Distribution-Aware Counterfactual Explanation by Mixed-Integer Linear Optimization

Kanamori, Kentaro; Takagi, Takuya; Kobayashi, Kurima; Arimura, Hiroki

doi:10.1527/tjsai.36-6_c-l44

Cited by 3 publications

(2 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…the exogenous ones; or instance-based, which exploits a distance function to detect the decision boundary of the black-box. There are several desiderata in this context: efficiency, robustness, diversity, actionability, and plausibility, among others [122,71,69]. To better understand the complex context and the many available possibilities, we refer the interested reader to [15,120,25].…”

Section: Counterfactualsmentioning

confidence: 99%

Benchmarking and Survey of Explanation Methods for Black Box Models

Bodria¹,

Giannotti²,

Guidotti³

et al. 2021

Preprint

View full text Add to dashboard Cite

The widespread adoption of black-box models in Artificial Intelligence has enhanced the need for explanation methods to reveal how these obscure models reach specific decisions. Retrieving explanations is fundamental to unveil possible biases and to resolve practical or ethical issues. Nowadays, the literature is full of methods with different explanations. We provide a categorization of explanation methods based on the type of explanation returned. We present the most recent and widely used explainers, and we show a visual comparison among explanations and a quantitative benchmarking.

show abstract

Section: Counterfactualsmentioning

confidence: 99%

Benchmarking and Survey of Explanation Methods for Black Box Models

Bodria¹,

Giannotti²,

Guidotti³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…One of the key difficulties in reasoning about tree ensembles is reasoning about the aggregation over the many trees in the tree ensemble. Previous approaches to tackling the large linear constraints that arise in reasoning over the aggregation of the many trees in the ensemble either used SAT Modulo Theory (SMT) solvers (Ignatiev, Narodytska, and Marques-Silva 2019c) or Mixed Integer Programming (MIP) solvers (Chen et al 2019;Kanamori et al 2021;Parmentier and Vidal 2021) to directly handle large linear constraints, or encoded the linear constraints to SAT (Izza and Marques-Silva 2021), which can be costly.…”

Section: Introductionmentioning

confidence: 99%

Using MaxSAT for Efficient Explanations of Tree Ensembles

Ignatiev

Izza

Stuckey

et al. 2022

AAAI

View full text Add to dashboard Cite

Tree ensembles (TEs) denote a prevalent machine learning model that do not offer guarantees of interpretability, that represent a challenge from the perspective of explainable artificial intelligence. Besides model agnostic approaches, recent work proposed to explain TEs with formally-defined explanations, which are computed with oracles for propositional satisfiability (SAT) and satisfiability modulo theories. The computation of explanations for TEs involves linear constraints to express the prediction. In practice, this deteriorates scalability of the underlying reasoners. Motivated by the inherent propositional nature of TEs, this paper proposes to circumvent the need for linear constraints and instead employ an optimization engine for pure propositional logic to efficiently handle the prediction. Concretely, the paper proposes to use a MaxSAT solver and exploit the objective function to determine a winning class. This is achieved by devising a propositional encoding for computing explanations of TEs. Furthermore, the paper proposes additional heuristics to improve the underlying MaxSAT solving procedure. Experimental results obtained on a wide range of publicly available datasets demonstrate that the proposed MaxSAT-based approach is either on par or outperforms the existing reasoning-based explainers, thus representing a robust and efficient alternative for computing formal explanations for TEs.

show abstract

What-If XAI Framework (WiXAI): From Counterfactuals towards Causal Understanding

Kshetry,

Kantardzic

2024

JCC

View full text Add to dashboard Cite

People learn causal relations since childhood using counterfactual reasoning. Counterfactual reasoning uses counterfactual examples which take the form of "what if this has happened differently". Counterfactual examples are also the basis of counterfactual explanation in explainable artificial intelligence (XAI). However, a framework that relies solely on optimization algorithms to find and present counterfactual samples cannot help users gain a deeper understanding of the system. Without a way to verify their understanding, the users can even be misled by such explanations. Such limitations can be overcome through an interactive and iterative framework that allows the users to explore their desired "what-if" scenarios. The purpose of our research is to develop such a framework. In this paper, we present our "what-if" XAI framework (WiXAI), which visualizes the artificial intelligence (AI) classification model from the perspective of the user's sample and guides their "what-if" exploration. We also formulated how to use the WiXAI framework to generate counterfactuals and understand the feature-feature and feature-output relations in-depth for a local sample. These relations help move the users toward causal understanding.

show abstract

Distribution-Aware Counterfactual Explanation by Mixed-Integer Linear Optimization

Cited by 3 publications

References 28 publications

Benchmarking and Survey of Explanation Methods for Black Box Models

Benchmarking and Survey of Explanation Methods for Black Box Models

Using MaxSAT for Efficient Explanations of Tree Ensembles

What-If XAI Framework (WiXAI): From Counterfactuals towards Causal Understanding

Contact Info

Product

Resources

About