Work

Producing Diverse Rashomon Sets of Counterfactual Explanations with Niching Particle Swarm Optimization Algorithms

Year: 2023

Type: conference-paper

Abstract: Counterfactual explanation is a popular eXplainable AI technique, that gives contrastive explanations to answer potential "what-if" questions about the workings of machine learning models. However, research into how explanations are understood by human beings has shown that an optimal explanation should be both selected and social, providing multiple varying explanations for the same event that allow a user to select specific explanations based on prior beliefs and cognitive biases. In order to provide such explanations, a Rashomon set of explanations can be created: a set of explanations utilising different features in the data. Current work to generate counterfactual explanations does not take this need into account, only focusing on producing a single optimal counterfactual.

Source: Genetic and Evolutionary Computation Conference (GECCO)

DOI: 10.1145/3583131.3590444

Authors: Hayden Andersen, Andrew Lensen, Will Browne, Yi Mei

Institutions: Victoria University of Wellington, Queensland University of Technology

Language: English

Topic: Explainable Artificial Intelligence (XAI)

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: bronze

OpenAlex ID: W4391908970

FWCI: 0.896

Cites: 36

Cited by: 5

Related to: 10