---
_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'
...