---
_id: '48780'
abstract:
- lang: eng
  text: Explainable Artificial Intelligence (XAI) has mainly focused on static learning
    tasks so far. In this paper, we consider XAI in the context of online learning
    in dynamic environments, such as learning from real-time data streams, where models
    are learned incrementally and continuously adapted over the course of time. More
    specifically, we motivate the problem of explaining model change, i.e. explaining
    the difference between models before and after adaptation, instead of the models
    themselves. In this regard, we provide the first efficient model-agnostic approach
    to dynamically detecting, quantifying, and explaining significant model changes.
    Our approach is based on an adaptation of the well-known Permutation Feature Importance
    (PFI) measure. It includes two hyperparameters that control the sensitivity and
    directly influence explanation frequency, so that a human user can adjust the
    method to individual requirements and application needs. We assess and validate
    our method’s efficacy on illustrative synthetic data streams with three popular
    model classes.
author:
- first_name: Maximilian
  full_name: Muschalik, Maximilian
  last_name: Muschalik
- first_name: Fabian
  full_name: Fumagalli, Fabian
  id: '93420'
  last_name: Fumagalli
- first_name: Barbara
  full_name: Hammer, Barbara
  last_name: Hammer
- first_name: Eyke
  full_name: Huellermeier, Eyke
  id: '48129'
  last_name: Huellermeier
citation:
  ama: Muschalik M, Fumagalli F, Hammer B, Huellermeier E. Agnostic Explanation of
    Model Change based on Feature Importance. <i>KI - Künstliche Intelligenz</i>.
    2022;36(3-4):211-224. doi:<a href="https://doi.org/10.1007/s13218-022-00766-6">10.1007/s13218-022-00766-6</a>
  apa: Muschalik, M., Fumagalli, F., Hammer, B., &#38; Huellermeier, E. (2022). Agnostic
    Explanation of Model Change based on Feature Importance. <i>KI - Künstliche Intelligenz</i>,
    <i>36</i>(3–4), 211–224. <a href="https://doi.org/10.1007/s13218-022-00766-6">https://doi.org/10.1007/s13218-022-00766-6</a>
  bibtex: '@article{Muschalik_Fumagalli_Hammer_Huellermeier_2022, title={Agnostic
    Explanation of Model Change based on Feature Importance}, volume={36}, DOI={<a
    href="https://doi.org/10.1007/s13218-022-00766-6">10.1007/s13218-022-00766-6</a>},
    number={3–4}, journal={KI - Künstliche Intelligenz}, publisher={Springer Science
    and Business Media LLC}, author={Muschalik, Maximilian and Fumagalli, Fabian and
    Hammer, Barbara and Huellermeier, Eyke}, year={2022}, pages={211–224} }'
  chicago: 'Muschalik, Maximilian, Fabian Fumagalli, Barbara Hammer, and Eyke Huellermeier.
    “Agnostic Explanation of Model Change Based on Feature Importance.” <i>KI - Künstliche
    Intelligenz</i> 36, no. 3–4 (2022): 211–24. <a href="https://doi.org/10.1007/s13218-022-00766-6">https://doi.org/10.1007/s13218-022-00766-6</a>.'
  ieee: 'M. Muschalik, F. Fumagalli, B. Hammer, and E. Huellermeier, “Agnostic Explanation
    of Model Change based on Feature Importance,” <i>KI - Künstliche Intelligenz</i>,
    vol. 36, no. 3–4, pp. 211–224, 2022, doi: <a href="https://doi.org/10.1007/s13218-022-00766-6">10.1007/s13218-022-00766-6</a>.'
  mla: Muschalik, Maximilian, et al. “Agnostic Explanation of Model Change Based on
    Feature Importance.” <i>KI - Künstliche Intelligenz</i>, vol. 36, no. 3–4, Springer
    Science and Business Media LLC, 2022, pp. 211–24, doi:<a href="https://doi.org/10.1007/s13218-022-00766-6">10.1007/s13218-022-00766-6</a>.
  short: M. Muschalik, F. Fumagalli, B. Hammer, E. Huellermeier, KI - Künstliche Intelligenz
    36 (2022) 211–224.
date_created: 2023-11-10T14:21:06Z
date_updated: 2025-01-16T16:19:35Z
department:
- _id: '660'
doi: 10.1007/s13218-022-00766-6
intvolume: '        36'
issue: 3-4
keyword:
- Artificial Intelligence
language:
- iso: eng
page: 211-224
project:
- _id: '126'
  name: 'TRR 318 - C3: TRR 318 - Subproject C3'
- _id: '117'
  name: 'TRR 318 - C: TRR 318 - Project Area C'
- _id: '109'
  grant_number: '438445824'
  name: 'TRR 318: TRR 318 - Erklärbarkeit konstruieren'
publication: KI - Künstliche Intelligenz
publication_identifier:
  issn:
  - 0933-1875
  - 1610-1987
publication_status: published
publisher: Springer Science and Business Media LLC
status: public
title: Agnostic Explanation of Model Change based on Feature Importance
type: journal_article
user_id: '93420'
volume: 36
year: '2022'
...