---
_id: '58224'
author:
- first_name: Philip
full_name: Kenneweg, Philip
last_name: Kenneweg
- first_name: Tristan
full_name: Kenneweg, Tristan
last_name: Kenneweg
- first_name: Fabian
full_name: Fumagalli, Fabian
last_name: Fumagalli
- first_name: Barbara
full_name: Hammer, Barbara
last_name: Hammer
citation:
ama: 'Kenneweg P, Kenneweg T, Fumagalli F, Hammer B. No learning rates needed: Introducing
SALSA - Stable Armijo Line Search Adaptation. In: 2024 International Joint
Conference on Neural Networks (IJCNN). ; 2024:1-8. doi:10.1109/IJCNN60899.2024.10650124'
apa: 'Kenneweg, P., Kenneweg, T., Fumagalli, F., & Hammer, B. (2024). No learning
rates needed: Introducing SALSA - Stable Armijo Line Search Adaptation. 2024
International Joint Conference on Neural Networks (IJCNN), 1–8. https://doi.org/10.1109/IJCNN60899.2024.10650124'
bibtex: '@inproceedings{Kenneweg_Kenneweg_Fumagalli_Hammer_2024, title={No learning
rates needed: Introducing SALSA - Stable Armijo Line Search Adaptation}, DOI={10.1109/IJCNN60899.2024.10650124},
booktitle={2024 International Joint Conference on Neural Networks (IJCNN)}, author={Kenneweg,
Philip and Kenneweg, Tristan and Fumagalli, Fabian and Hammer, Barbara}, year={2024},
pages={1–8} }'
chicago: 'Kenneweg, Philip, Tristan Kenneweg, Fabian Fumagalli, and Barbara Hammer.
“No Learning Rates Needed: Introducing SALSA - Stable Armijo Line Search Adaptation.”
In 2024 International Joint Conference on Neural Networks (IJCNN), 1–8,
2024. https://doi.org/10.1109/IJCNN60899.2024.10650124.'
ieee: 'P. Kenneweg, T. Kenneweg, F. Fumagalli, and B. Hammer, “No learning rates
needed: Introducing SALSA - Stable Armijo Line Search Adaptation,” in 2024
International Joint Conference on Neural Networks (IJCNN), 2024, pp. 1–8,
doi: 10.1109/IJCNN60899.2024.10650124.'
mla: 'Kenneweg, Philip, et al. “No Learning Rates Needed: Introducing SALSA - Stable
Armijo Line Search Adaptation.” 2024 International Joint Conference on Neural
Networks (IJCNN), 2024, pp. 1–8, doi:10.1109/IJCNN60899.2024.10650124.'
short: 'P. Kenneweg, T. Kenneweg, F. Fumagalli, B. Hammer, in: 2024 International
Joint Conference on Neural Networks (IJCNN), 2024, pp. 1–8.'
date_created: 2025-01-16T16:21:28Z
date_updated: 2025-09-11T15:37:42Z
department:
- _id: '660'
doi: 10.1109/IJCNN60899.2024.10650124
keyword:
- Training
- Schedules
- Codes
- Search methods
- Source coding
- Computer architecture
- Transformers
language:
- iso: eng
page: 1-8
project:
- _id: '109'
name: 'TRR 318: TRR 318 - Erklärbarkeit konstruieren'
- _id: '117'
name: 'TRR 318 - C: TRR 318 - Project Area C'
- _id: '126'
name: 'TRR 318 - C3: TRR 318 - Subproject C3'
publication: 2024 International Joint Conference on Neural Networks (IJCNN)
status: public
title: 'No learning rates needed: Introducing SALSA - Stable Armijo Line Search Adaptation'
type: conference
user_id: '93420'
year: '2024'
...
---
_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: '55311'
abstract:
- lang: eng
text: Addressing the limitations of individual attribution scores via the Shapley
value (SV), the field of explainable AI (XAI) has recently explored intricate
interactions of features or data points. In particular, extensions of the SV,
such as the Shapley Interaction Index (SII), have been proposed as a measure to
still benefit from the axiomatic basis of the SV. However, similar to the SV,
their exact computation remains computationally prohibitive. Hence, we propose
with SVARM-IQ a sampling-based approach to efficiently approximate Shapley-based
interaction indices of any order. SVARM-IQ can be applied to a broad class of
interaction indices, including the SII, by leveraging a novel stratified representation.
We provide non-asymptotic theoretical guarantees on its approximation quality
and empirically demonstrate that SVARM-IQ achieves state-of-the-art estimation
results in practical XAI scenarios on different model classes and application
domains.
author:
- first_name: Patrick
full_name: Kolpaczki, Patrick
last_name: Kolpaczki
- 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: 'Kolpaczki P, Muschalik M, Fumagalli F, Hammer B, Huellermeier E. SVARM-IQ:
Efficient Approximation of Any-order Shapley Interactions through Stratification.
In: Proceedings of The 27th International Conference on Artificial Intelligence
and Statistics (AISTATS). Vol 238. Proceedings of Machine Learning Research.
PMLR; 2024:3520–3528.'
apa: 'Kolpaczki, P., Muschalik, M., Fumagalli, F., Hammer, B., & Huellermeier,
E. (2024). SVARM-IQ: Efficient Approximation of Any-order Shapley Interactions
through Stratification. Proceedings of The 27th International Conference on
Artificial Intelligence and Statistics (AISTATS), 238, 3520–3528.'
bibtex: '@inproceedings{Kolpaczki_Muschalik_Fumagalli_Hammer_Huellermeier_2024,
series={Proceedings of Machine Learning Research}, title={SVARM-IQ: Efficient
Approximation of Any-order Shapley Interactions through Stratification}, volume={238},
booktitle={Proceedings of The 27th International Conference on Artificial Intelligence
and Statistics (AISTATS)}, publisher={PMLR}, author={Kolpaczki, Patrick and Muschalik,
Maximilian and Fumagalli, Fabian and Hammer, Barbara and Huellermeier, Eyke},
year={2024}, pages={3520–3528}, collection={Proceedings of Machine Learning Research}
}'
chicago: 'Kolpaczki, Patrick, Maximilian Muschalik, Fabian Fumagalli, Barbara Hammer,
and Eyke Huellermeier. “SVARM-IQ: Efficient Approximation of Any-Order Shapley
Interactions through Stratification.” In Proceedings of The 27th International
Conference on Artificial Intelligence and Statistics (AISTATS), 238:3520–3528.
Proceedings of Machine Learning Research. PMLR, 2024.'
ieee: 'P. Kolpaczki, M. Muschalik, F. Fumagalli, B. Hammer, and E. Huellermeier,
“SVARM-IQ: Efficient Approximation of Any-order Shapley Interactions through Stratification,”
in Proceedings of The 27th International Conference on Artificial Intelligence
and Statistics (AISTATS), 2024, vol. 238, pp. 3520–3528.'
mla: 'Kolpaczki, Patrick, et al. “SVARM-IQ: Efficient Approximation of Any-Order
Shapley Interactions through Stratification.” Proceedings of The 27th International
Conference on Artificial Intelligence and Statistics (AISTATS), vol. 238,
PMLR, 2024, pp. 3520–3528.'
short: 'P. Kolpaczki, M. Muschalik, F. Fumagalli, B. Hammer, E. Huellermeier, in:
Proceedings of The 27th International Conference on Artificial Intelligence and
Statistics (AISTATS), PMLR, 2024, pp. 3520–3528.'
date_created: 2024-07-18T09:39:14Z
date_updated: 2025-09-11T16:22:30Z
department:
- _id: '660'
intvolume: ' 238'
language:
- iso: eng
page: 3520–3528
project:
- _id: '109'
name: 'TRR 318: TRR 318 - Erklärbarkeit konstruieren'
- _id: '117'
name: 'TRR 318 - C: TRR 318 - Project Area C'
- _id: '126'
name: 'TRR 318 - C3: TRR 318 - Subproject C3'
publication: Proceedings of The 27th International Conference on Artificial Intelligence
and Statistics (AISTATS)
publisher: PMLR
series_title: Proceedings of Machine Learning Research
status: public
title: 'SVARM-IQ: Efficient Approximation of Any-order Shapley Interactions through
Stratification'
type: conference
user_id: '93420'
volume: 238
year: '2024'
...
---
_id: '58223'
abstract:
- lang: eng
text: The Shapley value (SV) is a prevalent approach of allocating credit to machine
learning (ML) entities to understand black box ML models. Enriching such interpretations
with higher-order interactions is inevitable for complex systems, where the Shapley
Interaction Index (SII) is a direct axiomatic extension of the SV. While it is
well-known that the SV yields an optimal approximation of any game via a weighted
least square (WLS) objective, an extension of this result to SII has been a long-standing
open problem, which even led to the proposal of an alternative index. In this
work, we characterize higher-order SII as a solution to a WLS problem, which constructs
an optimal approximation via SII and k-Shapley values (k-SII). We prove this representation
for the SV and pairwise SII and give empirically validated conjectures for higher
orders. As a result, we propose KernelSHAP-IQ, a direct extension of KernelSHAP
for SII, and demonstrate state-of-the-art performance for feature interactions.
author:
- first_name: Fabian
full_name: Fumagalli, Fabian
last_name: Fumagalli
- first_name: Maximilian
full_name: Muschalik, Maximilian
last_name: Muschalik
- first_name: Patrick
full_name: Kolpaczki, Patrick
last_name: Kolpaczki
- first_name: Eyke
full_name: Hüllermeier, Eyke
last_name: Hüllermeier
- first_name: Barbara
full_name: Hammer, Barbara
last_name: Hammer
citation:
ama: 'Fumagalli F, Muschalik M, Kolpaczki P, Hüllermeier E, Hammer B. KernelSHAP-IQ:
Weighted Least Square Optimization for Shapley Interactions. In: Proceedings
of the 41st International Conference on Machine Learning (ICML). Vol 235.
Proceedings of Machine Learning Research. PMLR; 2024:14308–14342.'
apa: 'Fumagalli, F., Muschalik, M., Kolpaczki, P., Hüllermeier, E., & Hammer,
B. (2024). KernelSHAP-IQ: Weighted Least Square Optimization for Shapley Interactions.
Proceedings of the 41st International Conference on Machine Learning (ICML),
235, 14308–14342.'
bibtex: '@inproceedings{Fumagalli_Muschalik_Kolpaczki_Hüllermeier_Hammer_2024, series={Proceedings
of Machine Learning Research}, title={KernelSHAP-IQ: Weighted Least Square Optimization
for Shapley Interactions}, volume={235}, booktitle={Proceedings of the 41st International
Conference on Machine Learning (ICML)}, publisher={PMLR}, author={Fumagalli, Fabian
and Muschalik, Maximilian and Kolpaczki, Patrick and Hüllermeier, Eyke and Hammer,
Barbara}, year={2024}, pages={14308–14342}, collection={Proceedings of Machine
Learning Research} }'
chicago: 'Fumagalli, Fabian, Maximilian Muschalik, Patrick Kolpaczki, Eyke Hüllermeier,
and Barbara Hammer. “KernelSHAP-IQ: Weighted Least Square Optimization for Shapley
Interactions.” In Proceedings of the 41st International Conference on Machine
Learning (ICML), 235:14308–14342. Proceedings of Machine Learning Research.
PMLR, 2024.'
ieee: 'F. Fumagalli, M. Muschalik, P. Kolpaczki, E. Hüllermeier, and B. Hammer,
“KernelSHAP-IQ: Weighted Least Square Optimization for Shapley Interactions,”
in Proceedings of the 41st International Conference on Machine Learning (ICML),
2024, vol. 235, pp. 14308–14342.'
mla: 'Fumagalli, Fabian, et al. “KernelSHAP-IQ: Weighted Least Square Optimization
for Shapley Interactions.” Proceedings of the 41st International Conference
on Machine Learning (ICML), vol. 235, PMLR, 2024, pp. 14308–14342.'
short: 'F. Fumagalli, M. Muschalik, P. Kolpaczki, E. Hüllermeier, B. Hammer, in:
Proceedings of the 41st International Conference on Machine Learning (ICML), PMLR,
2024, pp. 14308–14342.'
date_created: 2025-01-16T16:12:16Z
date_updated: 2025-09-11T16:27:05Z
department:
- _id: '660'
intvolume: ' 235'
language:
- iso: eng
page: 14308–14342
project:
- _id: '109'
name: 'TRR 318: TRR 318 - Erklärbarkeit konstruieren'
- _id: '117'
name: 'TRR 318 - C: TRR 318 - Project Area C'
- _id: '126'
name: 'TRR 318 - C3: TRR 318 - Subproject C3'
publication: Proceedings of the 41st International Conference on Machine Learning
(ICML)
publisher: PMLR
series_title: Proceedings of Machine Learning Research
status: public
title: 'KernelSHAP-IQ: Weighted Least Square Optimization for Shapley Interactions'
type: conference
user_id: '93420'
volume: 235
year: '2024'
...
---
_id: '61228'
author:
- first_name: Maximilian
full_name: Muschalik, Maximilian
last_name: Muschalik
- first_name: Hubert
full_name: Baniecki, Hubert
last_name: Baniecki
- first_name: Fabian
full_name: Fumagalli, Fabian
id: '93420'
last_name: Fumagalli
- first_name: Patrick
full_name: Kolpaczki, Patrick
last_name: Kolpaczki
- 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, Baniecki H, Fumagalli F, Kolpaczki P, Hammer B, Huellermeier
E. shapiq: Shapley interactions for machine learning. In: Advances in Neural
Information Processing Systems (NeurIPS). Vol 37. ; 2024:130324–130357.'
apa: 'Muschalik, M., Baniecki, H., Fumagalli, F., Kolpaczki, P., Hammer, B., &
Huellermeier, E. (2024). shapiq: Shapley interactions for machine learning. Advances
in Neural Information Processing Systems (NeurIPS), 37, 130324–130357.'
bibtex: '@inproceedings{Muschalik_Baniecki_Fumagalli_Kolpaczki_Hammer_Huellermeier_2024,
title={shapiq: Shapley interactions for machine learning}, volume={37}, booktitle={Advances
in Neural Information Processing Systems (NeurIPS)}, author={Muschalik, Maximilian
and Baniecki, Hubert and Fumagalli, Fabian and Kolpaczki, Patrick and Hammer,
Barbara and Huellermeier, Eyke}, year={2024}, pages={130324–130357} }'
chicago: 'Muschalik, Maximilian, Hubert Baniecki, Fabian Fumagalli, Patrick Kolpaczki,
Barbara Hammer, and Eyke Huellermeier. “Shapiq: Shapley Interactions for Machine
Learning.” In Advances in Neural Information Processing Systems (NeurIPS),
37:130324–130357, 2024.'
ieee: 'M. Muschalik, H. Baniecki, F. Fumagalli, P. Kolpaczki, B. Hammer, and E.
Huellermeier, “shapiq: Shapley interactions for machine learning,” in Advances
in Neural Information Processing Systems (NeurIPS), 2024, vol. 37, pp. 130324–130357.'
mla: 'Muschalik, Maximilian, et al. “Shapiq: Shapley Interactions for Machine Learning.”
Advances in Neural Information Processing Systems (NeurIPS), vol. 37, 2024,
pp. 130324–130357.'
short: 'M. Muschalik, H. Baniecki, F. Fumagalli, P. Kolpaczki, B. Hammer, E. Huellermeier,
in: Advances in Neural Information Processing Systems (NeurIPS), 2024, pp. 130324–130357.'
date_created: 2025-09-11T15:39:01Z
date_updated: 2025-09-11T16:17:35Z
department:
- _id: '660'
intvolume: ' 37'
language:
- iso: eng
page: 130324–130357
project:
- _id: '117'
name: TRR 318 - Project Area C
- _id: '126'
name: TRR 318 - Subproject C3
- _id: '109'
name: 'TRR 318: Erklärbarkeit konstruieren'
publication: Advances in Neural Information Processing Systems (NeurIPS)
status: public
title: 'shapiq: Shapley interactions for machine learning'
type: conference
user_id: '93420'
volume: 37
year: '2024'
...
---
_id: '61230'
author:
- first_name: Patrick
full_name: Kolpaczki, Patrick
last_name: Kolpaczki
- first_name: Viktor
full_name: Bengs, Viktor
last_name: Bengs
- first_name: Maximilian
full_name: Muschalik, Maximilian
last_name: Muschalik
- first_name: Eyke
full_name: Hüllermeier, Eyke
last_name: Hüllermeier
citation:
ama: 'Kolpaczki P, Bengs V, Muschalik M, Hüllermeier E. Approximating the shapley
value without marginal contributions. In: Proceedings of the AAAI Conference
on Artificial Intelligence (AAAI). Vol 38. ; 2024:13246–13255.'
apa: Kolpaczki, P., Bengs, V., Muschalik, M., & Hüllermeier, E. (2024). Approximating
the shapley value without marginal contributions. Proceedings of the AAAI Conference
on Artificial Intelligence (AAAI), 38(12), 13246–13255.
bibtex: '@inproceedings{Kolpaczki_Bengs_Muschalik_Hüllermeier_2024, title={Approximating
the shapley value without marginal contributions}, volume={38}, number={12}, booktitle={Proceedings
of the AAAI conference on Artificial Intelligence (AAAI)}, author={Kolpaczki,
Patrick and Bengs, Viktor and Muschalik, Maximilian and Hüllermeier, Eyke}, year={2024},
pages={13246–13255} }'
chicago: Kolpaczki, Patrick, Viktor Bengs, Maximilian Muschalik, and Eyke Hüllermeier.
“Approximating the Shapley Value without Marginal Contributions.” In Proceedings
of the AAAI Conference on Artificial Intelligence (AAAI), 38:13246–13255,
2024.
ieee: P. Kolpaczki, V. Bengs, M. Muschalik, and E. Hüllermeier, “Approximating the
shapley value without marginal contributions,” in Proceedings of the AAAI conference
on Artificial Intelligence (AAAI), 2024, vol. 38, no. 12, pp. 13246–13255.
mla: Kolpaczki, Patrick, et al. “Approximating the Shapley Value without Marginal
Contributions.” Proceedings of the AAAI Conference on Artificial Intelligence
(AAAI), vol. 38, no. 12, 2024, pp. 13246–13255.
short: 'P. Kolpaczki, V. Bengs, M. Muschalik, E. Hüllermeier, in: Proceedings of
the AAAI Conference on Artificial Intelligence (AAAI), 2024, pp. 13246–13255.'
date_created: 2025-09-11T15:46:40Z
date_updated: 2025-09-11T16:17:54Z
department:
- _id: '660'
intvolume: ' 38'
issue: '12'
language:
- iso: eng
page: 13246–13255
project:
- _id: '117'
name: TRR 318 - Project Area C
- _id: '126'
name: TRR 318 - Subproject C3
- _id: '109'
name: 'TRR 318: Erklärbarkeit konstruieren'
publication: Proceedings of the AAAI conference on Artificial Intelligence (AAAI)
status: public
title: Approximating the shapley value without marginal contributions
type: conference
user_id: '93420'
volume: 38
year: '2024'
...
---
_id: '61176'
abstract:
- lang: eng
text: We revisit the phenomenon of syntactic complexity convergence in conversational
interaction, originally found for English dialogue, which has theoretical implication
for dialogical concepts such as mutual understanding. We use a modified metric
to quantify syntactic complexity based on dependency parsing. The results show
that syntactic complexity convergence can be statistically confirmed in one of
three selected German datasets that were analysed. Given that the dataset which
shows such convergence is much larger than the other two selected datasets, the
empirical results indicate a certain degree of linguistic generality of syntactic
complexity convergence in conversational interaction. We also found a different
type of syntactic complexity convergence in one of the datasets while further
investigation is still necessary.
author:
- first_name: Yu
full_name: Wang, Yu
last_name: Wang
- first_name: Hendrik
full_name: Buschmeier, Hendrik
id: '76456'
last_name: Buschmeier
orcid: 0000-0002-9613-5713
citation:
ama: 'Wang Y, Buschmeier H. Revisiting the phenomenon of syntactic complexity convergence
on German dialogue data. In: Proceedings of the 20th Conference on Natural
Language Processing (KONVENS 2024). ; 2024:75–80.'
apa: Wang, Y., & Buschmeier, H. (2024). Revisiting the phenomenon of syntactic
complexity convergence on German dialogue data. Proceedings of the 20th Conference
on Natural Language Processing (KONVENS 2024), 75–80.
bibtex: '@inproceedings{Wang_Buschmeier_2024, place={Vienna, Austria}, title={Revisiting
the phenomenon of syntactic complexity convergence on German dialogue data}, booktitle={Proceedings
of the 20th Conference on Natural Language Processing (KONVENS 2024)}, author={Wang,
Yu and Buschmeier, Hendrik}, year={2024}, pages={75–80} }'
chicago: Wang, Yu, and Hendrik Buschmeier. “Revisiting the Phenomenon of Syntactic
Complexity Convergence on German Dialogue Data.” In Proceedings of the 20th
Conference on Natural Language Processing (KONVENS 2024), 75–80. Vienna, Austria,
2024.
ieee: Y. Wang and H. Buschmeier, “Revisiting the phenomenon of syntactic complexity
convergence on German dialogue data,” in Proceedings of the 20th Conference
on Natural Language Processing (KONVENS 2024), Vienna, Austria, 2024, pp.
75–80.
mla: Wang, Yu, and Hendrik Buschmeier. “Revisiting the Phenomenon of Syntactic Complexity
Convergence on German Dialogue Data.” Proceedings of the 20th Conference on
Natural Language Processing (KONVENS 2024), 2024, pp. 75–80.
short: 'Y. Wang, H. Buschmeier, in: Proceedings of the 20th Conference on Natural
Language Processing (KONVENS 2024), Vienna, Austria, 2024, pp. 75–80.'
conference:
end_date: 2024-09-12
location: Vienna, Austria
name: 20th Conference on Natural Language Processing (KONVENS 2024)
start_date: 2024-09-11
date_created: 2025-09-11T07:08:24Z
date_updated: 2025-09-12T06:25:05Z
department:
- _id: '660'
extern: '1'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://aclanthology.org/2024.konvens-main.8
oa: '1'
page: 75–80
place: Vienna, Austria
project:
- _id: '112'
name: 'TRR 318; TP A02: Verstehensprozess einer Erklärung beobachten und auswerten'
publication: Proceedings of the 20th Conference on Natural Language Processing (KONVENS
2024)
publication_status: published
quality_controlled: '1'
status: public
title: Revisiting the phenomenon of syntactic complexity convergence on German dialogue
data
type: conference
user_id: '76456'
year: '2024'
...
---
_id: '55911'
abstract:
- lang: eng
text: According to the Entropy Rate Constancy (ERC) principle, the information density
of a text is approximately constant over its length. Whether this principle also
applies to nonverbal communication signals is still under investigation. We perform
empirical analyses of video-recorded dialogue data and investigate whether listener
gaze, as an important nonverbal communication signal, adheres to the ERC principle.
Results show (1) that the ERC principle holds for listener gaze; and (2) that
the two linguistic factors syntactic complexity and turn transition potential
are weakly correlated with local entropy of listener gaze.
author:
- first_name: Yu
full_name: Wang, Yu
last_name: Wang
- first_name: Yang
full_name: Xu, Yang
last_name: Xu
- first_name: Gabriel
full_name: Skantze, Gabriel
last_name: Skantze
- first_name: Hendrik
full_name: Buschmeier, Hendrik
id: '76456'
last_name: Buschmeier
orcid: 0000-0002-9613-5713
citation:
ama: 'Wang Y, Xu Y, Skantze G, Buschmeier H. How much does nonverbal communication
conform to entropy rate constancy?: A case study on listener gaze in interaction.
In: Findings of the Association for Computational Linguistics ACL 2024.
; 2024:3533–3545.'
apa: 'Wang, Y., Xu, Y., Skantze, G., & Buschmeier, H. (2024). How much does
nonverbal communication conform to entropy rate constancy?: A case study on listener
gaze in interaction. Findings of the Association for Computational Linguistics
ACL 2024, 3533–3545.'
bibtex: '@inproceedings{Wang_Xu_Skantze_Buschmeier_2024, place={Bangkok, Thailand},
title={How much does nonverbal communication conform to entropy rate constancy?:
A case study on listener gaze in interaction}, booktitle={Findings of the Association
for Computational Linguistics ACL 2024}, author={Wang, Yu and Xu, Yang and Skantze,
Gabriel and Buschmeier, Hendrik}, year={2024}, pages={3533–3545} }'
chicago: 'Wang, Yu, Yang Xu, Gabriel Skantze, and Hendrik Buschmeier. “How Much
Does Nonverbal Communication Conform to Entropy Rate Constancy?: A Case Study
on Listener Gaze in Interaction.” In Findings of the Association for Computational
Linguistics ACL 2024, 3533–3545. Bangkok, Thailand, 2024.'
ieee: 'Y. Wang, Y. Xu, G. Skantze, and H. Buschmeier, “How much does nonverbal communication
conform to entropy rate constancy?: A case study on listener gaze in interaction,”
in Findings of the Association for Computational Linguistics ACL 2024,
Bangkok, Thailand, 2024, pp. 3533–3545.'
mla: 'Wang, Yu, et al. “How Much Does Nonverbal Communication Conform to Entropy
Rate Constancy?: A Case Study on Listener Gaze in Interaction.” Findings of
the Association for Computational Linguistics ACL 2024, 2024, pp. 3533–3545.'
short: 'Y. Wang, Y. Xu, G. Skantze, H. Buschmeier, in: Findings of the Association
for Computational Linguistics ACL 2024, Bangkok, Thailand, 2024, pp. 3533–3545.'
conference:
location: Bangkok, Thailand
name: 62nd Annual Meeting of the Association for Computational Linguistics
date_created: 2024-08-30T07:42:28Z
date_updated: 2025-09-12T06:25:05Z
department:
- _id: '660'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://aclanthology.org/2024.findings-acl.210/
oa: '1'
page: 3533–3545
place: Bangkok, Thailand
project:
- _id: '112'
name: 'TRR 318 - A02: TRR 318 - Verstehensprozess einer Erklärung beobachten und
auswerten (Teilprojekt A02)'
publication: Findings of the Association for Computational Linguistics ACL 2024
quality_controlled: '1'
status: public
title: 'How much does nonverbal communication conform to entropy rate constancy?:
A case study on listener gaze in interaction'
type: conference
user_id: '76456'
year: '2024'
...
---
_id: '56314'
author:
- first_name: Alina Naomi
full_name: Riechmann, Alina Naomi
last_name: Riechmann
- first_name: Hendrik
full_name: Buschmeier, Hendrik
id: '76456'
last_name: Buschmeier
orcid: 0000-0002-9613-5713
citation:
ama: 'Riechmann AN, Buschmeier H. Automatic reconstruction of dialogue participants’
coordinating gaze behavior from multiple camera perspectives. In: Book of Abstracts
of the 2nd International Multimodal Communication Symposium. ; 2024:38–39.'
apa: Riechmann, A. N., & Buschmeier, H. (2024). Automatic reconstruction of
dialogue participants’ coordinating gaze behavior from multiple camera perspectives.
Book of Abstracts of the 2nd International Multimodal Communication Symposium,
38–39.
bibtex: '@inproceedings{Riechmann_Buschmeier_2024, place={Frankfurt am Main, Germany},
title={Automatic reconstruction of dialogue participants’ coordinating gaze behavior
from multiple camera perspectives}, booktitle={Book of Abstracts of the 2nd International
Multimodal Communication Symposium}, author={Riechmann, Alina Naomi and Buschmeier,
Hendrik}, year={2024}, pages={38–39} }'
chicago: Riechmann, Alina Naomi, and Hendrik Buschmeier. “Automatic Reconstruction
of Dialogue Participants’ Coordinating Gaze Behavior from Multiple Camera Perspectives.”
In Book of Abstracts of the 2nd International Multimodal Communication Symposium,
38–39. Frankfurt am Main, Germany, 2024.
ieee: A. N. Riechmann and H. Buschmeier, “Automatic reconstruction of dialogue participants’
coordinating gaze behavior from multiple camera perspectives,” in Book of Abstracts
of the 2nd International Multimodal Communication Symposium, Frankfurt am
Main, Germany, 2024, pp. 38–39.
mla: Riechmann, Alina Naomi, and Hendrik Buschmeier. “Automatic Reconstruction of
Dialogue Participants’ Coordinating Gaze Behavior from Multiple Camera Perspectives.”
Book of Abstracts of the 2nd International Multimodal Communication Symposium,
2024, pp. 38–39.
short: 'A.N. Riechmann, H. Buschmeier, in: Book of Abstracts of the 2nd International
Multimodal Communication Symposium, Frankfurt am Main, Germany, 2024, pp. 38–39.'
conference:
end_date: 2024-09-27
location: Frankfurt am Main, Germany
name: 2nd International Multimodal Communication Symposium
start_date: 2024-09-25
date_created: 2024-10-03T16:08:04Z
date_updated: 2025-09-12T06:30:39Z
department:
- _id: '660'
extern: '1'
language:
- iso: eng
page: 38–39
place: Frankfurt am Main, Germany
project:
- _id: '112'
name: 'TRR 318 - A02: TRR 318 - Verstehensprozess einer Erklärung beobachten und
auswerten (Teilprojekt A02)'
publication: Book of Abstracts of the 2nd International Multimodal Communication Symposium
publication_status: published
quality_controlled: '1'
related_material:
link:
- relation: poster
url: https://doi.org/10.6084/m9.figshare.27080500
status: public
title: Automatic reconstruction of dialogue participants’ coordinating gaze behavior
from multiple camera perspectives
type: conference_abstract
user_id: '76456'
year: '2024'
...
---
_id: '58722'
abstract:
- lang: eng
text: Dialects introduce syntactic and lexical variations in language that occur
in regional or social groups. Most NLP methods are not sensitive to such variations.
This may lead to unfair behavior of the methods, conveying negative bias towards
dialect speakers. While previous work has studied dialect-related fairness for
aspects like hate speech, other aspects of biased language, such as lewdness,
remain fully unexplored. To fill this gap, we investigate performance disparities
between dialects in the detection of five aspects of biased language and how to
mitigate them. To alleviate bias, we present a multitask learning approach that
models dialect language as an auxiliary task to incorporate syntactic and lexical
variations. In our experiments with African-American English dialect, we provide
empirical evidence that complementing common learning approaches with dialect
modeling improves their fairness. Furthermore, the results suggest that multitask
learning achieves state-of-the-art performance and helps to detect properties
of biased language more reliably.
author:
- first_name: Maximilian
full_name: Spliethöver, Maximilian
id: '84035'
last_name: Spliethöver
orcid: 0000-0003-4364-1409
- first_name: Sai Nikhil
full_name: Menon, Sai Nikhil
last_name: Menon
- first_name: Henning
full_name: Wachsmuth, Henning
id: '3900'
last_name: Wachsmuth
citation:
ama: 'Spliethöver M, Menon SN, Wachsmuth H. Disentangling Dialect from Social Bias
via Multitask Learning to Improve Fairness. In: Ku L-W, Martins A, Srikumar V,
eds. Findings of the Association for Computational Linguistics: ACL 2024.
Association for Computational Linguistics; 2024:9294–9313. doi:10.18653/v1/2024.findings-acl.553'
apa: 'Spliethöver, M., Menon, S. N., & Wachsmuth, H. (2024). Disentangling Dialect
from Social Bias via Multitask Learning to Improve Fairness. In L.-W. Ku, A. Martins,
& V. Srikumar (Eds.), Findings of the Association for Computational Linguistics:
ACL 2024 (pp. 9294–9313). Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.findings-acl.553'
bibtex: '@inproceedings{Spliethöver_Menon_Wachsmuth_2024, place={Bangkok, Thailand},
title={Disentangling Dialect from Social Bias via Multitask Learning to Improve
Fairness}, DOI={10.18653/v1/2024.findings-acl.553},
booktitle={Findings of the Association for Computational Linguistics: ACL 2024},
publisher={Association for Computational Linguistics}, author={Spliethöver, Maximilian
and Menon, Sai Nikhil and Wachsmuth, Henning}, editor={Ku, Lun-Wei and Martins,
Andre and Srikumar, Vivek}, year={2024}, pages={9294–9313} }'
chicago: 'Spliethöver, Maximilian, Sai Nikhil Menon, and Henning Wachsmuth. “Disentangling
Dialect from Social Bias via Multitask Learning to Improve Fairness.” In Findings
of the Association for Computational Linguistics: ACL 2024, edited by Lun-Wei
Ku, Andre Martins, and Vivek Srikumar, 9294–9313. Bangkok, Thailand: Association
for Computational Linguistics, 2024. https://doi.org/10.18653/v1/2024.findings-acl.553.'
ieee: 'M. Spliethöver, S. N. Menon, and H. Wachsmuth, “Disentangling Dialect from
Social Bias via Multitask Learning to Improve Fairness,” in Findings of the
Association for Computational Linguistics: ACL 2024, 2024, pp. 9294–9313,
doi: 10.18653/v1/2024.findings-acl.553.'
mla: 'Spliethöver, Maximilian, et al. “Disentangling Dialect from Social Bias via
Multitask Learning to Improve Fairness.” Findings of the Association for Computational
Linguistics: ACL 2024, edited by Lun-Wei Ku et al., Association for Computational
Linguistics, 2024, pp. 9294–9313, doi:10.18653/v1/2024.findings-acl.553.'
short: 'M. Spliethöver, S.N. Menon, H. Wachsmuth, in: L.-W. Ku, A. Martins, V. Srikumar
(Eds.), Findings of the Association for Computational Linguistics: ACL 2024, Association
for Computational Linguistics, Bangkok, Thailand, 2024, pp. 9294–9313.'
date_created: 2025-02-20T08:18:01Z
date_updated: 2025-09-12T09:52:59Z
department:
- _id: '600'
- _id: '660'
doi: 10.18653/v1/2024.findings-acl.553
editor:
- first_name: Lun-Wei
full_name: Ku, Lun-Wei
last_name: Ku
- first_name: Andre
full_name: Martins, Andre
last_name: Martins
- first_name: Vivek
full_name: Srikumar, Vivek
last_name: Srikumar
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://aclanthology.org/2024.findings-acl.553/
oa: '1'
page: 9294–9313
place: Bangkok, Thailand
project:
- _id: '118'
name: 'TRR 318 - INF: TRR 318 - Project Area INF'
publication: 'Findings of the Association for Computational Linguistics: ACL 2024'
publisher: Association for Computational Linguistics
related_material:
link:
- relation: software
url: https://github.com/webis-de/acl24-dialect-aware-bias-detection
status: public
title: Disentangling Dialect from Social Bias via Multitask Learning to Improve Fairness
type: conference
user_id: '84035'
year: '2024'
...
---
_id: '61273'
abstract:
- lang: eng
text: "In human-machine explanation interactions, such as tutoring systems or customer
support chatbots, it is important for the machine explainer to infer the human
user's understanding. Nonverbal signals play an important role for expressing
mental states like understanding and confusion in these interactions. However,
an individual's expressions may vary depending on other factors. In cases where
these factors are unknown, machine learning methods that infer understanding from
nonverbal cues become unreliable. Stress for example has been shown to affect
human expression, but it is not clear from the current research how stress affects
the expression of understanding.\r\nTo address this gap, we design a paradigm
that induces understanding and confusion through game rule explanations. During
the explanations, self-perceived understanding and confusion are annotated by
the participants. A stress condition is also introduced to enable the investigation
of changes in the expression of social signals under stress.\r\nWe conducted a
study to validate the stress induction and participants reported a statistically
significant increase in stress during the stress condition compared to the neutral
control condition. \r\nAdditionally, feedback from participants shows that the
paradigm is effective in inducing understanding and confusion. \r\nThis paradigm
paves the way for further studies investigating social signals of understanding
to improve human-machine explanation interactions for varying contexts."
author:
- first_name: Jonas
full_name: Paletschek, Jonas
id: '98941'
last_name: Paletschek
citation:
ama: 'Paletschek J. A Paradigm to Investigate Social Signals of Understanding and
Their Susceptibility to Stress. In: 12th International Conference on Affective
Computing & Intelligent Interaction. IEEE; 2024. doi:10.1109/ACII63134.2024.00040'
apa: Paletschek, J. (2024). A Paradigm to Investigate Social Signals of Understanding
and Their Susceptibility to Stress. 12th International Conference on Affective
Computing & Intelligent Interaction. 12th International Conference on
Affective Computing & Intelligent Interaction, Glasgow. https://doi.org/10.1109/ACII63134.2024.00040
bibtex: '@inproceedings{Paletschek_2024, title={A Paradigm to Investigate Social
Signals of Understanding and Their Susceptibility to Stress}, DOI={10.1109/ACII63134.2024.00040},
booktitle={12th International Conference on Affective Computing & Intelligent
Interaction}, publisher={IEEE}, author={Paletschek, Jonas}, year={2024} }'
chicago: Paletschek, Jonas. “A Paradigm to Investigate Social Signals of Understanding
and Their Susceptibility to Stress.” In 12th International Conference on Affective
Computing & Intelligent Interaction. IEEE, 2024. https://doi.org/10.1109/ACII63134.2024.00040.
ieee: 'J. Paletschek, “A Paradigm to Investigate Social Signals of Understanding
and Their Susceptibility to Stress,” presented at the 12th International Conference
on Affective Computing & Intelligent Interaction, Glasgow, 2024, doi: 10.1109/ACII63134.2024.00040.'
mla: Paletschek, Jonas. “A Paradigm to Investigate Social Signals of Understanding
and Their Susceptibility to Stress.” 12th International Conference on Affective
Computing & Intelligent Interaction, IEEE, 2024, doi:10.1109/ACII63134.2024.00040.
short: 'J. Paletschek, in: 12th International Conference on Affective Computing
& Intelligent Interaction, IEEE, 2024.'
conference:
end_date: 2024-09-18
location: Glasgow
name: 12th International Conference on Affective Computing & Intelligent Interaction
start_date: 2024-09-15
date_created: 2025-09-15T11:24:56Z
date_updated: 2025-09-16T07:57:53Z
ddc:
- '150'
department:
- _id: '660'
doi: 10.1109/ACII63134.2024.00040
file:
- access_level: closed
content_type: application/pdf
creator: paletsch
date_created: 2025-09-15T11:18:01Z
date_updated: 2025-09-15T11:18:01Z
file_id: '61274'
file_name: ACII2024_Camera_Ready.pdf
file_size: 8807478
relation: main_file
success: 1
file_date_updated: 2025-09-15T11:18:01Z
has_accepted_license: '1'
keyword:
- Understanding
- Nonverbal Social Signals
- Stress Induction
- Explanation
- Machine Learning Bias
language:
- iso: eng
project:
- _id: '1200'
name: TRR 318 - Teilprojekt A6 - Inklusive Ko-Konstruktion sozialer Signale des
Verstehens
publication: 12th International Conference on Affective Computing & Intelligent Interaction
publication_status: published
publisher: IEEE
status: public
title: A Paradigm to Investigate Social Signals of Understanding and Their Susceptibility
to Stress
type: conference
user_id: '98941'
year: '2024'
...
---
_id: '61290'
abstract:
- lang: eng
text: ffective computing often relies on audiovisual data to identify affective
states from non-verbal signals, such as facial expressions and vocal cues. Since
automatic affect recognition can be used in sensitive applications, such as healthcare
and education, it is crucial to understand how models arrive at their decisions.
Interpretability of machine learning models is the goal of the emerging research
area of Explainable AI (explainable AI (XAI)). This scoping review aims to survey
the field of audiovisual affective machine learning to identify how XAI is applied
in this domain. We first provide an overview of XAI concepts relevant to affective
computing. Next, following the recommended PRISMA guidelines, we perform a literature
search in the ACM, IEEE, Web of Science and PubMed databases. After systematically
reviewing 1190 articles, a final set of 65 papers is included in our analysis.
We quantitatively summarize the scope, methods and evaluation of the XAI techniques
used in the identified papers. Our findings show encouraging developments for
using XAI to explain models in audiovisual affective computing, yet only a limited
set of methods are used in the reviewed works. Following a critical discussion,
we provide recommendations for incorporating interpretability in future work for
affective machine learnin
article_type: review
author:
- first_name: David
full_name: Johnson, David
id: '97208'
last_name: Johnson
- first_name: Olya
full_name: Hakobyan, Olya
last_name: Hakobyan
- first_name: Jonas
full_name: Paletschek, Jonas
id: '98941'
last_name: Paletschek
- first_name: Hanna
full_name: Drimalla, Hanna
last_name: Drimalla
citation:
ama: 'Johnson D, Hakobyan O, Paletschek J, Drimalla H. Explainable AI for Audio
and Visual Affective Computing: A Scoping Review. IEEE Transactions on Affective
Computing. 2024;16(2):518-536. doi:10.1109/taffc.2024.3505269'
apa: 'Johnson, D., Hakobyan, O., Paletschek, J., & Drimalla, H. (2024). Explainable
AI for Audio and Visual Affective Computing: A Scoping Review. IEEE Transactions
on Affective Computing, 16(2), 518–536. https://doi.org/10.1109/taffc.2024.3505269'
bibtex: '@article{Johnson_Hakobyan_Paletschek_Drimalla_2024, title={Explainable
AI for Audio and Visual Affective Computing: A Scoping Review}, volume={16}, DOI={10.1109/taffc.2024.3505269},
number={2}, journal={IEEE Transactions on Affective Computing}, publisher={Institute
of Electrical and Electronics Engineers (IEEE)}, author={Johnson, David and Hakobyan,
Olya and Paletschek, Jonas and Drimalla, Hanna}, year={2024}, pages={518–536}
}'
chicago: 'Johnson, David, Olya Hakobyan, Jonas Paletschek, and Hanna Drimalla. “Explainable
AI for Audio and Visual Affective Computing: A Scoping Review.” IEEE Transactions
on Affective Computing 16, no. 2 (2024): 518–36. https://doi.org/10.1109/taffc.2024.3505269.'
ieee: 'D. Johnson, O. Hakobyan, J. Paletschek, and H. Drimalla, “Explainable AI
for Audio and Visual Affective Computing: A Scoping Review,” IEEE Transactions
on Affective Computing, vol. 16, no. 2, pp. 518–536, 2024, doi: 10.1109/taffc.2024.3505269.'
mla: 'Johnson, David, et al. “Explainable AI for Audio and Visual Affective Computing:
A Scoping Review.” IEEE Transactions on Affective Computing, vol. 16, no.
2, Institute of Electrical and Electronics Engineers (IEEE), 2024, pp. 518–36,
doi:10.1109/taffc.2024.3505269.'
short: D. Johnson, O. Hakobyan, J. Paletschek, H. Drimalla, IEEE Transactions on
Affective Computing 16 (2024) 518–536.
date_created: 2025-09-16T07:24:07Z
date_updated: 2025-09-16T08:02:23Z
ddc:
- '000'
department:
- _id: '660'
doi: 10.1109/taffc.2024.3505269
file:
- access_level: closed
content_type: application/pdf
creator: johnson
date_created: 2025-09-16T07:34:27Z
date_updated: 2025-09-16T07:34:27Z
file_id: '61291'
file_name: Explainable_AI_for_Audio_and_Visual_Affective_Computing_A_Scoping_Review.pdf
file_size: 3252812
relation: main_file
success: 1
file_date_updated: 2025-09-16T07:34:27Z
has_accepted_license: '1'
intvolume: ' 16'
issue: '2'
language:
- iso: eng
page: 518-536
project:
- _id: '110'
name: TRR 318 - Project Area A
- _id: '1204'
name: TRR 318 - Teilprojekt IRG BI
- _id: '1200'
name: TRR 318 - Teilprojekt A6 - Inklusive Ko-Konstruktion sozialer Signale des
Verstehens
publication: IEEE Transactions on Affective Computing
publication_identifier:
issn:
- 1949-3045
- 2371-9850
publication_status: published
publisher: Institute of Electrical and Electronics Engineers (IEEE)
status: public
title: 'Explainable AI for Audio and Visual Affective Computing: A Scoping Review'
type: journal_article
user_id: '97208'
volume: 16
year: '2024'
...
---
_id: '55429'
abstract:
- lang: eng
text: A detailed understanding of the cognitive process underlying diagnostic reasoning
in medical experts is currently lacking. While high-level theories like hypothetico-deductive
reasoning were proposed long ago, the inner workings of the step-by-step dynamics
within the mind remain unknown. We present a fully automated approach to elicit,
monitor, and record diagnostic reasoning processes at a fine-grained level. A
web-based user interface enables physicians to carry out a full diagnosis process
on a simulated patient, given as a pre-defined clinical vignette. By collecting
the physician’s information queries and hypothesis revisions, highly detailed
diagnostic reasoning trajectories are captured leading to a diagnosis and its
justification. Four expert epileptologists with a mean experience of 19 years
were recruited to evaluate the system and share their impressions in semi-structured
interviews. We find that the recorded trajectories validate proposed theories
on broader diagnostic reasoning, while also providing valuable additional details
extending previous findings.
author:
- first_name: Dominik
full_name: Battefeld, Dominik
id: '91864'
last_name: Battefeld
orcid: 0000-0002-5480-0594
- first_name: Sigrid
full_name: Mues, Sigrid
last_name: Mues
- first_name: Tim
full_name: Wehner, Tim
last_name: Wehner
- first_name: Patrick
full_name: House, Patrick
last_name: House
- first_name: Christoph
full_name: Kellinghaus, Christoph
last_name: Kellinghaus
- first_name: Jörg
full_name: Wellmer, Jörg
last_name: Wellmer
- first_name: Stefan
full_name: Kopp, Stefan
last_name: Kopp
citation:
ama: 'Battefeld D, Mues S, Wehner T, et al. Revealing the Dynamics of Medical Diagnostic
Reasoning as Step-by-Step Cognitive Process Trajectories. In: Proceedings of
the 46th Annual Conference of the Cognitive Science Society. ; 2024.'
apa: Battefeld, D., Mues, S., Wehner, T., House, P., Kellinghaus, C., Wellmer, J.,
& Kopp, S. (2024). Revealing the Dynamics of Medical Diagnostic Reasoning
as Step-by-Step Cognitive Process Trajectories. Proceedings of the 46th Annual
Conference of the Cognitive Science Society. The Annual Meeting of the Cognitive
Science Society, Rotterdam, NL.
bibtex: '@inproceedings{Battefeld_Mues_Wehner_House_Kellinghaus_Wellmer_Kopp_2024,
title={Revealing the Dynamics of Medical Diagnostic Reasoning as Step-by-Step
Cognitive Process Trajectories}, booktitle={Proceedings of the 46th Annual Conference
of the Cognitive Science Society}, author={Battefeld, Dominik and Mues, Sigrid
and Wehner, Tim and House, Patrick and Kellinghaus, Christoph and Wellmer, Jörg
and Kopp, Stefan}, year={2024} }'
chicago: Battefeld, Dominik, Sigrid Mues, Tim Wehner, Patrick House, Christoph Kellinghaus,
Jörg Wellmer, and Stefan Kopp. “Revealing the Dynamics of Medical Diagnostic Reasoning
as Step-by-Step Cognitive Process Trajectories.” In Proceedings of the 46th
Annual Conference of the Cognitive Science Society, 2024.
ieee: D. Battefeld et al., “Revealing the Dynamics of Medical Diagnostic
Reasoning as Step-by-Step Cognitive Process Trajectories,” presented at the The
Annual Meeting of the Cognitive Science Society, Rotterdam, NL, 2024.
mla: Battefeld, Dominik, et al. “Revealing the Dynamics of Medical Diagnostic Reasoning
as Step-by-Step Cognitive Process Trajectories.” Proceedings of the 46th Annual
Conference of the Cognitive Science Society, 2024.
short: 'D. Battefeld, S. Mues, T. Wehner, P. House, C. Kellinghaus, J. Wellmer,
S. Kopp, in: Proceedings of the 46th Annual Conference of the Cognitive Science
Society, 2024.'
conference:
end_date: 2024-07-27
location: Rotterdam, NL
name: The Annual Meeting of the Cognitive Science Society
start_date: 2024-07-24
date_created: 2024-07-30T08:17:35Z
date_updated: 2025-09-23T15:11:19Z
ddc:
- '000'
department:
- _id: '660'
file:
- access_level: closed
content_type: application/pdf
creator: doba2
date_created: 2024-07-30T08:21:13Z
date_updated: 2024-07-30T08:21:13Z
file_id: '55430'
file_name: Battefeld_2024_Revealing-the-dynamics.pdf
file_size: 486513
relation: main_file
success: 1
file_date_updated: 2024-07-30T08:21:13Z
has_accepted_license: '1'
keyword:
- Differential Diagnosis
- Diagnostic Reasoning
- Reasoning Process Analysis
- Seizure
- Epilepsy
language:
- iso: eng
project:
- _id: '128'
name: 'TRR 318 - C5: TRR 318 - Subproject C5'
publication: Proceedings of the 46th Annual Conference of the Cognitive Science Society
publication_status: published
quality_controlled: '1'
status: public
title: Revealing the Dynamics of Medical Diagnostic Reasoning as Step-by-Step Cognitive
Process Trajectories
type: conference
user_id: '91864'
year: '2024'
...
---
_id: '48355'
abstract:
- lang: eng
text: "Unsupervised speech disentanglement aims at separating fast varying from\r\nslowly
varying components of a speech signal. In this contribution, we take a\r\ncloser
look at the embedding vector representing the slowly varying signal\r\ncomponents,
commonly named the speaker embedding vector. We ask, which\r\nproperties of a
speaker's voice are captured and investigate to which extent do\r\nindividual
embedding vector components sign responsible for them, using the\r\nconcept of
Shapley values. Our findings show that certain speaker-specific\r\nacoustic-phonetic
properties can be fairly well predicted from the speaker\r\nembedding, while the
investigated more abstract voice quality features cannot."
author:
- first_name: Frederik
full_name: Rautenberg, Frederik
id: '72602'
last_name: Rautenberg
- first_name: Michael
full_name: Kuhlmann, Michael
id: '49871'
last_name: Kuhlmann
- first_name: Jana
full_name: Wiechmann, Jana
last_name: Wiechmann
- first_name: Fritz
full_name: Seebauer, Fritz
last_name: Seebauer
- first_name: Petra
full_name: Wagner, Petra
last_name: Wagner
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Rautenberg F, Kuhlmann M, Wiechmann J, Seebauer F, Wagner P, Haeb-Umbach R.
On Feature Importance and Interpretability of Speaker Representations. In: ITG
Conference on Speech Communication. ; 2023.'
apa: Rautenberg, F., Kuhlmann, M., Wiechmann, J., Seebauer, F., Wagner, P., &
Haeb-Umbach, R. (2023). On Feature Importance and Interpretability of Speaker
Representations. ITG Conference on Speech Communication. ITG Conference
on Speech Communication, Aachen.
bibtex: '@inproceedings{Rautenberg_Kuhlmann_Wiechmann_Seebauer_Wagner_Haeb-Umbach_2023,
title={On Feature Importance and Interpretability of Speaker Representations},
booktitle={ITG Conference on Speech Communication}, author={Rautenberg, Frederik
and Kuhlmann, Michael and Wiechmann, Jana and Seebauer, Fritz and Wagner, Petra
and Haeb-Umbach, Reinhold}, year={2023} }'
chicago: Rautenberg, Frederik, Michael Kuhlmann, Jana Wiechmann, Fritz Seebauer,
Petra Wagner, and Reinhold Haeb-Umbach. “On Feature Importance and Interpretability
of Speaker Representations.” In ITG Conference on Speech Communication,
2023.
ieee: F. Rautenberg, M. Kuhlmann, J. Wiechmann, F. Seebauer, P. Wagner, and R. Haeb-Umbach,
“On Feature Importance and Interpretability of Speaker Representations,” presented
at the ITG Conference on Speech Communication, Aachen, 2023.
mla: Rautenberg, Frederik, et al. “On Feature Importance and Interpretability of
Speaker Representations.” ITG Conference on Speech Communication, 2023.
short: 'F. Rautenberg, M. Kuhlmann, J. Wiechmann, F. Seebauer, P. Wagner, R. Haeb-Umbach,
in: ITG Conference on Speech Communication, 2023.'
conference:
end_date: 2023-09-22
location: Aachen
name: ITG Conference on Speech Communication
start_date: 2023-09-20
date_created: 2023-10-20T08:04:46Z
date_updated: 2023-11-22T13:44:33Z
ddc:
- '000'
department:
- _id: '54'
- _id: '660'
external_id:
arxiv:
- '2310.12599'
file:
- access_level: closed
content_type: application/pdf
creator: frra
date_created: 2023-10-20T08:20:58Z
date_updated: 2023-10-20T08:20:58Z
file_id: '48359'
file_name: arxiv.pdf
file_size: 272390
relation: main_file
success: 1
file_date_updated: 2023-10-20T08:20:58Z
has_accepted_license: '1'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2310.12599
oa: '1'
project:
- _id: '129'
grant_number: '438445824'
name: 'TRR 318 - C06: TRR 318 - Technisch unterstütztes Erklären von Stimmcharakteristika
(Teilprojekt C06)'
publication: ITG Conference on Speech Communication
status: public
title: On Feature Importance and Interpretability of Speaker Representations
type: conference
user_id: '72602'
year: '2023'
...
---
_id: '48410'
author:
- first_name: Jana
full_name: Wiechmann, Jana
last_name: Wiechmann
- first_name: Frederik
full_name: Rautenberg, Frederik
id: '72602'
last_name: Rautenberg
- first_name: Petra
full_name: Wagner, Petra
last_name: Wagner
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Wiechmann J, Rautenberg F, Wagner P, Haeb-Umbach R. Explaining voice characteristics
to novice voice practitioners-How successful is it? In: 20th International
Congress of the Phonetic Sciences (ICPhS) . ; 2023.'
apa: Wiechmann, J., Rautenberg, F., Wagner, P., & Haeb-Umbach, R. (2023). Explaining
voice characteristics to novice voice practitioners-How successful is it? 20th
International Congress of the Phonetic Sciences (ICPhS) .
bibtex: '@inproceedings{Wiechmann_Rautenberg_Wagner_Haeb-Umbach_2023, title={Explaining
voice characteristics to novice voice practitioners-How successful is it?}, booktitle={20th
International Congress of the Phonetic Sciences (ICPhS) }, author={Wiechmann,
Jana and Rautenberg, Frederik and Wagner, Petra and Haeb-Umbach, Reinhold}, year={2023}
}'
chicago: Wiechmann, Jana, Frederik Rautenberg, Petra Wagner, and Reinhold Haeb-Umbach.
“Explaining Voice Characteristics to Novice Voice Practitioners-How Successful
Is It?” In 20th International Congress of the Phonetic Sciences (ICPhS) ,
2023.
ieee: J. Wiechmann, F. Rautenberg, P. Wagner, and R. Haeb-Umbach, “Explaining voice
characteristics to novice voice practitioners-How successful is it?,” 2023.
mla: Wiechmann, Jana, et al. “Explaining Voice Characteristics to Novice Voice Practitioners-How
Successful Is It?” 20th International Congress of the Phonetic Sciences (ICPhS)
, 2023.
short: 'J. Wiechmann, F. Rautenberg, P. Wagner, R. Haeb-Umbach, in: 20th International
Congress of the Phonetic Sciences (ICPhS) , 2023.'
conference:
end_date: 2023-08-11
start_date: 2023-08-07
date_created: 2023-10-24T08:05:40Z
date_updated: 2023-11-22T13:44:59Z
ddc:
- '040'
department:
- _id: '54'
- _id: '660'
file:
- access_level: closed
content_type: application/pdf
creator: frra
date_created: 2023-10-24T08:03:27Z
date_updated: 2023-10-24T08:03:27Z
file_id: '48413'
file_name: 188.pdf
file_size: 209980
relation: main_file
success: 1
file_date_updated: 2023-10-24T08:03:27Z
has_accepted_license: '1'
language:
- iso: eng
main_file_link:
- open_access: '1'
oa: '1'
project:
- _id: '129'
grant_number: '438445824'
name: 'TRR 318 - C06: TRR 318 - Technisch unterstütztes Erklären von Stimmcharakteristika
(Teilprojekt C06)'
publication: '20th International Congress of the Phonetic Sciences (ICPhS) '
status: public
title: Explaining voice characteristics to novice voice practitioners-How successful
is it?
type: conference
user_id: '72602'
year: '2023'
...
---
_id: '48595'
author:
- first_name: Tobias Martin
full_name: Peters, Tobias Martin
id: '92810'
last_name: Peters
orcid: 0009-0008-5193-6243
- first_name: Roel W.
full_name: Visser, Roel W.
last_name: Visser
citation:
ama: 'Peters TM, Visser RW. The Importance of Distrust in AI. In: Communications
in Computer and Information Science. Springer Nature Switzerland; 2023. doi:10.1007/978-3-031-44070-0_15'
apa: Peters, T. M., & Visser, R. W. (2023). The Importance of Distrust in AI.
Communications in Computer and Information Science. https://doi.org/10.1007/978-3-031-44070-0_15
bibtex: '@inproceedings{Peters_Visser_2023, place={Cham}, title={The Importance
of Distrust in AI}, DOI={10.1007/978-3-031-44070-0_15},
booktitle={Communications in Computer and Information Science}, publisher={Springer
Nature Switzerland}, author={Peters, Tobias Martin and Visser, Roel W.}, year={2023}
}'
chicago: 'Peters, Tobias Martin, and Roel W. Visser. “The Importance of Distrust
in AI.” In Communications in Computer and Information Science. Cham: Springer
Nature Switzerland, 2023. https://doi.org/10.1007/978-3-031-44070-0_15.'
ieee: 'T. M. Peters and R. W. Visser, “The Importance of Distrust in AI,” 2023,
doi: 10.1007/978-3-031-44070-0_15.'
mla: Peters, Tobias Martin, and Roel W. Visser. “The Importance of Distrust in AI.”
Communications in Computer and Information Science, Springer Nature Switzerland,
2023, doi:10.1007/978-3-031-44070-0_15.
short: 'T.M. Peters, R.W. Visser, in: Communications in Computer and Information
Science, Springer Nature Switzerland, Cham, 2023.'
date_created: 2023-11-02T10:04:19Z
date_updated: 2023-11-24T11:59:49Z
department:
- _id: '424'
- _id: '660'
doi: 10.1007/978-3-031-44070-0_15
language:
- iso: eng
place: Cham
project:
- _id: '124'
name: 'TRR 318 - C1: TRR 318 - Subproject C1'
publication: Communications in Computer and Information Science
publication_identifier:
isbn:
- '9783031440694'
- '9783031440700'
issn:
- 1865-0929
- 1865-0937
publication_status: published
publisher: Springer Nature Switzerland
status: public
title: The Importance of Distrust in AI
type: conference
user_id: '92810'
year: '2023'
...
---
_id: '48777'
abstract:
- lang: eng
text: AbstractExplainable artificial intelligence
has mainly focused on static learning scenarios so far. We are interested in dynamic
scenarios where data is sampled progressively, and learning is done in an incremental
rather than a batch mode. We seek efficient incremental algorithms for computing
feature importance (FI). Permutation feature importance (PFI) is a well-established
model-agnostic measure to obtain global FI based on feature marginalization of
absent features. We propose an efficient, model-agnostic algorithm called iPFI
to estimate this measure incrementally and under dynamic modeling conditions including
concept drift. We prove theoretical guarantees on the approximation quality in
terms of expectation and variance. To validate our theoretical findings and the
efficacy of our approaches in incremental scenarios dealing with streaming data
rather than traditional batch settings, we conduct multiple experimental studies
on benchmark data with and without concept drift.
author:
- first_name: Fabian
full_name: Fumagalli, Fabian
last_name: Fumagalli
- first_name: Maximilian
full_name: Muschalik, Maximilian
last_name: Muschalik
- first_name: Eyke
full_name: Hüllermeier, Eyke
last_name: Hüllermeier
- first_name: Barbara
full_name: Hammer, Barbara
last_name: Hammer
citation:
ama: 'Fumagalli F, Muschalik M, Hüllermeier E, Hammer B. Incremental permutation
feature importance (iPFI): towards online explanations on data streams. Machine
Learning. Published online 2023. doi:10.1007/s10994-023-06385-y'
apa: 'Fumagalli, F., Muschalik, M., Hüllermeier, E., & Hammer, B. (2023). Incremental
permutation feature importance (iPFI): towards online explanations on data streams.
Machine Learning. https://doi.org/10.1007/s10994-023-06385-y'
bibtex: '@article{Fumagalli_Muschalik_Hüllermeier_Hammer_2023, title={Incremental
permutation feature importance (iPFI): towards online explanations on data streams},
DOI={10.1007/s10994-023-06385-y},
journal={Machine Learning}, publisher={Springer Science and Business Media LLC},
author={Fumagalli, Fabian and Muschalik, Maximilian and Hüllermeier, Eyke and
Hammer, Barbara}, year={2023} }'
chicago: 'Fumagalli, Fabian, Maximilian Muschalik, Eyke Hüllermeier, and Barbara
Hammer. “Incremental Permutation Feature Importance (IPFI): Towards Online Explanations
on Data Streams.” Machine Learning, 2023. https://doi.org/10.1007/s10994-023-06385-y.'
ieee: 'F. Fumagalli, M. Muschalik, E. Hüllermeier, and B. Hammer, “Incremental permutation
feature importance (iPFI): towards online explanations on data streams,” Machine
Learning, 2023, doi: 10.1007/s10994-023-06385-y.'
mla: 'Fumagalli, Fabian, et al. “Incremental Permutation Feature Importance (IPFI):
Towards Online Explanations on Data Streams.” Machine Learning, Springer
Science and Business Media LLC, 2023, doi:10.1007/s10994-023-06385-y.'
short: F. Fumagalli, M. Muschalik, E. Hüllermeier, B. Hammer, Machine Learning (2023).
date_created: 2023-11-10T14:15:36Z
date_updated: 2023-11-10T14:24:27Z
department:
- _id: '424'
- _id: '660'
doi: 10.1007/s10994-023-06385-y
keyword:
- Artificial Intelligence
- Software
language:
- iso: eng
publication: Machine Learning
publication_identifier:
issn:
- 0885-6125
- 1573-0565
publication_status: published
publisher: Springer Science and Business Media LLC
status: public
title: 'Incremental permutation feature importance (iPFI): towards online explanations
on data streams'
type: journal_article
user_id: '55908'
year: '2023'
...
---
_id: '44853'
author:
- first_name: Suzana
full_name: Alpsancar, Suzana
id: '93637'
last_name: Alpsancar
citation:
ama: 'Alpsancar S. What is AI Ethics? Ethics as means of self-regulation and the
need for critical reflection . In: International Conference on Computer Ethics
2023. Vol 1. ; 2023:1--17.'
apa: Alpsancar, S. (2023). What is AI Ethics? Ethics as means of self-regulation
and the need for critical reflection . International Conference on Computer
Ethics 2023, 1(1), 1--17.
bibtex: '@inproceedings{Alpsancar_2023, title={What is AI Ethics? Ethics as means
of self-regulation and the need for critical reflection }, volume={1}, number={1},
booktitle={International Conference on Computer Ethics 2023}, author={Alpsancar,
Suzana}, year={2023}, pages={1--17} }'
chicago: Alpsancar, Suzana. “What Is AI Ethics? Ethics as Means of Self-Regulation
and the Need for Critical Reflection .” In International Conference on Computer
Ethics 2023, 1:1--17, 2023.
ieee: S. Alpsancar, “What is AI Ethics? Ethics as means of self-regulation and the
need for critical reflection ,” in International Conference on Computer Ethics
2023, Chicago, Illinois, 2023, vol. 1, no. 1, pp. 1--17.
mla: Alpsancar, Suzana. “What Is AI Ethics? Ethics as Means of Self-Regulation and
the Need for Critical Reflection .” International Conference on Computer Ethics
2023, vol. 1, no. 1, 2023, pp. 1--17.
short: 'S. Alpsancar, in: International Conference on Computer Ethics 2023, 2023,
pp. 1--17.'
conference:
end_date: 2023-05-18
location: Chicago, Illinois
name: 'International Conference on Computer Ethics '
start_date: 2023-05-16
date_created: 2023-05-16T00:21:24Z
date_updated: 2023-12-20T13:35:13Z
department:
- _id: '14'
- _id: '660'
intvolume: ' 1'
issue: '1'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://journals.library.iit.edu/index.php/CEPE2023/article/view/227
oa: '1'
page: 1--17
project:
- _id: '370'
grant_number: '438445824'
name: 'TRR 318 - B06: TRR 318 - Ethik und Normativität der erklärbaren KI (Teilprojekt
B06)'
publication: International Conference on Computer Ethics 2023
publication_status: published
quality_controlled: '1'
status: public
title: 'What is AI Ethics? Ethics as means of self-regulation and the need for critical
reflection '
type: conference
user_id: '93637'
volume: 1
year: '2023'
...
---
_id: '51345'
abstract:
- lang: eng
text: The algorithmic imaginary as a theoretical concept has received increasing
attention in recent years as it aims at users’ appropriation of algorithmic processes
operating in opacity. But the concept originally only starts from the users’ point
of view, while the processes on the platforms’ side are largely left out. In contrast,
this paper argues that what is true for users is also valid for algorithmic processes
and the designers behind. On the one hand, the algorithm imagines users’ future
behavior via machine learning, which is supposed to predict all their future actions.
On the other hand, the designers anticipate different actions that could potentially
performed by users with every new implementation of features such as social media
feeds. In order to bring into view this permanently reciprocal interplay coupled
to the imaginary, in which not only the users are involved, I will argue for a
more comprehensive and theoretically precise algorithmic imaginary referring to
the theory of Cornelius Castoriadis. In such a perspective, an important contribution
can be formulated for a theory of social media platforms that goes beyond praxeocentrism
or structural determinism.
author:
- first_name: Christian
full_name: Schulz, Christian
id: '72684'
last_name: Schulz
citation:
ama: Schulz C. A new algorithmic imaginary. Media, Culture & Society.
2023;45(3):646-655. doi:10.1177/01634437221136014
apa: Schulz, C. (2023). A new algorithmic imaginary. Media, Culture & Society,
45(3), 646–655. https://doi.org/10.1177/01634437221136014
bibtex: '@article{Schulz_2023, title={A new algorithmic imaginary}, volume={45},
DOI={10.1177/01634437221136014},
number={3}, journal={Media, Culture & Society}, publisher={SAGE Publications},
author={Schulz, Christian}, year={2023}, pages={646–655} }'
chicago: 'Schulz, Christian. “A New Algorithmic Imaginary.” Media, Culture &
Society 45, no. 3 (2023): 646–55. https://doi.org/10.1177/01634437221136014.'
ieee: 'C. Schulz, “A new algorithmic imaginary,” Media, Culture & Society,
vol. 45, no. 3, pp. 646–655, 2023, doi: 10.1177/01634437221136014.'
mla: Schulz, Christian. “A New Algorithmic Imaginary.” Media, Culture & Society,
vol. 45, no. 3, SAGE Publications, 2023, pp. 646–55, doi:10.1177/01634437221136014.
short: C. Schulz, Media, Culture & Society 45 (2023) 646–655.
date_created: 2024-02-14T09:21:17Z
date_updated: 2024-02-26T08:39:45Z
department:
- _id: '660'
doi: 10.1177/01634437221136014
intvolume: ' 45'
issue: '3'
keyword:
- Sociology and Political Science
- Communication
language:
- iso: eng
page: 646-655
project:
- _id: '122'
name: 'TRR 318 - B3: TRR 318 - Subproject B3'
publication: Media, Culture & Society
publication_identifier:
issn:
- 0163-4437
- 1460-3675
publication_status: published
publisher: SAGE Publications
status: public
title: A new algorithmic imaginary
type: journal_article
user_id: '54779'
volume: 45
year: '2023'
...
---
_id: '51372'
abstract:
- lang: eng
text: Machine learning is frequently used in affective computing, but presents challenges
due the opacity of state-of-the-art machine learning methods. Because of the impact
affective machine learning systems may have on an individual's life, it is important
that models be made transparent to detect and mitigate biased decision making.
In this regard, affective machine learning could benefit from the recent advancements
in explainable artificial intelligence (XAI) research. We perform a structured
literature review to examine the use of interpretability in the context of affective
machine learning. We focus on studies using audio, visual, or audiovisual data
for model training and identified 29 research articles. Our findings show an emergence
of the use of interpretability methods in the last five years. However, their
use is currently limited regarding the range of methods used, the depth of evaluations,
and the consideration of use-cases. We outline the main gaps in the research and
provide recommendations for researchers that aim to implement interpretable methods
for affective machine learning.
author:
- first_name: 'David '
full_name: 'Johnson, David '
last_name: Johnson
- first_name: Olya
full_name: Hakobyan, Olya
last_name: Hakobyan
- first_name: Hanna
full_name: Drimalla, Hanna
last_name: Drimalla
citation:
ama: 'Johnson D, Hakobyan O, Drimalla H. Towards Interpretability in Audio and Visual
Affective Machine Learning: A Review. Published online 2023.'
apa: 'Johnson, D., Hakobyan, O., & Drimalla, H. (2023). Towards Interpretability
in Audio and Visual Affective Machine Learning: A Review.'
bibtex: '@article{Johnson_Hakobyan_Drimalla_2023, title={Towards Interpretability
in Audio and Visual Affective Machine Learning: A Review}, author={Johnson, David and
Hakobyan, Olya and Drimalla, Hanna}, year={2023} }'
chicago: 'Johnson, David , Olya Hakobyan, and Hanna Drimalla. “Towards Interpretability
in Audio and Visual Affective Machine Learning: A Review,” 2023.'
ieee: 'D. Johnson, O. Hakobyan, and H. Drimalla, “Towards Interpretability in Audio
and Visual Affective Machine Learning: A Review.” 2023.'
mla: 'Johnson, David, et al. Towards Interpretability in Audio and Visual Affective
Machine Learning: A Review. 2023.'
short: D. Johnson, O. Hakobyan, H. Drimalla, (2023).
date_created: 2024-02-18T10:52:36Z
date_updated: 2024-02-26T08:43:01Z
department:
- _id: '660'
language:
- iso: eng
project:
- _id: '110'
name: 'TRR 318 - A: TRR 318 - Project Area A'
status: public
title: 'Towards Interpretability in Audio and Visual Affective Machine Learning: A
Review'
type: preprint
user_id: '54779'
year: '2023'
...