---
_id: '53073'
abstract:
- lang: eng
  text: While shallow decision trees may be interpretable, larger ensemble models
    like gradient-boosted trees, which often set the state of the art in machine learning
    problems involving tabular data, still remain black box models. As a remedy, the
    Shapley value (SV) is a well-known concept in explainable artificial intelligence
    (XAI) research for quantifying additive feature attributions of predictions. The
    model-specific TreeSHAP methodology solves the exponential complexity for retrieving
    exact SVs from tree-based models. Expanding beyond individual feature attribution,
    Shapley interactions reveal the impact of intricate feature interactions of any
    order. In this work, we present TreeSHAP-IQ, an efficient method to compute any-order
    additive Shapley interactions for predictions of tree-based models. TreeSHAP-IQ
    is supported by a mathematical framework that exploits polynomial arithmetic to
    compute the interaction scores in a single recursive traversal of the tree, akin
    to Linear TreeSHAP. We apply TreeSHAP-IQ on state-of-the-art tree ensembles and
    explore interactions on well-established benchmark datasets.
author:
- first_name: Maximilian
  full_name: Muschalik, Maximilian
  last_name: Muschalik
- first_name: Fabian
  full_name: Fumagalli, Fabian
  id: '93420'
  last_name: Fumagalli
- first_name: Barbara
  full_name: Hammer, Barbara
  last_name: Hammer
- first_name: Eyke
  full_name: Huellermeier, Eyke
  id: '48129'
  last_name: Huellermeier
citation:
  ama: 'Muschalik M, Fumagalli F, Hammer B, Huellermeier E. Beyond TreeSHAP: Efficient
    Computation of Any-Order Shapley Interactions for Tree Ensembles. In: <i>Proceedings
    of the AAAI Conference on Artificial Intelligence (AAAI)</i>. Vol 38. ; 2024:14388-14396.
    doi:<a href="https://doi.org/10.1609/aaai.v38i13.29352">10.1609/aaai.v38i13.29352</a>'
  apa: 'Muschalik, M., Fumagalli, F., Hammer, B., &#38; Huellermeier, E. (2024). Beyond
    TreeSHAP: Efficient Computation of Any-Order Shapley Interactions for Tree Ensembles.
    <i>Proceedings of the AAAI Conference on Artificial Intelligence (AAAI)</i>, <i>38</i>(13),
    14388–14396. <a href="https://doi.org/10.1609/aaai.v38i13.29352">https://doi.org/10.1609/aaai.v38i13.29352</a>'
  bibtex: '@inproceedings{Muschalik_Fumagalli_Hammer_Huellermeier_2024, title={Beyond
    TreeSHAP: Efficient Computation of Any-Order Shapley Interactions for Tree Ensembles},
    volume={38}, DOI={<a href="https://doi.org/10.1609/aaai.v38i13.29352">10.1609/aaai.v38i13.29352</a>},
    number={13}, booktitle={Proceedings of the AAAI Conference on Artificial Intelligence
    (AAAI)}, author={Muschalik, Maximilian and Fumagalli, Fabian and Hammer, Barbara
    and Huellermeier, Eyke}, year={2024}, pages={14388–14396} }'
  chicago: 'Muschalik, Maximilian, Fabian Fumagalli, Barbara Hammer, and Eyke Huellermeier.
    “Beyond TreeSHAP: Efficient Computation of Any-Order Shapley Interactions for
    Tree Ensembles.” In <i>Proceedings of the AAAI Conference on Artificial Intelligence
    (AAAI)</i>, 38:14388–96, 2024. <a href="https://doi.org/10.1609/aaai.v38i13.29352">https://doi.org/10.1609/aaai.v38i13.29352</a>.'
  ieee: 'M. Muschalik, F. Fumagalli, B. Hammer, and E. Huellermeier, “Beyond TreeSHAP:
    Efficient Computation of Any-Order Shapley Interactions for Tree Ensembles,” in
    <i>Proceedings of the AAAI Conference on Artificial Intelligence (AAAI)</i>, 2024,
    vol. 38, no. 13, pp. 14388–14396, doi: <a href="https://doi.org/10.1609/aaai.v38i13.29352">10.1609/aaai.v38i13.29352</a>.'
  mla: 'Muschalik, Maximilian, et al. “Beyond TreeSHAP: Efficient Computation of Any-Order
    Shapley Interactions for Tree Ensembles.” <i>Proceedings of the AAAI Conference
    on Artificial Intelligence (AAAI)</i>, vol. 38, no. 13, 2024, pp. 14388–96, doi:<a
    href="https://doi.org/10.1609/aaai.v38i13.29352">10.1609/aaai.v38i13.29352</a>.'
  short: 'M. Muschalik, F. Fumagalli, B. Hammer, E. Huellermeier, in: Proceedings
    of the AAAI Conference on Artificial Intelligence (AAAI), 2024, pp. 14388–14396.'
date_created: 2024-03-27T14:50:04Z
date_updated: 2025-09-11T16:20:11Z
department:
- _id: '660'
doi: 10.1609/aaai.v38i13.29352
intvolume: '        38'
issue: '13'
keyword:
- Explainable Artificial Intelligence
language:
- iso: eng
page: 14388-14396
project:
- _id: '126'
  name: 'TRR 318 - C3: TRR 318 - Subproject C3'
- _id: '109'
  name: 'TRR 318: TRR 318 - Erklärbarkeit konstruieren'
- _id: '117'
  name: 'TRR 318 - C: TRR 318 - Project Area C'
publication: Proceedings of the AAAI Conference on Artificial Intelligence (AAAI)
publication_identifier:
  issn:
  - 2374-3468
  - 2159-5399
publication_status: published
status: public
title: 'Beyond TreeSHAP: Efficient Computation of Any-Order Shapley Interactions for
  Tree Ensembles'
type: conference
user_id: '93420'
volume: 38
year: '2024'
...
---
_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: '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'
...