Year: 2025
Type: conference-paper
Abstract: Algorithmic recourse (AR) has made significant progress by identifying small perturbations in input features that can alter predictions, which provide a data-centric approach to understand decisions from diverse black-box models on the Web. Towards the feasibility issue, i.e., whether the recoursed examples provides actionable and reliable recommendations to end-users, causal algorithmic recourse have incorporated structural causal model (SCM) to preserve the realistic constraints among input features. For instance, preserving structural causal knowledge between "age" and "educational level" can avoid generating samples with decreasing age and increasing educational level. However, previous causal AR methods suffer from the requirement of prior structural causal knowledge, e.g., prior causal graph or the whole SCM, which restricts the realistic application of causal AR methods.
Authors: Haotian Wang, Hao Zou, Xueguang Zhou, Shangwen Wang, Wenjing Yang, Peng Cui
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Awards: G392688194, G4427191711, G5377421199
OpenAlex ID: W4410088877