---
_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: '27506'
abstract:
- lang: eng
  text: Explainability for machine learning gets more and more important in high-stakes
    decisions like real estate appraisal. While traditional hedonic house pricing
    models are fed with hard information based on housing attributes, recently also
    soft information has been incorporated to increase the predictive performance.
    This soft information can be extracted from image data by complex models like
    Convolutional Neural Networks (CNNs). However, these are intransparent which excludes
    their use for high-stakes financial decisions. To overcome this limitation, we
    examine if a two-stage modeling approach can provide explainability. We combine
    visual interpretability by Regression Activation Maps (RAM) for the CNN and a
    linear regression for the overall prediction. Our experiments are based on 62.000
    family homes in Philadelphia and the results indicate that the CNN learns aspects
    related to vegetation and quality aspects of the house from exterior images, improving
    the predictive accuracy of real estate appraisal by up to 5.4%.
author:
- first_name: Jan-Peter
  full_name: Kucklick, Jan-Peter
  id: '77066'
  last_name: Kucklick
citation:
  ama: 'Kucklick J-P. Visual Interpretability of Image-based Real Estate Appraisal.
    In: <i>55th Annual Hawaii International Conference on System Sciences (HICSS-55)</i>.
    ; 2022.'
  apa: Kucklick, J.-P. (2022). Visual Interpretability of Image-based Real Estate
    Appraisal. <i>55th Annual Hawaii International Conference on System Sciences (HICSS-55)</i>.
    Hawaii International Conference on System Science (HICSS), Virtual.
  bibtex: '@inproceedings{Kucklick_2022, title={Visual Interpretability of Image-based
    Real Estate Appraisal}, booktitle={55th Annual Hawaii International Conference
    on System Sciences (HICSS-55)}, author={Kucklick, Jan-Peter}, year={2022} }'
  chicago: Kucklick, Jan-Peter. “Visual Interpretability of Image-Based Real Estate
    Appraisal.” In <i>55th Annual Hawaii International Conference on System Sciences
    (HICSS-55)</i>, 2022.
  ieee: J.-P. Kucklick, “Visual Interpretability of Image-based Real Estate Appraisal,”
    presented at the Hawaii International Conference on System Science (HICSS), Virtual,
    2022.
  mla: Kucklick, Jan-Peter. “Visual Interpretability of Image-Based Real Estate Appraisal.”
    <i>55th Annual Hawaii International Conference on System Sciences (HICSS-55)</i>,
    2022.
  short: 'J.-P. Kucklick, in: 55th Annual Hawaii International Conference on System
    Sciences (HICSS-55), 2022.'
conference:
  end_date: 2022-01-07
  location: Virtual
  name: Hawaii International Conference on System Science (HICSS)
  start_date: 2022-01-03
date_created: 2021-11-17T07:08:15Z
date_updated: 2022-01-06T06:57:40Z
department:
- _id: '195'
- _id: '196'
keyword:
- Explainable Artificial Intelligence (XAI)
- Regression Activation Maps
- Real Estate Appraisal
- Convolutional Block Attention Module
- Computer Vision
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://scholarspace.manoa.hawaii.edu/bitstream/10125/79519/0149.pdf
oa: '1'
publication: 55th Annual Hawaii International Conference on System Sciences (HICSS-55)
status: public
title: Visual Interpretability of Image-based Real Estate Appraisal
type: conference
user_id: '77066'
year: '2022'
...
---
_id: '27507'
abstract:
- lang: eng
  text: Accurate real estate appraisal is essential in decision making processes of
    financial institutions, governments, and trending real estate platforms like Zillow.
    One of the most important factors of a property’s value is its location. However,
    creating accurate quantifications of location remains a challenge. While traditional
    approaches rely on Geographical Information Systems (GIS), recently unstructured
    data in form of images was incorporated in the appraisal process, but text data
    remains an untapped reservoir. Our study shows that using text data in form of
    geolocated Wikipedia articles can increase predictive performance over traditional
    GIS-based methods by 8.2% in spatial out-of-sample validation. A framework to
    automatically extract geographically weighted vector representations for text
    is established and used alongside traditional structural housing features to make
    predictions and to uncover local patterns on sale price for real estate transactions
    between 2015 and 2020 in Allegheny County, Pennsylvania.
author:
- first_name: Tim
  full_name: Heuwinkel, Tim
  last_name: Heuwinkel
- 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: 'Heuwinkel T, Kucklick J-P, Müller O. Using Geolocated Text to Quantify Location
    in Real Estate Appraisal. In: <i>55th Annual Hawaii International Conference on
    System Sciences (HICSS-55)</i>. ; 2022.'
  apa: Heuwinkel, T., Kucklick, J.-P., &#38; Müller, O. (2022). Using Geolocated Text
    to Quantify Location in Real Estate Appraisal. <i>55th Annual Hawaii International
    Conference on System Sciences (HICSS-55)</i>. Hawaii International Conference
    on System Science (HICSS), Virtual.
  bibtex: '@inproceedings{Heuwinkel_Kucklick_Müller_2022, title={Using Geolocated
    Text to Quantify Location in Real Estate Appraisal}, booktitle={55th Annual Hawaii
    International Conference on System Sciences (HICSS-55)}, author={Heuwinkel, Tim
    and Kucklick, Jan-Peter and Müller, Oliver}, year={2022} }'
  chicago: Heuwinkel, Tim, Jan-Peter Kucklick, and Oliver Müller. “Using Geolocated
    Text to Quantify Location in Real Estate Appraisal.” In <i>55th Annual Hawaii
    International Conference on System Sciences (HICSS-55)</i>, 2022.
  ieee: T. Heuwinkel, J.-P. Kucklick, and O. Müller, “Using Geolocated Text to Quantify
    Location in Real Estate Appraisal,” presented at the Hawaii International Conference
    on System Science (HICSS), Virtual, 2022.
  mla: Heuwinkel, Tim, et al. “Using Geolocated Text to Quantify Location in Real
    Estate Appraisal.” <i>55th Annual Hawaii International Conference on System Sciences
    (HICSS-55)</i>, 2022.
  short: 'T. Heuwinkel, J.-P. Kucklick, O. Müller, in: 55th Annual Hawaii International
    Conference on System Sciences (HICSS-55), 2022.'
conference:
  end_date: 2022-01-07
  location: Virtual
  name: Hawaii International Conference on System Science (HICSS)
  start_date: 2022-01-03
date_created: 2021-11-17T07:12:03Z
date_updated: 2022-01-06T06:57:40Z
department:
- _id: '195'
keyword:
- Real Estate Appraisal
- Text Regression
- Natural Language Processing (NLP)
- Location Intelligence
- Wikipedia
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://scholarspace.manoa.hawaii.edu/bitstream/10125/80039/0561.pdf
oa: '1'
publication: 55th Annual Hawaii International Conference on System Sciences (HICSS-55)
status: public
title: Using Geolocated Text to Quantify Location in Real Estate Appraisal
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'
...