---
_id: '45299'
abstract:
- lang: eng
  text: Many applications are driven by Machine Learning (ML) today. While complex
    ML models lead to an accurate prediction, their inner decision-making is obfuscated.
    However, especially for high-stakes decisions, interpretability and explainability
    of the model are necessary. Therefore, we develop a holistic interpretability
    and explainability framework (HIEF) to objectively describe and evaluate an intelligent
    system’s explainable AI (XAI) capacities. This guides data scientists to create
    more transparent models. To evaluate our framework, we analyse 50 real estate
    appraisal papers to ensure the robustness of HIEF. Additionally, we identify six
    typical types of intelligent systems, so-called archetypes, which range from explanatory
    to predictive, and demonstrate how researchers can use the framework to identify
    blind-spot topics in their domain. Finally, regarding comprehensiveness, we used
    a random sample of six intelligent systems and conducted an applicability check
    to provide external validity.
author:
- first_name: Jan-Peter
  full_name: Kucklick, Jan-Peter
  id: '77066'
  last_name: Kucklick
citation:
  ama: 'Kucklick J-P. HIEF: a holistic interpretability and explainability framework.
    <i>Journal of Decision Systems</i>. Published online 2023:1-41. doi:<a href="https://doi.org/10.1080/12460125.2023.2207268">10.1080/12460125.2023.2207268</a>'
  apa: 'Kucklick, J.-P. (2023). HIEF: a holistic interpretability and explainability
    framework. <i>Journal of Decision Systems</i>, 1–41. <a href="https://doi.org/10.1080/12460125.2023.2207268">https://doi.org/10.1080/12460125.2023.2207268</a>'
  bibtex: '@article{Kucklick_2023, title={HIEF: a holistic interpretability and explainability
    framework}, DOI={<a href="https://doi.org/10.1080/12460125.2023.2207268">10.1080/12460125.2023.2207268</a>},
    journal={Journal of Decision Systems}, publisher={Taylor &#38; Francis}, author={Kucklick,
    Jan-Peter}, year={2023}, pages={1–41} }'
  chicago: 'Kucklick, Jan-Peter. “HIEF: A Holistic Interpretability and Explainability
    Framework.” <i>Journal of Decision Systems</i>, 2023, 1–41. <a href="https://doi.org/10.1080/12460125.2023.2207268">https://doi.org/10.1080/12460125.2023.2207268</a>.'
  ieee: 'J.-P. Kucklick, “HIEF: a holistic interpretability and explainability framework,”
    <i>Journal of Decision Systems</i>, pp. 1–41, 2023, doi: <a href="https://doi.org/10.1080/12460125.2023.2207268">10.1080/12460125.2023.2207268</a>.'
  mla: 'Kucklick, Jan-Peter. “HIEF: A Holistic Interpretability and Explainability
    Framework.” <i>Journal of Decision Systems</i>, Taylor &#38; Francis, 2023, pp.
    1–41, doi:<a href="https://doi.org/10.1080/12460125.2023.2207268">10.1080/12460125.2023.2207268</a>.'
  short: J.-P. Kucklick, Journal of Decision Systems (2023) 1–41.
date_created: 2023-05-26T05:04:45Z
date_updated: 2023-05-26T05:08:36Z
department:
- _id: '195'
- _id: '196'
doi: 10.1080/12460125.2023.2207268
keyword:
- Explainable AI (XAI)
- machine learning
- interpretability
- real estate appraisal
- framework
- taxonomy
language:
- iso: eng
main_file_link:
- url: https://www.tandfonline.com/doi/full/10.1080/12460125.2023.2207268
page: 1-41
publication: Journal of Decision Systems
publication_identifier:
  issn:
  - 1246-0125
  - 2116-7052
publication_status: published
publisher: Taylor & Francis
status: public
title: 'HIEF: a holistic interpretability and explainability framework'
type: journal_article
user_id: '77066'
year: '2023'
...