---
res:
  bibo_abstract:
  - 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.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Jan-Peter
      foaf_name: Kucklick, Jan-Peter
      foaf_surname: Kucklick
      foaf_workInfoHomepage: http://www.librecat.org/personId=77066
  - foaf_Person:
      foaf_givenName: Oliver
      foaf_name: Müller, Oliver
      foaf_surname: Müller
      foaf_workInfoHomepage: http://www.librecat.org/personId=72849
  bibo_doi: 10.1145/3567430
  dct_date: 2022^xs_gYear
  dct_isPartOf:
  - http://id.crossref.org/issn/2158-656X
  - http://id.crossref.org/issn/2158-6578
  dct_language: eng
  dct_publisher: Association for Computing Machinery (ACM)@
  dct_subject:
  - Interpretability
  - Convolutional Neural Network
  - Accuracy-Interpretability Trade-Of
  - Real Estate Appraisal
  - Hedonic Pricing
  - Grad-Ram
  dct_title: 'Tackling the Accuracy–Interpretability Trade-off: Interpretable Deep
    Learning Models for Satellite Image-based Real Estate Appraisal@'
...