---
_id: '63611'
abstract:
- lang: eng
text: When humans interact with artificial intelligence (AI), one desideratum is
appropriate trust. Typically, appropriate trust encompasses that humans trust
AI except for instances in which they either explicitly notice AI errors or are
suspicious that errors could be present. So far, appropriate trust or related
notions have mainly been investigated by assessing trust and reliance. In this
contribution, we argue that these assessments are insufficient to measure the
complex aim of appropriate trust and the related notion of healthy distrust. We
introduce and test the perspective of covert visual attention as an additional
indicator for appropriate trust and draw conceptual connections to the notion
of healthy distrust. To test the validity of our conceptualization, we formalize
visual attention using the Theory of Visual Attention and measure its properties
that are potentially relevant to appropriate trust and healthy distrust in an
image classification task. Based on temporal-order judgment performance, we estimate
participants' attentional capacity and attentional weight toward correct and incorrect
mock-up AI classifications. We observe that misclassifications reduce attentional
capacity compared to correct classifications. However, our results do not indicate
that this reduction is beneficial for a subsequent judgment of the classifications.
The attentional weighting is not affected by the classifications' correctness
but by the difficulty of categorizing the stimuli themselves. We discuss these
results, their implications, and the limited potential for using visual attention
as an indicator of appropriate trust and healthy distrust.
article_number: '1694367'
article_type: original
author:
- first_name: Tobias Martin
full_name: Peters, Tobias Martin
id: '92810'
last_name: Peters
orcid: 0009-0008-5193-6243
- first_name: Kai
full_name: Biermeier, Kai
id: '55908'
last_name: Biermeier
orcid: 0000-0002-2879-2359
- first_name: Ingrid
full_name: Scharlau, Ingrid
id: '451'
last_name: Scharlau
orcid: 0000-0003-2364-9489
citation:
ama: 'Peters TM, Biermeier K, Scharlau I. Assessing healthy distrust in human-AI
interaction: interpreting changes in visual attention. Frontiers in Psychology.
2026;16. doi:10.3389/fpsyg.2025.1694367'
apa: 'Peters, T. M., Biermeier, K., & Scharlau, I. (2026). Assessing healthy
distrust in human-AI interaction: interpreting changes in visual attention. Frontiers
in Psychology, 16, Article 1694367. https://doi.org/10.3389/fpsyg.2025.1694367'
bibtex: '@article{Peters_Biermeier_Scharlau_2026, title={Assessing healthy distrust
in human-AI interaction: interpreting changes in visual attention}, volume={16},
DOI={10.3389/fpsyg.2025.1694367},
number={1694367}, journal={Frontiers in Psychology}, publisher={Frontiers Media
SA}, author={Peters, Tobias Martin and Biermeier, Kai and Scharlau, Ingrid}, year={2026}
}'
chicago: 'Peters, Tobias Martin, Kai Biermeier, and Ingrid Scharlau. “Assessing
Healthy Distrust in Human-AI Interaction: Interpreting Changes in Visual Attention.”
Frontiers in Psychology 16 (2026). https://doi.org/10.3389/fpsyg.2025.1694367.'
ieee: 'T. M. Peters, K. Biermeier, and I. Scharlau, “Assessing healthy distrust
in human-AI interaction: interpreting changes in visual attention,” Frontiers
in Psychology, vol. 16, Art. no. 1694367, 2026, doi: 10.3389/fpsyg.2025.1694367.'
mla: 'Peters, Tobias Martin, et al. “Assessing Healthy Distrust in Human-AI Interaction:
Interpreting Changes in Visual Attention.” Frontiers in Psychology, vol.
16, 1694367, Frontiers Media SA, 2026, doi:10.3389/fpsyg.2025.1694367.'
short: T.M. Peters, K. Biermeier, I. Scharlau, Frontiers in Psychology 16 (2026).
date_created: 2026-01-14T14:21:59Z
date_updated: 2026-01-14T14:29:03Z
department:
- _id: '424'
- _id: '660'
doi: 10.3389/fpsyg.2025.1694367
intvolume: ' 16'
keyword:
- appropriate trust
- healthy distrust
- visual attention
- Theory of Visual Attention
- human-AI interaction
- Bayesian cognitive model
- image classification
language:
- iso: eng
project:
- _id: '124'
name: 'TRR 318 ; TP C01: Gesundes Misstrauen in Erklärungen'
publication: Frontiers in Psychology
publication_identifier:
issn:
- 1664-1078
publication_status: published
publisher: Frontiers Media SA
status: public
title: 'Assessing healthy distrust in human-AI interaction: interpreting changes in
visual attention'
type: journal_article
user_id: '92810'
volume: 16
year: '2026'
...
---
_id: '59756'
abstract:
- lang: eng
text: "A current concern in the field of Artificial Intelligence (AI) is to ensure
the trustworthiness of AI systems. The development of explainability methods is
one prominent way to address this, which has often resulted in the assumption
that the use of explainability will lead to an increase in the trust of users
and wider society. However, the dynamics between explainability and trust are
not well established and empirical investigations of their relation remain mixed
or inconclusive.\r\nIn this paper we provide a detailed description of the concepts
of user trust and distrust in AI and their relation to appropriate reliance. For
that we draw from the fields of machine learning, human–computer interaction,
and the social sciences. Based on these insights, we have created a focused study
of empirical literature of existing empirical studies that investigate the effects
of AI systems and XAI methods on user (dis)trust, in order to substantiate our
conceptualization of trust, distrust, and reliance. With respect to our conceptual
understanding we identify gaps in existing empirical work. With clarifying the
concepts and summarizing the empirical studies, we aim to provide researchers,
who examine user trust in AI, with an improved starting point for developing user
studies to measure and evaluate the user’s attitude towards and reliance on AI
systems."
article_number: '101357'
author:
- first_name: Roel
full_name: Visser, Roel
last_name: Visser
- first_name: Tobias Martin
full_name: Peters, Tobias Martin
id: '92810'
last_name: Peters
orcid: 0009-0008-5193-6243
- first_name: Ingrid
full_name: Scharlau, Ingrid
id: '451'
last_name: Scharlau
orcid: 0000-0003-2364-9489
- first_name: Barbara
full_name: Hammer, Barbara
last_name: Hammer
citation:
ama: 'Visser R, Peters TM, Scharlau I, Hammer B. Trust, distrust, and appropriate
reliance in (X)AI: A conceptual clarification of user trust and survey of its
empirical evaluation. Cognitive Systems Research. Published online 2025.
doi:10.1016/j.cogsys.2025.101357'
apa: 'Visser, R., Peters, T. M., Scharlau, I., & Hammer, B. (2025). Trust, distrust,
and appropriate reliance in (X)AI: A conceptual clarification of user trust and
survey of its empirical evaluation. Cognitive Systems Research, Article
101357. https://doi.org/10.1016/j.cogsys.2025.101357'
bibtex: '@article{Visser_Peters_Scharlau_Hammer_2025, title={Trust, distrust, and
appropriate reliance in (X)AI: A conceptual clarification of user trust and survey
of its empirical evaluation}, DOI={10.1016/j.cogsys.2025.101357},
number={101357}, journal={Cognitive Systems Research}, publisher={Elsevier BV},
author={Visser, Roel and Peters, Tobias Martin and Scharlau, Ingrid and Hammer,
Barbara}, year={2025} }'
chicago: 'Visser, Roel, Tobias Martin Peters, Ingrid Scharlau, and Barbara Hammer.
“Trust, Distrust, and Appropriate Reliance in (X)AI: A Conceptual Clarification
of User Trust and Survey of Its Empirical Evaluation.” Cognitive Systems Research,
2025. https://doi.org/10.1016/j.cogsys.2025.101357.'
ieee: 'R. Visser, T. M. Peters, I. Scharlau, and B. Hammer, “Trust, distrust, and
appropriate reliance in (X)AI: A conceptual clarification of user trust and survey
of its empirical evaluation,” Cognitive Systems Research, Art. no. 101357,
2025, doi: 10.1016/j.cogsys.2025.101357.'
mla: 'Visser, Roel, et al. “Trust, Distrust, and Appropriate Reliance in (X)AI:
A Conceptual Clarification of User Trust and Survey of Its Empirical Evaluation.”
Cognitive Systems Research, 101357, Elsevier BV, 2025, doi:10.1016/j.cogsys.2025.101357.'
short: R. Visser, T.M. Peters, I. Scharlau, B. Hammer, Cognitive Systems Research
(2025).
date_created: 2025-05-02T09:26:15Z
date_updated: 2025-05-15T11:16:27Z
department:
- _id: '424'
- _id: '660'
doi: 10.1016/j.cogsys.2025.101357
keyword:
- XAI
- Appropriate trust
- Distrust
- Reliance
- Human-centric evaluation
- Trustworthy AI
language:
- iso: eng
project:
- _id: '124'
name: 'TRR 318 - C1: TRR 318 - Subproject C1 - Gesundes Misstrauen in Erklärungen'
publication: Cognitive Systems Research
publication_identifier:
issn:
- 1389-0417
publication_status: published
publisher: Elsevier BV
status: public
title: 'Trust, distrust, and appropriate reliance in (X)AI: A conceptual clarification
of user trust and survey of its empirical evaluation'
type: journal_article
user_id: '92810'
year: '2025'
...
---
_id: '59755'
abstract:
- lang: eng
text: "Due to the application of Artificial Intelligence (AI) in high-risk domains
like law or medicine,\r\ntrustworthy AI and trust in AI are of increasing scientific
and public relevance. A typical conception,\r\nfor example in the context of medical
diagnosis, is that a knowledgeable user receives AIgenerated\r\nclassification
as advice. Research to improve such interactions often aims to foster the\r\nuser’s
trust, which in turn should improve the combined human-AI performance. Given that
AI\r\nmodels can err, we argue that the possibility to critically review, thus
to distrust, an AI decision is\r\nan equally interesting target of research.\r\nWe
created two image classification scenarios in which the participants received
mock-up\r\nAI advice. The quality of the advice decreases for a phase of the experiment.
We studied the\r\ntask performance, trust and distrust of the participants, and
tested whether an instruction to\r\nremain skeptical and review each piece of
advice led to a better performance compared to a\r\nneutral condition. Our results
indicate that this instruction does not improve but rather worsens\r\nthe participants’
performance. Repeated single-item self-report of trust and distrust shows an\r\nincrease
in trust and a decrease in distrust after the drop in the AI’s classification
quality, with no\r\ndifference between the two instructions. Furthermore, via
a Bayesian Signal Detection Theory\r\nanalysis, we provide a procedure to assess
appropriate reliance in detail, by quantifying whether\r\nthe problems of under-
and over-reliance have been mitigated. We discuss implications of our\r\nresults
for the usage of disclaimers before interacting with AI, as prominently used in
current\r\nLLM-based chatbots, and for trust and distrust research."
article_type: original
author:
- first_name: Tobias Martin
full_name: Peters, Tobias Martin
id: '92810'
last_name: Peters
orcid: 0009-0008-5193-6243
- first_name: Ingrid
full_name: Scharlau, Ingrid
id: '451'
last_name: Scharlau
orcid: 0000-0003-2364-9489
citation:
ama: 'Peters TM, Scharlau I. Interacting with fallible AI: Is distrust helpful when
receiving AI misclassifications? Frontiers in Psychology. 2025;16. doi:10.3389/fpsyg.2025.1574809'
apa: 'Peters, T. M., & Scharlau, I. (2025). Interacting with fallible AI: Is
distrust helpful when receiving AI misclassifications? Frontiers in Psychology,
16. https://doi.org/10.3389/fpsyg.2025.1574809'
bibtex: '@article{Peters_Scharlau_2025, title={Interacting with fallible AI: Is
distrust helpful when receiving AI misclassifications?}, volume={16}, DOI={10.3389/fpsyg.2025.1574809},
journal={Frontiers in Psychology}, author={Peters, Tobias Martin and Scharlau,
Ingrid}, year={2025} }'
chicago: 'Peters, Tobias Martin, and Ingrid Scharlau. “Interacting with Fallible
AI: Is Distrust Helpful When Receiving AI Misclassifications?” Frontiers in
Psychology 16 (2025). https://doi.org/10.3389/fpsyg.2025.1574809.'
ieee: 'T. M. Peters and I. Scharlau, “Interacting with fallible AI: Is distrust
helpful when receiving AI misclassifications?,” Frontiers in Psychology,
vol. 16, 2025, doi: 10.3389/fpsyg.2025.1574809.'
mla: 'Peters, Tobias Martin, and Ingrid Scharlau. “Interacting with Fallible AI:
Is Distrust Helpful When Receiving AI Misclassifications?” Frontiers in Psychology,
vol. 16, 2025, doi:10.3389/fpsyg.2025.1574809.'
short: T.M. Peters, I. Scharlau, Frontiers in Psychology 16 (2025).
date_created: 2025-05-02T09:22:39Z
date_updated: 2025-05-27T09:10:09Z
department:
- _id: '424'
- _id: '660'
doi: 10.3389/fpsyg.2025.1574809
intvolume: ' 16'
keyword:
- trust in AI
- trust
- distrust
- human-AI interaction
- Signal Detection Theory
- Bayesian parameter estimation
- image classification
language:
- iso: eng
project:
- _id: '124'
name: 'TRR 318 - C1: TRR 318 - Subproject C1 - Gesundes Misstrauen in Erklärungen'
publication: Frontiers in Psychology
publication_status: published
status: public
title: 'Interacting with fallible AI: Is distrust helpful when receiving AI misclassifications?'
type: journal_article
user_id: '92810'
volume: 16
year: '2025'
...
---
_id: '58650'
abstract:
- lang: eng
text: 'Technical systems are characterized by increasing interdisciplinarity, complexity
and networking. A product and its corresponding production systems require interdisciplinary
multi-objective optimization. Sustainability and recyclability demands increase
said complexity. The efficiency of previously established engineering methods
is reaching its limits, which can only be overcome by systematic integration of
extreme data. The aim of "hybrid decision support" is as follows: Data science
and artificial intelligence should be used to supplement human capabilities in
conjunction with existing heuristics, methods, modeling and simulation to increase
the efficiency of product creation.'
alternative_title:
- Hybride Entscheidungsunterstützung in der Produktentstehung - Mit Data Science und
Künstlicher Intelligenz die Leistungsfähigkeit erhöhen
article_type: original
author:
- first_name: Iris
full_name: Gräßler, Iris
id: '47565'
last_name: Gräßler
orcid: 0000-0001-5765-971X
- first_name: Jens
full_name: Pottebaum, Jens
id: '405'
last_name: Pottebaum
orcid: http://orcid.org/0000-0001-8778-2989
- first_name: Peter
full_name: Nyhuis, Peter
last_name: Nyhuis
- first_name: Rainer
full_name: Stark, Rainer
last_name: Stark
- first_name: Klaus-Dieter
full_name: Thoben, Klaus-Dieter
last_name: Thoben
- first_name: Petra
full_name: Wiederkehr, Petra
last_name: Wiederkehr
citation:
ama: Gräßler I, Pottebaum J, Nyhuis P, Stark R, Thoben K-D, Wiederkehr P. Hybrid
Decision Support in Product Creation - Improving performance with data science
and artificial intelligence. Industry 40 Science. 2025;2025(1). doi:10.30844/i4sd.25.1.18
apa: Gräßler, I., Pottebaum, J., Nyhuis, P., Stark, R., Thoben, K.-D., & Wiederkehr,
P. (2025). Hybrid Decision Support in Product Creation - Improving performance
with data science and artificial intelligence. Industry 4.0 Science, 2025(1).
https://doi.org/10.30844/i4sd.25.1.18
bibtex: '@article{Gräßler_Pottebaum_Nyhuis_Stark_Thoben_Wiederkehr_2025, title={Hybrid
Decision Support in Product Creation - Improving performance with data science
and artificial intelligence}, volume={2025}, DOI={10.30844/i4sd.25.1.18},
number={1}, journal={Industry 4.0 Science}, publisher={GITO mbH Verlag}, author={Gräßler,
Iris and Pottebaum, Jens and Nyhuis, Peter and Stark, Rainer and Thoben, Klaus-Dieter
and Wiederkehr, Petra}, year={2025} }'
chicago: Gräßler, Iris, Jens Pottebaum, Peter Nyhuis, Rainer Stark, Klaus-Dieter
Thoben, and Petra Wiederkehr. “Hybrid Decision Support in Product Creation - Improving
Performance with Data Science and Artificial Intelligence.” Industry 4.0 Science
2025, no. 1 (2025). https://doi.org/10.30844/i4sd.25.1.18.
ieee: 'I. Gräßler, J. Pottebaum, P. Nyhuis, R. Stark, K.-D. Thoben, and P. Wiederkehr,
“Hybrid Decision Support in Product Creation - Improving performance with data
science and artificial intelligence,” Industry 4.0 Science, vol. 2025,
no. 1, 2025, doi: 10.30844/i4sd.25.1.18.'
mla: Gräßler, Iris, et al. “Hybrid Decision Support in Product Creation - Improving
Performance with Data Science and Artificial Intelligence.” Industry 4.0 Science,
vol. 2025, no. 1, GITO mbH Verlag, 2025, doi:10.30844/i4sd.25.1.18.
short: I. Gräßler, J. Pottebaum, P. Nyhuis, R. Stark, K.-D. Thoben, P. Wiederkehr,
Industry 4.0 Science 2025 (2025).
date_created: 2025-02-15T09:31:30Z
date_updated: 2025-02-15T09:40:52Z
department:
- _id: '152'
doi: 10.30844/i4sd.25.1.18
intvolume: ' 2025'
issue: '1'
keyword:
- AI
- artificial intelligence
- Data Science
- decision support
- extreme data
- Künstliche Intelligenz
- product creation
- product development
language:
- iso: eng
main_file_link:
- open_access: '1'
oa: '1'
publication: Industry 4.0 Science
publication_identifier:
issn:
- 2942-6170
publication_status: published
publisher: GITO mbH Verlag
quality_controlled: '1'
status: public
title: Hybrid Decision Support in Product Creation - Improving performance with data
science and artificial intelligence
type: journal_article
user_id: '405'
volume: 2025
year: '2025'
...
---
_id: '61410'
abstract:
- lang: eng
text: "Purpose: The purpose of this study is to identify, analyze, and explain the
implications that could\r\narise for service settings if AI systems develop, or
are perceived to develop, consciousness – the\r\nability to acknowledge their
own existence and the capacity for positive or negative experiences.\r\n\r\nDesign/methodology/approach:
This study proposes and explores four hypothetical scenarios in\r\nwhich conscious
AI in service could manifest. We contextualize our resulting typology in the\r\nhealth
service context and integrate extant literature on technology-enabled service,
AI\r\nconsciousness, and AI ethics into the narrative.\r\n\r\nFindings: This study
provides a unique theoretical contribution to service research in the form of\r\na
Type IV theory. It enables future service researchers to apprehend, explain, and
predict how\r\nfunctionally conscious AI in service might unfold.\r\n\r\nOriginality:
An increasingly prolific public discourse acknowledges that conscious AI systems\r\nmay
emerge. Against this backdrop, this study aims to systematically explore a question
that is\r\nperhaps the most critical and timely, but also inherently speculative,
in relation to AI in service\r\nresearch by introducing much-needed theory and
terminology.\r\n\r\nPractical implications: The ethical use of conscious AI in
service could emerge as a distinct\r\ncompetitive advantage in the future. Achieving
this outcome involves speculative yet actionable\r\nrecommendations that include
training, guiding, and controlling how humans engage with such\r\nsystems, developing
appropriate wellbeing protocols for functionally conscious AI systems, and\r\nestablishing
AI rights and governance frameworks."
article_type: original
author:
- first_name: Christoph
full_name: Breidbach, Christoph
last_name: Breidbach
- first_name: Casper Ferm
full_name: Lars-Erik, Casper Ferm
last_name: Lars-Erik
- first_name: Paul
full_name: Maglio, Paul
last_name: Maglio
- first_name: Daniel
full_name: Beverungen, Daniel
id: '59677'
last_name: Beverungen
- first_name: Jochen
full_name: Wirtz, Jochen
last_name: Wirtz
- first_name: Alex
full_name: Twigg, Alex
last_name: Twigg
citation:
ama: Breidbach C, Lars-Erik CF, Maglio P, Beverungen D, Wirtz J, Twigg A. Conscious
Artificial Intelligence in Service. Journal of Service Management.
apa: Breidbach, C., Lars-Erik, C. F., Maglio, P., Beverungen, D., Wirtz, J., &
Twigg, A. (n.d.). Conscious Artificial Intelligence in Service. Journal of
Service Management.
bibtex: '@article{Breidbach_Lars-Erik_Maglio_Beverungen_Wirtz_Twigg, title={Conscious
Artificial Intelligence in Service}, journal={Journal of Service Management},
publisher={Emerald}, author={Breidbach, Christoph and Lars-Erik, Casper Ferm and
Maglio, Paul and Beverungen, Daniel and Wirtz, Jochen and Twigg, Alex} }'
chicago: Breidbach, Christoph, Casper Ferm Lars-Erik, Paul Maglio, Daniel Beverungen,
Jochen Wirtz, and Alex Twigg. “Conscious Artificial Intelligence in Service.”
Journal of Service Management, n.d.
ieee: C. Breidbach, C. F. Lars-Erik, P. Maglio, D. Beverungen, J. Wirtz, and A.
Twigg, “Conscious Artificial Intelligence in Service,” Journal of Service Management.
mla: Breidbach, Christoph, et al. “Conscious Artificial Intelligence in Service.”
Journal of Service Management, Emerald.
short: C. Breidbach, C.F. Lars-Erik, P. Maglio, D. Beverungen, J. Wirtz, A. Twigg,
Journal of Service Management (n.d.).
date_created: 2025-09-23T11:47:47Z
date_updated: 2025-11-10T10:22:59Z
ddc:
- '380'
department:
- _id: '195'
file:
- access_level: closed
content_type: application/pdf
creator: dabe
date_created: 2025-11-10T10:20:48Z
date_updated: 2025-11-10T10:20:48Z
file_id: '62150'
file_name: Breidbach et al, 2025_Conscious AI in Service_w link.pdf
file_size: 743479
relation: main_file
success: 1
file_date_updated: 2025-11-10T10:20:48Z
has_accepted_license: '1'
keyword:
- AI
- AI consciousness
- AI ethics
- service systems
language:
- iso: eng
publication: Journal of Service Management
publication_status: inpress
publisher: Emerald
quality_controlled: '1'
status: public
title: Conscious Artificial Intelligence in Service
type: journal_article
user_id: '59677'
year: '2025'
...
---
_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: '63019'
author:
- first_name: Johannes Aurelius Tamino
full_name: Donner, Johannes Aurelius Tamino
id: '72054'
last_name: Donner
orcid: 0009-0007-4757-4393
- first_name: Alexander
full_name: Schlüter, Alexander
id: '103302'
last_name: Schlüter
orcid: 0000-0002-2569-1624
citation:
ama: 'Donner JAT, Schlüter A. Development of an AI-driven decentralized control
for fifth generation district heating and cooling networks. In: SDEWES Conference
2025. ; 2025.'
apa: Donner, J. A. T., & Schlüter, A. (2025). Development of an AI-driven decentralized
control for fifth generation district heating and cooling networks. SDEWES
Conference 2025. 20th SDEWES Conference, Dubrovnik.
bibtex: '@inproceedings{Donner_Schlüter_2025, title={Development of an AI-driven
decentralized control for fifth generation district heating and cooling networks},
booktitle={SDEWES Conference 2025}, author={Donner, Johannes Aurelius Tamino and
Schlüter, Alexander}, year={2025} }'
chicago: Donner, Johannes Aurelius Tamino, and Alexander Schlüter. “Development
of an AI-Driven Decentralized Control for Fifth Generation District Heating and
Cooling Networks.” In SDEWES Conference 2025, 2025.
ieee: J. A. T. Donner and A. Schlüter, “Development of an AI-driven decentralized
control for fifth generation district heating and cooling networks,” presented
at the 20th SDEWES Conference, Dubrovnik, 2025.
mla: Donner, Johannes Aurelius Tamino, and Alexander Schlüter. “Development of an
AI-Driven Decentralized Control for Fifth Generation District Heating and Cooling
Networks.” SDEWES Conference 2025, 2025.
short: 'J.A.T. Donner, A. Schlüter, in: SDEWES Conference 2025, 2025.'
conference:
end_date: 10.10.2025
location: Dubrovnik
name: 20th SDEWES Conference
start_date: 05.10.2025
date_created: 2025-12-10T12:30:59Z
date_updated: 2026-01-06T07:53:40Z
department:
- _id: '876'
- _id: '321'
- _id: '9'
- _id: '393'
keyword:
- 5GDHC
- district heating
- DHC
- waste heat
- AI-Driven
language:
- iso: eng
publication: SDEWES Conference 2025
status: public
title: Development of an AI-driven decentralized control for fifth generation district
heating and cooling networks
type: conference_abstract
user_id: '103302'
year: '2025'
...
---
_id: '56166'
abstract:
- lang: eng
text: Developing Intelligent Technical Systems (ITS) involves a complex process
encompassing planning, analysis, design, production, and maintenance. Model-Based
Systems Engineering (MBSE) is a key methodology for systematic systems engineering.
Designing models for ITS requires harmonious interaction of various elements,
posing a challenge in MBSE. Leveraging Generative Artificial Intelligence, we
generated a dataset for modeling, using prompt engineering on large language models.
The generated artifacts can aid engineers in MBSE design or serve as synthetic
training data for AI assistants.
author:
- first_name: Pranav Jayant
full_name: Kulkarni, Pranav Jayant
id: '86782'
last_name: Kulkarni
- first_name: Denis
full_name: Tissen, Denis
id: '44458'
last_name: Tissen
- first_name: Ruslan
full_name: Bernijazov, Ruslan
id: '36312'
last_name: Bernijazov
- first_name: Roman
full_name: Dumitrescu, Roman
id: '16190'
last_name: Dumitrescu
citation:
ama: 'Kulkarni PJ, Tissen D, Bernijazov R, Dumitrescu R. Towards Automated Design:
Automatically Generating Modeling Elements with Prompt Engineering and Generative
Artificial Intelligence. In: Malmqvist J, Candi M, Saemundsson R, Bystrom F, Isaksson
O, eds. DS 130: Proceedings of NordDesign 2024. ; 2024:617-625. doi:10.35199/NORDDESIGN2024.66'
apa: 'Kulkarni, P. J., Tissen, D., Bernijazov, R., & Dumitrescu, R. (2024).
Towards Automated Design: Automatically Generating Modeling Elements with Prompt
Engineering and Generative Artificial Intelligence. In J. Malmqvist, M. Candi,
R. Saemundsson, F. Bystrom, & O. Isaksson (Eds.), DS 130: Proceedings of
NordDesign 2024 (pp. 617–625). https://doi.org/10.35199/NORDDESIGN2024.66'
bibtex: '@inproceedings{Kulkarni_Tissen_Bernijazov_Dumitrescu_2024, title={Towards
Automated Design: Automatically Generating Modeling Elements with Prompt Engineering
and Generative Artificial Intelligence}, DOI={10.35199/NORDDESIGN2024.66},
booktitle={DS 130: Proceedings of NordDesign 2024}, author={Kulkarni, Pranav Jayant
and Tissen, Denis and Bernijazov, Ruslan and Dumitrescu, Roman}, editor={Malmqvist,
J. and Candi, M. and Saemundsson, R. and Bystrom, F. and Isaksson, O.}, year={2024},
pages={617–625} }'
chicago: 'Kulkarni, Pranav Jayant, Denis Tissen, Ruslan Bernijazov, and Roman Dumitrescu.
“Towards Automated Design: Automatically Generating Modeling Elements with Prompt
Engineering and Generative Artificial Intelligence.” In DS 130: Proceedings
of NordDesign 2024, edited by J. Malmqvist, M. Candi, R. Saemundsson, F. Bystrom,
and O. Isaksson, 617–25, 2024. https://doi.org/10.35199/NORDDESIGN2024.66.'
ieee: 'P. J. Kulkarni, D. Tissen, R. Bernijazov, and R. Dumitrescu, “Towards Automated
Design: Automatically Generating Modeling Elements with Prompt Engineering and
Generative Artificial Intelligence,” in DS 130: Proceedings of NordDesign 2024,
Reykjavik, 2024, pp. 617–625, doi: 10.35199/NORDDESIGN2024.66.'
mla: 'Kulkarni, Pranav Jayant, et al. “Towards Automated Design: Automatically Generating
Modeling Elements with Prompt Engineering and Generative Artificial Intelligence.”
DS 130: Proceedings of NordDesign 2024, edited by J. Malmqvist et al.,
2024, pp. 617–25, doi:10.35199/NORDDESIGN2024.66.'
short: 'P.J. Kulkarni, D. Tissen, R. Bernijazov, R. Dumitrescu, in: J. Malmqvist,
M. Candi, R. Saemundsson, F. Bystrom, O. Isaksson (Eds.), DS 130: Proceedings
of NordDesign 2024, 2024, pp. 617–625.'
conference:
end_date: 2024-08-14
location: Reykjavik
name: NordDesign Conference 2024
start_date: 2024-08-12
date_created: 2024-09-17T09:56:43Z
date_updated: 2024-09-17T09:57:07Z
doi: 10.35199/NORDDESIGN2024.66
editor:
- first_name: J.
full_name: Malmqvist, J.
last_name: Malmqvist
- first_name: M.
full_name: Candi, M.
last_name: Candi
- first_name: R.
full_name: Saemundsson, R.
last_name: Saemundsson
- first_name: F.
full_name: Bystrom, F.
last_name: Bystrom
- first_name: O.
full_name: Isaksson, O.
last_name: Isaksson
keyword:
- Data Driven Design
- Design Automation
- Systems Engineering (SE)
- Artificial Intelligence (AI)
language:
- iso: eng
page: 617-625
publication: 'DS 130: Proceedings of NordDesign 2024'
publication_identifier:
unknown:
- 978-1-912254-21-7
publication_status: epub_ahead
related_material:
link:
- relation: confirmation
url: https://www.designsociety.org/publication/47658/Towards+Automated+Design%3A+Automatically+Generating+Modeling+Elements+with+Prompt+Engineering+and+Generative+Artificial+Intelligence
status: public
title: 'Towards Automated Design: Automatically Generating Modeling Elements with
Prompt Engineering and Generative Artificial Intelligence'
type: conference
user_id: '86782'
year: '2024'
...
---
_id: '56277'
abstract:
- lang: eng
text: What is learner-sensitive feedback to argumentative learner texts when it
is to be issued computer- based? Learning stages are difficult to quantify. The
paper provides insight into the history of research since the 1980s and a preview
of what this automated feedback might look like. These questions are embedded
in a research project at the Universities of Paderborn and Hannover, Germany,
from which a software (project name ArgSchool) emerges that will provide such
feedback.
author:
- first_name: Sebastian
full_name: Kilsbach, Sebastian
id: '93839'
last_name: Kilsbach
- first_name: Nadine
full_name: Michel, Nadine
id: '47857'
last_name: Michel
citation:
ama: 'Kilsbach S, Michel N. Computer-Based Generation of Learner-Sensitive Feedback
to Argumentative Learner Texts. In: Proceedings of the Tenth Conference of
the International Society for the Study of Argumentation. ; 2024.'
apa: Kilsbach, S., & Michel, N. (2024). Computer-Based Generation of Learner-Sensitive
Feedback to Argumentative Learner Texts. Proceedings of the Tenth Conference
of the International Society for the Study of Argumentation. Tenth Conference
of the International Society for the Study of Argumentation, Leiden.
bibtex: '@inproceedings{Kilsbach_Michel_2024, title={Computer-Based Generation of
Learner-Sensitive Feedback to Argumentative Learner Texts}, booktitle={Proceedings
of the Tenth Conference of the International Society for the Study of Argumentation},
author={Kilsbach, Sebastian and Michel, Nadine}, year={2024} }'
chicago: Kilsbach, Sebastian, and Nadine Michel. “Computer-Based Generation of Learner-Sensitive
Feedback to Argumentative Learner Texts.” In Proceedings of the Tenth Conference
of the International Society for the Study of Argumentation, 2024.
ieee: S. Kilsbach and N. Michel, “Computer-Based Generation of Learner-Sensitive
Feedback to Argumentative Learner Texts,” presented at the Tenth Conference of
the International Society for the Study of Argumentation, Leiden, 2024.
mla: Kilsbach, Sebastian, and Nadine Michel. “Computer-Based Generation of Learner-Sensitive
Feedback to Argumentative Learner Texts.” Proceedings of the Tenth Conference
of the International Society for the Study of Argumentation, 2024.
short: 'S. Kilsbach, N. Michel, in: Proceedings of the Tenth Conference of the International
Society for the Study of Argumentation, 2024.'
conference:
end_date: 2023-07-07
location: Leiden
name: Tenth Conference of the International Society for the Study of Argumentation
start_date: 2023-07-04
date_created: 2024-09-30T09:24:12Z
date_updated: 2024-09-30T09:25:14Z
keyword:
- AI
- argumentation mining
- discourse history
- (automated
- learner-sensitive) feedback
language:
- iso: eng
publication: Proceedings of the Tenth Conference of the International Society for
the Study of Argumentation
status: public
title: Computer-Based Generation of Learner-Sensitive Feedback to Argumentative Learner
Texts
type: conference
user_id: '47857'
year: '2024'
...
---
_id: '56282'
abstract:
- lang: eng
text: "Algorithmic bias has long been recognized as a key problem affecting decision-making
processes that integrate artificial intelligence (AI) technologies. The increased
use of AI in making military decisions relevant to the use of force has sustained
such questions about biases in these technologies and in how human users programme
with and rely on data based on hierarchized socio-cultural norms, knowledges,
and modes of attention.\r\n\r\nIn this post, Dr Ingvild Bode, Professor at the
Center for War Studies, University of Southern Denmark, and Ishmael Bhila, PhD
researcher at the “Meaningful Human Control: Between Regulation and Reflexion”
project, Paderborn University, unpack the problem of algorithmic bias with reference
to AI-based decision support systems (AI DSS). They examine three categories of
algorithmic bias – preexisting bias, technical bias, and emergent bias – across
four lifecycle stages of an AI DSS, concluding that stakeholders in the ongoing
discussion about AI in the military domain should consider the impact of algorithmic
bias on AI DSS more seriously."
author:
- first_name: Ishmael
full_name: Bhila, Ishmael
id: '105772'
last_name: Bhila
- first_name: Ingvild
full_name: Bode, Ingvild
last_name: Bode
citation:
ama: Bhila I, Bode I. The Problem of Algorithmic Bias in AI-Based Military Decision
Support Systems. ICRC Humanitarian Law & Policy Blog; 2024.
apa: Bhila, I., & Bode, I. (2024). The problem of algorithmic bias in AI-based
military decision support systems. ICRC Humanitarian Law & Policy Blog.
bibtex: '@book{Bhila_Bode_2024, title={The problem of algorithmic bias in AI-based
military decision support systems}, publisher={ICRC Humanitarian Law & Policy
Blog}, author={Bhila, Ishmael and Bode, Ingvild}, year={2024} }'
chicago: Bhila, Ishmael, and Ingvild Bode. The Problem of Algorithmic Bias in
AI-Based Military Decision Support Systems. ICRC Humanitarian Law & Policy
Blog, 2024.
ieee: I. Bhila and I. Bode, The problem of algorithmic bias in AI-based military
decision support systems. ICRC Humanitarian Law & Policy Blog, 2024.
mla: Bhila, Ishmael, and Ingvild Bode. The Problem of Algorithmic Bias in AI-Based
Military Decision Support Systems. ICRC Humanitarian Law & Policy Blog,
2024.
short: I. Bhila, I. Bode, The Problem of Algorithmic Bias in AI-Based Military Decision
Support Systems, ICRC Humanitarian Law & Policy Blog, 2024.
date_created: 2024-09-30T11:44:28Z
date_updated: 2024-11-26T09:49:48Z
has_accepted_license: '1'
keyword:
- Algorithmic Bias
- AI
- Decision Support Systems
- Autonomous Weapons Systems
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://blogs.icrc.org/law-and-policy/2024/09/03/the-problem-of-algorithmic-bias-in-ai-based-military-decision-support-systems/
oa: '1'
publication_status: published
publisher: ICRC Humanitarian Law & Policy Blog
related_material:
link:
- relation: confirmation
url: https://blogs.icrc.org/law-and-policy/2024/09/03/the-problem-of-algorithmic-bias-in-ai-based-military-decision-support-systems/
status: public
title: The problem of algorithmic bias in AI-based military decision support systems
type: misc
user_id: '105772'
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: '33490'
abstract:
- lang: eng
text: Algorithmic fairness in Information Systems (IS) is a concept that aims to
mitigate systematic discrimination and bias in automated decision-making. However,
previous research argued that different fairness criteria are often incompatible.
In hiring, AI is used to assess and rank applicants according to their fit for
vacant positions. However, various types of bias also exist for AI-based algorithms
(e.g., using biased historical data). To reduce AI’s bias and thereby unfair treatment,
we conducted a systematic literature review to identify suitable strategies for
the context of hiring. We identified nine fundamental articles in this context
and extracted four types of approaches to address unfairness in AI, namely pre-process,
in-process, post-process, and feature selection. Based on our findings, we (a)
derived a research agenda for future studies and (b) proposed strategies for practitioners
who design and develop AIs for hiring purposes.
author:
- first_name: Jonas
full_name: Rieskamp, Jonas
id: '77643'
last_name: Rieskamp
- first_name: Lennart
full_name: Hofeditz, Lennart
last_name: Hofeditz
- first_name: Milad
full_name: Mirbabaie, Milad
id: '88691'
last_name: Mirbabaie
- first_name: Stefan
full_name: Stieglitz, Stefan
last_name: Stieglitz
citation:
ama: 'Rieskamp J, Hofeditz L, Mirbabaie M, Stieglitz S. Approaches to Improve Fairness
when Deploying AI-based Algorithms in Hiring – Using a Systematic Literature Review
to Guide Future Research. In: Proceedings of the Annual Hawaii International
Conference on System Sciences (HICSS). ; 2023.'
apa: Rieskamp, J., Hofeditz, L., Mirbabaie, M., & Stieglitz, S. (2023). Approaches
to Improve Fairness when Deploying AI-based Algorithms in Hiring – Using a Systematic
Literature Review to Guide Future Research. Proceedings of the Annual Hawaii
International Conference on System Sciences (HICSS). Proceedings of the Annual
Hawaii International Conference on System Sciences (HICSS).
bibtex: '@inproceedings{Rieskamp_Hofeditz_Mirbabaie_Stieglitz_2023, title={Approaches
to Improve Fairness when Deploying AI-based Algorithms in Hiring – Using a Systematic
Literature Review to Guide Future Research}, booktitle={Proceedings of the Annual
Hawaii International Conference on System Sciences (HICSS)}, author={Rieskamp,
Jonas and Hofeditz, Lennart and Mirbabaie, Milad and Stieglitz, Stefan}, year={2023}
}'
chicago: Rieskamp, Jonas, Lennart Hofeditz, Milad Mirbabaie, and Stefan Stieglitz.
“Approaches to Improve Fairness When Deploying AI-Based Algorithms in Hiring –
Using a Systematic Literature Review to Guide Future Research.” In Proceedings
of the Annual Hawaii International Conference on System Sciences (HICSS),
2023.
ieee: J. Rieskamp, L. Hofeditz, M. Mirbabaie, and S. Stieglitz, “Approaches to Improve
Fairness when Deploying AI-based Algorithms in Hiring – Using a Systematic Literature
Review to Guide Future Research,” presented at the Proceedings of the Annual Hawaii
International Conference on System Sciences (HICSS), 2023.
mla: Rieskamp, Jonas, et al. “Approaches to Improve Fairness When Deploying AI-Based
Algorithms in Hiring – Using a Systematic Literature Review to Guide Future Research.”
Proceedings of the Annual Hawaii International Conference on System Sciences
(HICSS), 2023.
short: 'J. Rieskamp, L. Hofeditz, M. Mirbabaie, S. Stieglitz, in: Proceedings of
the Annual Hawaii International Conference on System Sciences (HICSS), 2023.'
conference:
end_date: 2023-01-06
name: Proceedings of the Annual Hawaii International Conference on System Sciences
(HICSS)
start_date: 2023-01-03
date_created: 2022-09-27T12:39:12Z
date_updated: 2023-02-06T14:39:51Z
keyword:
- fairness in AI
- SLR
- hiring
- AI implementation
- AI-based algorithms
language:
- iso: eng
main_file_link:
- url: https://hdl.handle.net/10125/102654
publication: Proceedings of the Annual Hawaii International Conference on System Sciences
(HICSS)
status: public
title: Approaches to Improve Fairness when Deploying AI-based Algorithms in Hiring
– Using a Systematic Literature Review to Guide Future Research
type: conference
user_id: '77643'
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: '29220'
abstract:
- lang: eng
text: "Modern services often comprise several components, such as chained virtual
network functions, microservices, or\r\nmachine learning functions. Providing
such services requires to decide how often to instantiate each component, where
to place these instances in the network, how to chain them and route traffic through
them. \r\nTo overcome limitations of conventional, hardwired heuristics, deep
reinforcement learning (DRL) approaches for self-learning network and service
management have emerged recently. These model-free DRL approaches are more flexible
but typically learn tabula rasa, i.e., disregard existing understanding of networks,
services, and their coordination. \r\n\r\nInstead, we propose FutureCoord, a novel
model-based AI approach that leverages existing understanding of networks and
services for more efficient and effective coordination without time-intensive
training. FutureCoord combines Monte Carlo Tree Search with a stochastic traffic
model. This allows FutureCoord to estimate the impact of future incoming traffic
and effectively optimize long-term effects, taking fluctuating demand and Quality
of Service (QoS) requirements into account. Our extensive evaluation based on
real-world network topologies, services, and traffic traces indicates that FutureCoord
clearly outperforms state-of-the-art model-free and model-based approaches with
up to 51% higher flow success ratios."
author:
- first_name: Stefan
full_name: Werner, Stefan
last_name: Werner
- first_name: Stefan Balthasar
full_name: Schneider, Stefan Balthasar
id: '35343'
last_name: Schneider
orcid: 0000-0001-8210-4011
- first_name: Holger
full_name: Karl, Holger
id: '126'
last_name: Karl
citation:
ama: 'Werner S, Schneider SB, Karl H. Use What You Know: Network and Service Coordination
Beyond Certainty. In: IEEE/IFIP Network Operations and Management Symposium
(NOMS). IEEE; 2022.'
apa: 'Werner, S., Schneider, S. B., & Karl, H. (2022). Use What You Know: Network
and Service Coordination Beyond Certainty. IEEE/IFIP Network Operations and
Management Symposium (NOMS). IEEE/IFIP Network Operations and Management Symposium
(NOMS), Budapest.'
bibtex: '@inproceedings{Werner_Schneider_Karl_2022, title={Use What You Know: Network
and Service Coordination Beyond Certainty}, booktitle={IEEE/IFIP Network Operations
and Management Symposium (NOMS)}, publisher={IEEE}, author={Werner, Stefan and
Schneider, Stefan Balthasar and Karl, Holger}, year={2022} }'
chicago: 'Werner, Stefan, Stefan Balthasar Schneider, and Holger Karl. “Use What
You Know: Network and Service Coordination Beyond Certainty.” In IEEE/IFIP
Network Operations and Management Symposium (NOMS). IEEE, 2022.'
ieee: 'S. Werner, S. B. Schneider, and H. Karl, “Use What You Know: Network and
Service Coordination Beyond Certainty,” presented at the IEEE/IFIP Network Operations
and Management Symposium (NOMS), Budapest, 2022.'
mla: 'Werner, Stefan, et al. “Use What You Know: Network and Service Coordination
Beyond Certainty.” IEEE/IFIP Network Operations and Management Symposium (NOMS),
IEEE, 2022.'
short: 'S. Werner, S.B. Schneider, H. Karl, in: IEEE/IFIP Network Operations and
Management Symposium (NOMS), IEEE, 2022.'
conference:
end_date: 2022-04-29
location: Budapest
name: IEEE/IFIP Network Operations and Management Symposium (NOMS)
start_date: 2022-04-25
date_created: 2022-01-11T08:43:26Z
date_updated: 2022-01-11T08:44:04Z
ddc:
- '004'
department:
- _id: '75'
file:
- access_level: open_access
content_type: application/pdf
creator: stschn
date_created: 2022-01-11T08:39:57Z
date_updated: 2022-01-11T08:39:57Z
file_id: '29222'
file_name: author_version.pdf
file_size: 528653
relation: main_file
file_date_updated: 2022-01-11T08:39:57Z
has_accepted_license: '1'
keyword:
- network management
- service management
- AI
- Monte Carlo Tree Search
- model-based
- QoS
language:
- iso: eng
oa: '1'
project:
- _id: '1'
name: 'SFB 901: SFB 901'
- _id: '4'
name: 'SFB 901 - C: SFB 901 - Project Area C'
- _id: '16'
name: 'SFB 901 - C4: SFB 901 - Subproject C4'
publication: IEEE/IFIP Network Operations and Management Symposium (NOMS)
publisher: IEEE
quality_controlled: '1'
status: public
title: 'Use What You Know: Network and Service Coordination Beyond Certainty'
type: conference
user_id: '35343'
year: '2022'
...
---
_id: '37155'
abstract:
- lang: eng
text: 'Artificial intelligence (AI) has moved beyond the planning phase in many
organisations and it is often accompanied by uncertainties and fears of job loss
among employees. It is crucial to manage employees{\textquoteright} attitudes
towards the deployment of an AI-based technology effectively and counteract possible
resistance behaviour. We present lessons learned from an industry case where we
conducted interviews with affected employees. We evaluated our results with managers
across industries and found that that the deployment of AI-based technologies
does not differ from other IT, but that the change is perceived differently due
to misguided expectations. '
author:
- first_name: Stefan
full_name: Stieglitz, Stefan
last_name: Stieglitz
- first_name: Nicholas R. J.
full_name: Möllmann (Frick), Nicholas R. J.
last_name: Möllmann (Frick)
- first_name: Milad
full_name: Mirbabaie, Milad
id: '88691'
last_name: Mirbabaie
- first_name: Lennart
full_name: Hofeditz, Lennart
last_name: Hofeditz
- first_name: Björn
full_name: Ross, Björn
last_name: Ross
citation:
ama: 'Stieglitz S, Möllmann (Frick) NRJ, Mirbabaie M, Hofeditz L, Ross B. Recommendations
for Managing AI-Driven Change Processes: When Expectations Meet Reality. International
Journal of Management Practice. Published online 2021.'
apa: 'Stieglitz, S., Möllmann (Frick), N. R. J., Mirbabaie, M., Hofeditz, L., &
Ross, B. (2021). Recommendations for Managing AI-Driven Change Processes: When
Expectations Meet Reality. International Journal of Management Practice.'
bibtex: '@article{Stieglitz_Möllmann (Frick)_Mirbabaie_Hofeditz_Ross_2021, title={Recommendations
for Managing AI-Driven Change Processes: When Expectations Meet Reality}, journal={International
Journal of Management Practice}, publisher={Inderscience}, author={Stieglitz,
Stefan and Möllmann (Frick), Nicholas R. J. and Mirbabaie, Milad and Hofeditz,
Lennart and Ross, Björn}, year={2021} }'
chicago: 'Stieglitz, Stefan, Nicholas R. J. Möllmann (Frick), Milad Mirbabaie, Lennart
Hofeditz, and Björn Ross. “Recommendations for Managing AI-Driven Change Processes:
When Expectations Meet Reality.” International Journal of Management Practice,
2021.'
ieee: 'S. Stieglitz, N. R. J. Möllmann (Frick), M. Mirbabaie, L. Hofeditz, and B.
Ross, “Recommendations for Managing AI-Driven Change Processes: When Expectations
Meet Reality,” International Journal of Management Practice, 2021.'
mla: 'Stieglitz, Stefan, et al. “Recommendations for Managing AI-Driven Change Processes:
When Expectations Meet Reality.” International Journal of Management Practice,
Inderscience, 2021.'
short: S. Stieglitz, N.R.J. Möllmann (Frick), M. Mirbabaie, L. Hofeditz, B. Ross,
International Journal of Management Practice (2021).
date_created: 2023-01-17T15:37:55Z
date_updated: 2023-01-18T07:59:08Z
keyword:
- Artificial Intelligence
- Change Management
- Resistance
- AI-Driven Change
- AI Deployment
- AI Perception
language:
- iso: eng
publication: International Journal of Management Practice
publication_identifier:
issn:
- 1477-9064
publisher: Inderscience
status: public
title: 'Recommendations for Managing AI-Driven Change Processes: When Expectations
Meet Reality'
type: journal_article
user_id: '80546'
year: '2021'
...