---
_id: '61156'
abstract:
- lang: eng
text: Explainability has become an important topic in computer science and artificial
intelligence, leading to a subfield called Explainable Artificial Intelligence
(XAI). The goal of providing or seeking explanations is to achieve (better) ‘understanding’
on the part of the explainee. However, what it means to ‘understand’ is still
not clearly defined, and the concept itself is rarely the subject of scientific
investigation. This conceptual article aims to present a model of forms of understanding
for XAI-explanations and beyond. From an interdisciplinary perspective bringing
together computer science, linguistics, sociology, philosophy and psychology,
a definition of understanding and its forms, assessment, and dynamics during the
process of giving everyday explanations are explored. Two types of understanding
are considered as possible outcomes of explanations, namely enabledness, ‘knowing
how’ to do or decide something, and comprehension, ‘knowing that’ – both in different
degrees (from shallow to deep). Explanations regularly start with shallow understanding
in a specific domain and can lead to deep comprehension and enabledness of the
explanandum, which we see as a prerequisite for human users to gain agency. In
this process, the increase of comprehension and enabledness are highly interdependent.
Against the background of this systematization, special challenges of understanding
in XAI are discussed.
article_number: '101419'
article_type: original
author:
- first_name: Hendrik
full_name: Buschmeier, Hendrik
id: '76456'
last_name: Buschmeier
orcid: 0000-0002-9613-5713
- first_name: Heike M.
full_name: Buhl, Heike M.
id: '27152'
last_name: Buhl
- first_name: Friederike
full_name: Kern, Friederike
last_name: Kern
- first_name: Angela
full_name: Grimminger, Angela
id: '57578'
last_name: Grimminger
- first_name: Helen
full_name: Beierling, Helen
id: '50995'
last_name: Beierling
- first_name: Josephine Beryl
full_name: Fisher, Josephine Beryl
id: '56345'
last_name: Fisher
orcid: 0000-0002-9997-9241
- first_name: André
full_name: Groß, André
id: '93405'
last_name: Groß
orcid: 0000-0002-9593-7220
- first_name: Ilona
full_name: Horwath, Ilona
id: '68836'
last_name: Horwath
- first_name: Nils
full_name: Klowait, Nils
id: '98454'
last_name: Klowait
orcid: 0000-0002-7347-099X
- first_name: Stefan Teodorov
full_name: Lazarov, Stefan Teodorov
id: '90345'
last_name: Lazarov
orcid: 0009-0009-0892-9483
- first_name: Michael
full_name: Lenke, Michael
last_name: Lenke
- first_name: Vivien
full_name: Lohmer, Vivien
last_name: Lohmer
- first_name: Katharina
full_name: Rohlfing, Katharina
id: '50352'
last_name: Rohlfing
orcid: 0000-0002-5676-8233
- first_name: Ingrid
full_name: Scharlau, Ingrid
id: '451'
last_name: Scharlau
orcid: 0000-0003-2364-9489
- first_name: Amit
full_name: Singh, Amit
id: '91018'
last_name: Singh
orcid: 0000-0002-7789-1521
- first_name: Lutz
full_name: Terfloth, Lutz
id: '37320'
last_name: Terfloth
- first_name: Anna-Lisa
full_name: Vollmer, Anna-Lisa
id: '86589'
last_name: Vollmer
- first_name: Yu
full_name: Wang, Yu
last_name: Wang
- first_name: Annedore
full_name: Wilmes, Annedore
last_name: Wilmes
- first_name: Britta
full_name: Wrede, Britta
last_name: Wrede
citation:
ama: Buschmeier H, Buhl HM, Kern F, et al. Forms of Understanding for XAI-Explanations.
Cognitive Systems Research. 2025;94. doi:10.1016/j.cogsys.2025.101419
apa: Buschmeier, H., Buhl, H. M., Kern, F., Grimminger, A., Beierling, H., Fisher,
J. B., Groß, A., Horwath, I., Klowait, N., Lazarov, S. T., Lenke, M., Lohmer,
V., Rohlfing, K., Scharlau, I., Singh, A., Terfloth, L., Vollmer, A.-L., Wang,
Y., Wilmes, A., & Wrede, B. (2025). Forms of Understanding for XAI-Explanations.
Cognitive Systems Research, 94, Article 101419. https://doi.org/10.1016/j.cogsys.2025.101419
bibtex: '@article{Buschmeier_Buhl_Kern_Grimminger_Beierling_Fisher_Groß_Horwath_Klowait_Lazarov_et
al._2025, title={Forms of Understanding for XAI-Explanations}, volume={94}, DOI={10.1016/j.cogsys.2025.101419},
number={101419}, journal={Cognitive Systems Research}, author={Buschmeier, Hendrik
and Buhl, Heike M. and Kern, Friederike and Grimminger, Angela and Beierling,
Helen and Fisher, Josephine Beryl and Groß, André and Horwath, Ilona and Klowait,
Nils and Lazarov, Stefan Teodorov and et al.}, year={2025} }'
chicago: Buschmeier, Hendrik, Heike M. Buhl, Friederike Kern, Angela Grimminger,
Helen Beierling, Josephine Beryl Fisher, André Groß, et al. “Forms of Understanding
for XAI-Explanations.” Cognitive Systems Research 94 (2025). https://doi.org/10.1016/j.cogsys.2025.101419.
ieee: 'H. Buschmeier et al., “Forms of Understanding for XAI-Explanations,”
Cognitive Systems Research, vol. 94, Art. no. 101419, 2025, doi: 10.1016/j.cogsys.2025.101419.'
mla: Buschmeier, Hendrik, et al. “Forms of Understanding for XAI-Explanations.”
Cognitive Systems Research, vol. 94, 101419, 2025, doi:10.1016/j.cogsys.2025.101419.
short: H. Buschmeier, H.M. Buhl, F. Kern, A. Grimminger, H. Beierling, J.B. Fisher,
A. Groß, I. Horwath, N. Klowait, S.T. Lazarov, M. Lenke, V. Lohmer, K. Rohlfing,
I. Scharlau, A. Singh, L. Terfloth, A.-L. Vollmer, Y. Wang, A. Wilmes, B. Wrede,
Cognitive Systems Research 94 (2025).
date_created: 2025-09-08T14:24:32Z
date_updated: 2025-12-05T15:32:25Z
ddc:
- '006'
department:
- _id: '660'
doi: 10.1016/j.cogsys.2025.101419
file:
- access_level: closed
content_type: application/pdf
creator: hbuschme
date_created: 2025-12-01T21:02:20Z
date_updated: 2025-12-01T21:02:20Z
file_id: '62730'
file_name: Buschmeier-etal-2025-COGSYS.pdf
file_size: 10114981
relation: main_file
success: 1
file_date_updated: 2025-12-01T21:02:20Z
has_accepted_license: '1'
intvolume: ' 94'
keyword:
- understanding
- explaining
- explanations
- explainable
- AI
- interdisciplinarity
- comprehension
- enabledness
- agency
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://www.sciencedirect.com/science/article/pii/S1389041725000993?via%3Dihub
oa: '1'
project:
- _id: '111'
name: 'TRR 318; TP A01: Adaptives Erklären'
- _id: '112'
name: 'TRR 318; TP A02: Verstehensprozess einer Erklärung beobachten und auswerten'
- _id: '113'
name: TRR 318 - Subproject A3
- _id: '114'
name: 'TRR 318; TP A04: Integration des technischen Modells in das Partnermodell
bei der Erklärung von digitalen Artefakten'
- _id: '115'
name: 'TRR 318; TP A05: Echtzeitmessung der Aufmerksamkeit im Mensch-Roboter-Erklärdialog'
- _id: '122'
name: TRR 318 - Subproject B3
- _id: '123'
name: TRR 318 - Subproject B5
- _id: '119'
name: TRR 318 - Project Area Ö
publication: Cognitive Systems Research
publication_status: published
quality_controlled: '1'
status: public
title: Forms of Understanding for XAI-Explanations
type: journal_article
user_id: '57578'
volume: 94
year: '2025'
...
---
_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: Proceedings
of the AAAI Conference on Artificial Intelligence (AAAI). Vol 38. ; 2024:14388-14396.
doi:10.1609/aaai.v38i13.29352'
apa: 'Muschalik, M., Fumagalli, F., Hammer, B., & Huellermeier, E. (2024). Beyond
TreeSHAP: Efficient Computation of Any-Order Shapley Interactions for Tree Ensembles.
Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), 38(13),
14388–14396. https://doi.org/10.1609/aaai.v38i13.29352'
bibtex: '@inproceedings{Muschalik_Fumagalli_Hammer_Huellermeier_2024, title={Beyond
TreeSHAP: Efficient Computation of Any-Order Shapley Interactions for Tree Ensembles},
volume={38}, DOI={10.1609/aaai.v38i13.29352},
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 Proceedings of the AAAI Conference on Artificial Intelligence
(AAAI), 38:14388–96, 2024. https://doi.org/10.1609/aaai.v38i13.29352.'
ieee: 'M. Muschalik, F. Fumagalli, B. Hammer, and E. Huellermeier, “Beyond TreeSHAP:
Efficient Computation of Any-Order Shapley Interactions for Tree Ensembles,” in
Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), 2024,
vol. 38, no. 13, pp. 14388–14396, doi: 10.1609/aaai.v38i13.29352.'
mla: 'Muschalik, Maximilian, et al. “Beyond TreeSHAP: Efficient Computation of Any-Order
Shapley Interactions for Tree Ensembles.” Proceedings of the AAAI Conference
on Artificial Intelligence (AAAI), vol. 38, no. 13, 2024, pp. 14388–96, doi:10.1609/aaai.v38i13.29352.'
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: '51368'
abstract:
- lang: eng
text: Dealing with opaque algorithms, the frequent overlap between transparency
and explainability produces seemingly unsolvable dilemmas, as the much-discussed
trade-off between model performance and model transparency. Referring to Niklas
Luhmann's notion of communication, the paper argues that explainability does not
necessarily require transparency and proposes an alternative approach. Explanations
as communicative processes do not imply any disclosure of thoughts or neural processes,
but only reformulations that provide the partners with additional elements and
enable them to understand (from their perspective) what has been done and why.
Recent computational approaches aiming at post-hoc explainability reproduce what
happens in communication, producing explanations of the working of algorithms
that can be different from the processes of the algorithms.
author:
- first_name: 'Elena '
full_name: 'Esposito, Elena '
last_name: Esposito
citation:
ama: Esposito E. Does Explainability Require Transparency? Sociologica. 2023;16(3):17-27.
doi:10.6092/ISSN.1971-8853/15804
apa: Esposito, E. (2023). Does Explainability Require Transparency? Sociologica,
16(3), 17–27. https://doi.org/10.6092/ISSN.1971-8853/15804
bibtex: '@article{Esposito_2023, title={Does Explainability Require Transparency?},
volume={16}, DOI={10.6092/ISSN.1971-8853/15804},
number={3}, journal={Sociologica}, author={Esposito, Elena }, year={2023}, pages={17–27}
}'
chicago: 'Esposito, Elena . “Does Explainability Require Transparency?” Sociologica
16, no. 3 (2023): 17–27. https://doi.org/10.6092/ISSN.1971-8853/15804.'
ieee: 'E. Esposito, “Does Explainability Require Transparency?,” Sociologica,
vol. 16, no. 3, pp. 17–27, 2023, doi: 10.6092/ISSN.1971-8853/15804.'
mla: Esposito, Elena. “Does Explainability Require Transparency?” Sociologica,
vol. 16, no. 3, 2023, pp. 17–27, doi:10.6092/ISSN.1971-8853/15804.
short: E. Esposito, Sociologica 16 (2023) 17–27.
date_created: 2024-02-18T10:16:43Z
date_updated: 2024-02-26T08:46:26Z
department:
- _id: '660'
doi: 10.6092/ISSN.1971-8853/15804
intvolume: ' 16'
issue: '3'
keyword:
- Explainable AI
- Transparency
- Explanation
- Communication
- Sociological systems theory
language:
- iso: eng
page: 17-27
project:
- _id: '121'
grant_number: '438445824'
name: 'TRR 318 - B01: TRR 318 - Ein dialogbasierter Ansatz zur Erklärung von Modellen
des maschinellen Lernens (Teilprojekt B01)'
publication: Sociologica
status: public
title: Does Explainability Require Transparency?
type: journal_article
user_id: '54779'
volume: 16
year: '2023'
...
---
_id: '51369'
abstract:
- lang: eng
text: This short introduction presents the symposium ‘Explaining Machines’. It locates
the debate about Explainable AI in the history of the reflection about AI and
outlines the issues discussed in the contributions.
author:
- first_name: Elena
full_name: Esposito, Elena
last_name: Esposito
citation:
ama: 'Esposito E. Explaining Machines: Social Management of Incomprehensible Algorithms.
Introduction. Sociologica. 2023;16(3):1-4. doi:10.6092/ISSN.1971-8853/16265'
apa: 'Esposito, E. (2023). Explaining Machines: Social Management of Incomprehensible
Algorithms. Introduction. Sociologica, 16(3), 1–4. https://doi.org/10.6092/ISSN.1971-8853/16265'
bibtex: '@article{Esposito_2023, title={Explaining Machines: Social Management of
Incomprehensible Algorithms. Introduction}, volume={16}, DOI={10.6092/ISSN.1971-8853/16265},
number={3}, journal={Sociologica}, author={Esposito, Elena}, year={2023}, pages={1–4}
}'
chicago: 'Esposito, Elena. “Explaining Machines: Social Management of Incomprehensible
Algorithms. Introduction.” Sociologica 16, no. 3 (2023): 1–4. https://doi.org/10.6092/ISSN.1971-8853/16265.'
ieee: 'E. Esposito, “Explaining Machines: Social Management of Incomprehensible
Algorithms. Introduction,” Sociologica, vol. 16, no. 3, pp. 1–4, 2023,
doi: 10.6092/ISSN.1971-8853/16265.'
mla: 'Esposito, Elena. “Explaining Machines: Social Management of Incomprehensible
Algorithms. Introduction.” Sociologica, vol. 16, no. 3, 2023, pp. 1–4,
doi:10.6092/ISSN.1971-8853/16265.'
short: E. Esposito, Sociologica 16 (2023) 1–4.
date_created: 2024-02-18T10:23:23Z
date_updated: 2024-02-26T08:45:56Z
department:
- _id: '660'
doi: 10.6092/ISSN.1971-8853/16265
intvolume: ' 16'
issue: '3'
keyword:
- Explainable AI
- Inexplicability
- Transparency
- Explanation
- Opacity
- Contestability
language:
- iso: eng
page: 1-4
project:
- _id: '121'
grant_number: '438445824'
name: 'TRR 318 - B01: TRR 318 - Ein dialogbasierter Ansatz zur Erklärung von Modellen
des maschinellen Lernens (Teilprojekt B01)'
publication: Sociologica
status: public
title: 'Explaining Machines: Social Management of Incomprehensible Algorithms. Introduction'
type: journal_article
user_id: '54779'
volume: 16
year: '2023'
...
---
_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.
Journal of Decision Systems. Published online 2023:1-41. doi:10.1080/12460125.2023.2207268'
apa: 'Kucklick, J.-P. (2023). HIEF: a holistic interpretability and explainability
framework. Journal of Decision Systems, 1–41. https://doi.org/10.1080/12460125.2023.2207268'
bibtex: '@article{Kucklick_2023, title={HIEF: a holistic interpretability and explainability
framework}, DOI={10.1080/12460125.2023.2207268},
journal={Journal of Decision Systems}, publisher={Taylor & Francis}, author={Kucklick,
Jan-Peter}, year={2023}, pages={1–41} }'
chicago: 'Kucklick, Jan-Peter. “HIEF: A Holistic Interpretability and Explainability
Framework.” Journal of Decision Systems, 2023, 1–41. https://doi.org/10.1080/12460125.2023.2207268.'
ieee: 'J.-P. Kucklick, “HIEF: a holistic interpretability and explainability framework,”
Journal of Decision Systems, pp. 1–41, 2023, doi: 10.1080/12460125.2023.2207268.'
mla: 'Kucklick, Jan-Peter. “HIEF: A Holistic Interpretability and Explainability
Framework.” Journal of Decision Systems, Taylor & Francis, 2023, pp.
1–41, doi:10.1080/12460125.2023.2207268.'
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: '56477'
abstract:
- lang: eng
text: We describe a prototype of a Clinical Decision Support System (CDSS) that
provides (counterfactual) explanations to support accurate medical diagnosis.
The prototype is based on an inherently interpretable Bayesian network (BN). Our
research aims to investigate which explanations are most useful for medical experts
and whether co-constructing explanations can foster trust and acceptance of CDSS.
author:
- first_name: Felix
full_name: Liedeker, Felix
id: '93275'
last_name: Liedeker
- first_name: Philipp
full_name: Cimiano, Philipp
last_name: Cimiano
citation:
ama: 'Liedeker F, Cimiano P. A Prototype of an Interactive Clinical Decision Support
System with Counterfactual Explanations. In: ; 2023.'
apa: Liedeker, F., & Cimiano, P. (2023). A Prototype of an Interactive Clinical
Decision Support System with Counterfactual Explanations. xAI-2023 Late-breaking
Work, Demos and Doctoral Consortium co-located with the 1st World Conference on
eXplainable Artificial Intelligence (xAI-2023), Lissabon.
bibtex: '@inproceedings{Liedeker_Cimiano_2023, title={A Prototype of an Interactive
Clinical Decision Support System with Counterfactual Explanations}, author={Liedeker,
Felix and Cimiano, Philipp}, year={2023} }'
chicago: Liedeker, Felix, and Philipp Cimiano. “A Prototype of an Interactive Clinical
Decision Support System with Counterfactual Explanations,” 2023.
ieee: F. Liedeker and P. Cimiano, “A Prototype of an Interactive Clinical Decision
Support System with Counterfactual Explanations,” presented at the xAI-2023 Late-breaking
Work, Demos and Doctoral Consortium co-located with the 1st World Conference on
eXplainable Artificial Intelligence (xAI-2023), Lissabon, 2023.
mla: Liedeker, Felix, and Philipp Cimiano. A Prototype of an Interactive Clinical
Decision Support System with Counterfactual Explanations. 2023.
short: 'F. Liedeker, P. Cimiano, in: 2023.'
conference:
end_date: 2023-07-28
location: Lissabon
name: xAI-2023 Late-breaking Work, Demos and Doctoral Consortium co-located with
the 1st World Conference on eXplainable Artificial Intelligence (xAI-2023)
start_date: 2023-07-26
date_created: 2024-10-09T14:50:09Z
date_updated: 2024-10-09T15:04:53Z
department:
- _id: '660'
keyword:
- Explainable AI
- Clinical decision support
- Bayesian network
- Counterfactual explanations
language:
- iso: eng
project:
- _id: '128'
name: 'TRR 318 - C5: TRR 318 - Subproject C5'
status: public
title: A Prototype of an Interactive Clinical Decision Support System with Counterfactual
Explanations
type: conference
user_id: '93275'
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: 55th Annual Hawaii International Conference on System Sciences (HICSS-55).
; 2022.'
apa: Kucklick, J.-P. (2022). Visual Interpretability of Image-based Real Estate
Appraisal. 55th Annual Hawaii International Conference on System Sciences (HICSS-55).
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 55th Annual Hawaii International Conference on System Sciences
(HICSS-55), 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.”
55th Annual Hawaii International Conference on System Sciences (HICSS-55),
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:
Wirtschaftsinformatik 2022 Proceedings. ; 2022.'
apa: Kucklick, J.-P. (2022). Towards a model- and data-focused taxonomy of XAI systems.
Wirtschaftsinformatik 2022 Proceedings. 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 Wirtschaftsinformatik 2022 Proceedings, 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.”
Wirtschaftsinformatik 2022 Proceedings, 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: '24456'
abstract:
- lang: eng
text: One objective of current research in explainable intelligent systems is to
implement social aspects in order to increase the relevance of explanations. In
this paper, we argue that a novel conceptual framework is needed to overcome shortcomings
of existing AI systems with little attention to processes of interaction and learning.
Drawing from research in interaction and development, we first outline the novel
conceptual framework that pushes the design of AI systems toward true interactivity
with an emphasis on the role of the partner and social relevance. We propose that
AI systems will be able to provide a meaningful and relevant explanation only
if the process of explaining is extended to active contribution of both partners
that brings about dynamics that is modulated by different levels of analysis.
Accordingly, our conceptual framework comprises monitoring and scaffolding as
key concepts and claims that the process of explaining is not only modulated by
the interaction between explainee and explainer but is embedded into a larger
social context in which conventionalized and routinized behaviors are established.
We discuss our conceptual framework in relation to the established objectives
of transparency and autonomy that are raised for the design of explainable AI
systems currently.
article_type: original
author:
- first_name: Katharina J.
full_name: Rohlfing, Katharina J.
id: '50352'
last_name: Rohlfing
- first_name: Philipp
full_name: Cimiano, Philipp
last_name: Cimiano
- first_name: Ingrid
full_name: Scharlau, Ingrid
id: '451'
last_name: Scharlau
orcid: 0000-0003-2364-9489
- first_name: Tobias
full_name: Matzner, Tobias
id: '65695'
last_name: Matzner
- first_name: Heike M.
full_name: Buhl, Heike M.
id: '27152'
last_name: Buhl
- first_name: Hendrik
full_name: Buschmeier, Hendrik
last_name: Buschmeier
- first_name: Elena
full_name: Esposito, Elena
last_name: Esposito
- first_name: Angela
full_name: Grimminger, Angela
id: '57578'
last_name: Grimminger
- first_name: Barbara
full_name: Hammer, Barbara
last_name: Hammer
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
- first_name: Ilona
full_name: Horwath, Ilona
id: '68836'
last_name: Horwath
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
- first_name: Friederike
full_name: Kern, Friederike
last_name: Kern
- first_name: Stefan
full_name: Kopp, Stefan
last_name: Kopp
- first_name: Kirsten
full_name: Thommes, Kirsten
id: '72497'
last_name: Thommes
- first_name: Axel-Cyrille
full_name: Ngonga Ngomo, Axel-Cyrille
id: '65716'
last_name: Ngonga Ngomo
- first_name: Carsten
full_name: Schulte, Carsten
id: '60311'
last_name: Schulte
- first_name: Henning
full_name: Wachsmuth, Henning
id: '3900'
last_name: Wachsmuth
- first_name: Petra
full_name: Wagner, Petra
last_name: Wagner
- first_name: Britta
full_name: Wrede, Britta
last_name: Wrede
citation:
ama: 'Rohlfing KJ, Cimiano P, Scharlau I, et al. Explanation as a Social Practice:
Toward a Conceptual Framework for the Social Design of AI Systems. IEEE Transactions
on Cognitive and Developmental Systems. 2021;13(3):717-728. doi:10.1109/tcds.2020.3044366'
apa: 'Rohlfing, K. J., Cimiano, P., Scharlau, I., Matzner, T., Buhl, H. M., Buschmeier,
H., Esposito, E., Grimminger, A., Hammer, B., Haeb-Umbach, R., Horwath, I., Hüllermeier,
E., Kern, F., Kopp, S., Thommes, K., Ngonga Ngomo, A.-C., Schulte, C., Wachsmuth,
H., Wagner, P., & Wrede, B. (2021). Explanation as a Social Practice: Toward
a Conceptual Framework for the Social Design of AI Systems. IEEE Transactions
on Cognitive and Developmental Systems, 13(3), 717–728. https://doi.org/10.1109/tcds.2020.3044366'
bibtex: '@article{Rohlfing_Cimiano_Scharlau_Matzner_Buhl_Buschmeier_Esposito_Grimminger_Hammer_Haeb-Umbach_et
al._2021, title={Explanation as a Social Practice: Toward a Conceptual Framework
for the Social Design of AI Systems}, volume={13}, DOI={10.1109/tcds.2020.3044366},
number={3}, journal={IEEE Transactions on Cognitive and Developmental Systems},
author={Rohlfing, Katharina J. and Cimiano, Philipp and Scharlau, Ingrid and Matzner,
Tobias and Buhl, Heike M. and Buschmeier, Hendrik and Esposito, Elena and Grimminger,
Angela and Hammer, Barbara and Haeb-Umbach, Reinhold and et al.}, year={2021},
pages={717–728} }'
chicago: 'Rohlfing, Katharina J., Philipp Cimiano, Ingrid Scharlau, Tobias Matzner,
Heike M. Buhl, Hendrik Buschmeier, Elena Esposito, et al. “Explanation as a Social
Practice: Toward a Conceptual Framework for the Social Design of AI Systems.”
IEEE Transactions on Cognitive and Developmental Systems 13, no. 3 (2021):
717–28. https://doi.org/10.1109/tcds.2020.3044366.'
ieee: 'K. J. Rohlfing et al., “Explanation as a Social Practice: Toward a
Conceptual Framework for the Social Design of AI Systems,” IEEE Transactions
on Cognitive and Developmental Systems, vol. 13, no. 3, pp. 717–728, 2021,
doi: 10.1109/tcds.2020.3044366.'
mla: 'Rohlfing, Katharina J., et al. “Explanation as a Social Practice: Toward a
Conceptual Framework for the Social Design of AI Systems.” IEEE Transactions
on Cognitive and Developmental Systems, vol. 13, no. 3, 2021, pp. 717–28,
doi:10.1109/tcds.2020.3044366.'
short: K.J. Rohlfing, P. Cimiano, I. Scharlau, T. Matzner, H.M. Buhl, H. Buschmeier,
E. Esposito, A. Grimminger, B. Hammer, R. Haeb-Umbach, I. Horwath, E. Hüllermeier,
F. Kern, S. Kopp, K. Thommes, A.-C. Ngonga Ngomo, C. Schulte, H. Wachsmuth, P.
Wagner, B. Wrede, IEEE Transactions on Cognitive and Developmental Systems 13
(2021) 717–728.
date_created: 2021-09-14T20:52:57Z
date_updated: 2023-12-05T10:15:02Z
ddc:
- '300'
department:
- _id: '603'
- _id: '749'
- _id: '424'
- _id: '67'
- _id: '574'
- _id: '184'
- _id: '757'
- _id: '54'
- _id: '178'
doi: 10.1109/tcds.2020.3044366
file:
- access_level: open_access
content_type: application/pdf
creator: haebumb
date_created: 2023-11-20T16:33:51Z
date_updated: 2023-11-20T16:33:51Z
file_id: '49081'
file_name: 2020-12-01_explainability_final_version.pdf
file_size: 626217
relation: main_file
file_date_updated: 2023-11-20T16:33:51Z
has_accepted_license: '1'
intvolume: ' 13'
issue: '3'
keyword:
- Explainability
- process ofexplaining andunderstanding
- explainable artificial systems
language:
- iso: eng
oa: '1'
page: 717-728
project:
- _id: '109'
grant_number: '438445824'
name: 'TRR 318: TRR 318 - Erklärbarkeit konstruieren'
publication: IEEE Transactions on Cognitive and Developmental Systems
publication_identifier:
issn:
- 2379-8920
- 2379-8939
publication_status: published
quality_controlled: '1'
status: public
title: 'Explanation as a Social Practice: Toward a Conceptual Framework for the Social
Design of AI Systems'
type: journal_article
user_id: '42933'
volume: 13
year: '2021'
...