---
_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'
...
---
_id: '29539'
abstract:
- lang: eng
  text: Explainable Artificial Intelligence (XAI) is currently an important topic
    for the application of Machine Learning (ML) in high-stakes decision scenarios.
    Related research focuses on evaluating ML algorithms in terms of interpretability.
    However, providing a human understandable explanation of an intelligent system
    does not only relate to the used ML algorithm. The data and features used also
    have a considerable impact on interpretability. In this paper, we develop a taxonomy
    for describing XAI systems based on aspects about the algorithm and data. The
    proposed taxonomy gives researchers and practitioners opportunities to describe
    and evaluate current XAI systems with respect to interpretability and guides the
    future development of this class of systems.
author:
- first_name: Jan-Peter
  full_name: Kucklick, Jan-Peter
  id: '77066'
  last_name: Kucklick
citation:
  ama: 'Kucklick J-P. Towards a model- and data-focused taxonomy of XAI systems. In:
    <i>Wirtschaftsinformatik 2022 Proceedings</i>. ; 2022.'
  apa: Kucklick, J.-P. (2022). Towards a model- and data-focused taxonomy of XAI systems.
    <i>Wirtschaftsinformatik 2022 Proceedings</i>. Wirtschaftsinformatik 2022 (WI22),
    Nürnberg (online).
  bibtex: '@inproceedings{Kucklick_2022, title={Towards a model- and data-focused
    taxonomy of XAI systems}, booktitle={Wirtschaftsinformatik 2022 Proceedings},
    author={Kucklick, Jan-Peter}, year={2022} }'
  chicago: Kucklick, Jan-Peter. “Towards a Model- and Data-Focused Taxonomy of XAI
    Systems.” In <i>Wirtschaftsinformatik 2022 Proceedings</i>, 2022.
  ieee: J.-P. Kucklick, “Towards a model- and data-focused taxonomy of XAI systems,”
    presented at the Wirtschaftsinformatik 2022 (WI22), Nürnberg (online), 2022.
  mla: Kucklick, Jan-Peter. “Towards a Model- and Data-Focused Taxonomy of XAI Systems.”
    <i>Wirtschaftsinformatik 2022 Proceedings</i>, 2022.
  short: 'J.-P. Kucklick, in: Wirtschaftsinformatik 2022 Proceedings, 2022.'
conference:
  end_date: 2022-02-23
  location: Nürnberg (online)
  name: Wirtschaftsinformatik 2022 (WI22)
  start_date: 2022-02-21
date_created: 2022-01-26T08:22:03Z
date_updated: 2022-01-26T08:24:30Z
department:
- _id: '195'
- _id: '196'
keyword:
- Explainable Artificial Intelligence
- XAI
- Interpretability
- Decision Support Systems
- Taxonomy
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://aisel.aisnet.org/cgi/viewcontent.cgi?article=1056&context=wi2022
oa: '1'
publication: Wirtschaftsinformatik 2022 Proceedings
status: public
title: Towards a model- and data-focused taxonomy of XAI systems
type: conference
user_id: '77066'
year: '2022'
...
---
_id: '35620'
abstract:
- lang: eng
  text: Deep learning models fuel many modern decision support systems, because they
    typically provide high predictive performance. Among other domains, deep learning
    is used in real-estate appraisal, where it allows to extend the analysis from
    hard facts only (e.g., size, age) to also consider more implicit information about
    the location or appearance of houses in the form of image data. However, one downside
    of deep learning models is their intransparent mechanic of decision making, which
    leads to a trade-off between accuracy and interpretability. This limits their
    applicability for tasks where a justification of the decision is necessary. Therefore,
    in this paper, we first combine different perspectives on interpretability into
    a multi-dimensional framework for a socio-technical perspective on explainable
    artificial intelligence. Second, we measure the performance gains of using multi-view
    deep learning which leverages additional image data (satellite images) for real
    estate appraisal. Third, we propose and test a novel post-hoc explainability method
    called Grad-Ram. This modified version of Grad-Cam mitigates the intransparency
    of convolutional neural networks (CNNs) for predicting continuous outcome variables.
    With this, we try to reduce the accuracy-interpretability trade-off of multi-view
    deep learning models. Our proposed network architecture outperforms traditional
    hedonic regression models by 34% in terms of MAE. Furthermore, we find that the
    used satellite images are the second most important predictor after square feet
    in our model and that the network learns interpretable patterns about the neighborhood
    structure and density.
article_type: original
author:
- first_name: Jan-Peter
  full_name: Kucklick, Jan-Peter
  id: '77066'
  last_name: Kucklick
- first_name: Oliver
  full_name: Müller, Oliver
  id: '72849'
  last_name: Müller
citation:
  ama: 'Kucklick J-P, Müller O. Tackling the Accuracy–Interpretability Trade-off:
    Interpretable Deep Learning Models for Satellite Image-based Real Estate Appraisal.
    <i>ACM Transactions on Management Information Systems</i>. Published online 2022.
    doi:<a href="https://doi.org/10.1145/3567430">10.1145/3567430</a>'
  apa: 'Kucklick, J.-P., &#38; Müller, O. (2022). Tackling the Accuracy–Interpretability
    Trade-off: Interpretable Deep Learning Models for Satellite Image-based Real Estate
    Appraisal. <i>ACM Transactions on Management Information Systems</i>. <a href="https://doi.org/10.1145/3567430">https://doi.org/10.1145/3567430</a>'
  bibtex: '@article{Kucklick_Müller_2022, title={Tackling the Accuracy–Interpretability
    Trade-off: Interpretable Deep Learning Models for Satellite Image-based Real Estate
    Appraisal}, DOI={<a href="https://doi.org/10.1145/3567430">10.1145/3567430</a>},
    journal={ACM Transactions on Management Information Systems}, publisher={Association
    for Computing Machinery (ACM)}, author={Kucklick, Jan-Peter and Müller, Oliver},
    year={2022} }'
  chicago: 'Kucklick, Jan-Peter, and Oliver Müller. “Tackling the Accuracy–Interpretability
    Trade-off: Interpretable Deep Learning Models for Satellite Image-Based Real Estate
    Appraisal.” <i>ACM Transactions on Management Information Systems</i>, 2022. <a
    href="https://doi.org/10.1145/3567430">https://doi.org/10.1145/3567430</a>.'
  ieee: 'J.-P. Kucklick and O. Müller, “Tackling the Accuracy–Interpretability Trade-off:
    Interpretable Deep Learning Models for Satellite Image-based Real Estate Appraisal,”
    <i>ACM Transactions on Management Information Systems</i>, 2022, doi: <a href="https://doi.org/10.1145/3567430">10.1145/3567430</a>.'
  mla: 'Kucklick, Jan-Peter, and Oliver Müller. “Tackling the Accuracy–Interpretability
    Trade-off: Interpretable Deep Learning Models for Satellite Image-Based Real Estate
    Appraisal.” <i>ACM Transactions on Management Information Systems</i>, Association
    for Computing Machinery (ACM), 2022, doi:<a href="https://doi.org/10.1145/3567430">10.1145/3567430</a>.'
  short: J.-P. Kucklick, O. Müller, ACM Transactions on Management Information Systems
    (2022).
date_created: 2023-01-10T05:16:02Z
date_updated: 2023-01-10T05:20:18Z
department:
- _id: '195'
- _id: '196'
doi: 10.1145/3567430
keyword:
- Interpretability
- Convolutional Neural Network
- Accuracy-Interpretability Trade-Of
- Real Estate Appraisal
- Hedonic Pricing
- Grad-Ram
language:
- iso: eng
main_file_link:
- url: https://dl.acm.org/doi/pdf/10.1145/3567430
publication: ACM Transactions on Management Information Systems
publication_identifier:
  issn:
  - 2158-656X
  - 2158-6578
publication_status: published
publisher: Association for Computing Machinery (ACM)
status: public
title: 'Tackling the Accuracy–Interpretability Trade-off: Interpretable Deep Learning
  Models for Satellite Image-based Real Estate Appraisal'
type: journal_article
user_id: '77066'
year: '2022'
...