Improvement-focused causal recourse (ICR)

Improvement-focused causal recourse (ICR)

Abstract

Algorithmic recourse recommendations, such as Karimi et al.'s (2021) causal recourse (CR), inform stakeholders of how to act to revert unfavourable decisions. However, some actions lead to acceptance (i.e., revert the model's decision) but do not lead to improvement (i.e., may not revert the underlying real-world state). To recommend such actions is to recommend fooling the predictor. We introduce a novel method, Improvement-Focused Causal Recourse (ICR), which involves a conceptual shift: Firstly, we require ICR recommendations to guide towards improvement. Secondly, we do not tailor the recommendations to be accepted by a specific predictor. Instead, we leverage causal knowledge to design decision systems that predict accurately pre- and post-recourse. As a result, improvement guarantees translate into acceptance guarantees. We demonstrate that given correct causal knowledge, ICR, in contrast to existing approaches, guides towards both acceptance and improvement.

Grafik Top
Authors
  • König, Gunnar
  • Freiesleben, Timo
  • Grosse-Wentrup, Moritz
Grafik Top
Shortfacts
Category
Paper in Conference Proceedings or in Workshop Proceedings (Paper)
Event Title
The 37th AAAI Conference on Artificial Intelligence
Divisions
Neuroinformatics
Subjects
Kuenstliche Intelligenz
Event Location
Washington, DC, USA
Event Type
Conference
Event Dates
07-14 Feb 2023
Date
26 June 2023
Export
Grafik Top