pre-trained additive model to satisfy the interpretability axioms without
sacrificing accuracy. The technique works not just on models trained with our
learning algorithm, but on any multiclass additive model, including multiclass
linear and logistic regression. We demonstrate the effectiveness of API on a
12-class infant mortality dataset.Comment: KDD 201
Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations
Post-hoc explanations of machine learning models are crucial for people to
understand and act on algorithmic predictions. An intriguing class of
explanations is through counterfactuals, hypothetical examples that show people
how to obtain a different prediction. We posit that effective counterfactual
explanations should satisfy two properties: feasibility of the counterfactual
actions given user context and constraints, and diversity among the
counterfactuals presented. To this end, we propose a framework for generating
and evaluating a diverse set of counterfactual explanations based on
determinantal point processes. To evaluate the actionability of
counterfactuals, we provide metrics that enable comparison of
counterfactual-based methods to other local explanation methods. We further
address necessary tradeoffs and point to causal implications in optimizing for
counterfactuals. Our experiments on four real-world datasets show that our
framework can generate a set of counterfactuals that are diverse and well
approximate local decision boundaries, outperforming prior approaches to
generating diverse counterfactuals. We provide an implementation of the
framework at https://github.com/microsoft/DiCE.Comment: 13 page
Distill-and-Compare: Auditing Black-Box Models Using Transparent Model Distillation
Black-box risk scoring models permeate our lives, yet are typically
proprietary or opaque. We propose Distill-and-Compare, a model distillation and
comparison approach to audit such models. To gain insight into black-box
models, we treat them as teachers, training transparent student models to mimic
the risk scores assigned by black-box models. We compare the student model
trained with distillation to a second un-distilled transparent model trained on
ground-truth outcomes, and use differences between the two models to gain
insight into the black-box model. Our approach can be applied in a realistic
setting, without probing the black-box model API. We demonstrate the approach
on four public data sets: COMPAS, Stop-and-Frisk, Chicago Police, and Lending
Club. We also propose a statistical test to determine if a data set is missing
key features used to train the black-box model. Our test finds that the
ProPublica data is likely missing key feature(s) used in COMPAS.Comment: Camera-ready version for AAAI/ACM AIES 2018. Data and pseudocode at
https://github.com/shftan/auditblackbox. Previously titled "Detecting Bias in
Black-Box Models Using Transparent Model Distillation". A short version was
presented at NIPS 2017 Symposium on Interpretable Machine Learnin
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