Abstract
We consider and discuss the ways in which search landscapes might contribute to the future of explainable artificial intelligence (XAI), and vice versa. Landscapes are typically used to gain insight into algorithm search dynamics on optimisation problems; as such, it could be said that they explain algorithms and that they are a natural bridge between XAI and evolutionary computation. Despite this, there is very little existing literature which utilises landscapes for XAI, or which applies XAI techniques to landscape analysis. This position paper reviews the existing works, discusses possible future avenues, and advocates for increased research effort in this area.
| Original language | English |
|---|---|
| Title of host publication | GECCO 2023 Companion |
| Subtitle of host publication | Proceedings of the 2023 Genetic and Evolutionary Computation Conference Companion |
| Publisher | Association for Computing Machinery, Inc |
| Pages | 1663-1667 |
| Number of pages | 5 |
| ISBN (Print) | 9798400701207 |
| DOIs | |
| Publication status | Published - Jul 2023 |
| Event | 2023 Genetic and Evolutionary Computation Conference Companion, GECCO 2023 Companion - Lisbon, Portugal Duration: 15 Jul 2023 → 19 Jul 2023 |
Conference
| Conference | 2023 Genetic and Evolutionary Computation Conference Companion, GECCO 2023 Companion |
|---|---|
| Country/Territory | Portugal |
| City | Lisbon |
| Period | 15/07/23 → 19/07/23 |
Bibliographical note
Publisher Copyright:© 2023 Copyright held by the owner/author(s).
Keywords
- Explainable AI
- Fitness Landscapes
- Neural Networks
- Search Landscapes
- XAI