---
OA_place: repository
OA_type: green
_id: '22103'
abstract:
- lang: eng
text: "Modern AI systems increasingly rely on opaque, highly complex models whose
inner workings remain inaccessible even to experts. This opacity creates challenges
for trust, accountability, and compliance with\r\nemerging regulatory expectations
such as the “right to an explanation”. While traditional explainability methods—feature
attributions, counterfactuals, surrogate models—and interpretable model classes
provide valuable insights for engineers, they often fall short of delivering the
contextual, conversational explanations that\r\nreal users expect. Large Language
Models (LLMs) offer a promising new avenue for explanation due to their\r\nability
to engage interactively, adapt to user needs, and translate technical outputs
into more accessible reasoning. However, their tendencies toward hallucination,
conflict avoidance, and oversimplification introduce\r\nserious risks when used
as explanatory agents. This paper analyzes these opportunities and limitations,
examines verification strategies for ensuring explanation fidelity, and situates
LLM-generated explanations within\r\nbroader concerns about public trust. The
paper concludes by outlining best practices and future research directions for
building robust, verifiable, and human-aligned explanation systems."
acknowledgement: "This work has been supported by the European Research Council under
Grant No.: ERC-2020-AdG\r\n101020093. LLM–based tools have been used as\r\nwriting
assistance to help improve presentation.\r\n"
article_processing_charge: No
author:
- first_name: Filip
full_name: Cano Cordoba, Filip
id: 708cad98-e86a-11ef-8098-bdae2d7c6af1
last_name: Cano Cordoba
orcid: 0000-0002-0783-904X
citation:
ama: 'Cano Cordoba F. Explaining decisions one conversation at a time: Opportunities
and risks of LLMs as explainability assistants. In: Proceedings of the 18th
International Conference on Agents and Artificial Intelligence. Vol 5. Science
and Technology Publications; 2026:4689-4696. doi:10.5220/0014483200004052'
apa: 'Cano Cordoba, F. (2026). Explaining decisions one conversation at a time:
Opportunities and risks of LLMs as explainability assistants. In Proceedings
of the 18th International Conference on Agents and Artificial Intelligence
(Vol. 5, pp. 4689–4696). Marbella, Spain: Science and Technology Publications.
https://doi.org/10.5220/0014483200004052'
chicago: 'Cano Cordoba, Filip. “Explaining Decisions One Conversation at a Time:
Opportunities and Risks of LLMs as Explainability Assistants.” In Proceedings
of the 18th International Conference on Agents and Artificial Intelligence,
5:4689–96. Science and Technology Publications, 2026. https://doi.org/10.5220/0014483200004052.'
ieee: 'F. Cano Cordoba, “Explaining decisions one conversation at a time: Opportunities
and risks of LLMs as explainability assistants,” in Proceedings of the 18th
International Conference on Agents and Artificial Intelligence, Marbella,
Spain, 2026, vol. 5, pp. 4689–4696.'
ista: 'Cano Cordoba F. 2026. Explaining decisions one conversation at a time: Opportunities
and risks of LLMs as explainability assistants. Proceedings of the 18th International
Conference on Agents and Artificial Intelligence. ICAART: International Conference
on Agents and Artificial Intelligence vol. 5, 4689–4696.'
mla: 'Cano Cordoba, Filip. “Explaining Decisions One Conversation at a Time: Opportunities
and Risks of LLMs as Explainability Assistants.” Proceedings of the 18th International
Conference on Agents and Artificial Intelligence, vol. 5, Science and Technology
Publications, 2026, pp. 4689–96, doi:10.5220/0014483200004052.'
short: F. Cano Cordoba, in:, Proceedings of the 18th International Conference on
Agents and Artificial Intelligence, Science and Technology Publications, 2026,
pp. 4689–4696.
conference:
end_date: 2026-03-08
location: Marbella, Spain
name: 'ICAART: International Conference on Agents and Artificial Intelligence'
start_date: 2026-03-05
corr_author: '1'
das_tickbox: '0'
date_created: 2026-06-21T22:03:00Z
date_published: 2026-04-01T00:00:00Z
date_updated: 2026-06-24T08:37:00Z
day: '01'
department:
- _id: ToHe
doi: 10.5220/0014483200004052
ec_funded: 1
intvolume: ' 5'
keyword:
- Explainable AI
- Large Language Models
- Trust in AI
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://filipcano.org/files/icaart26llm.pdf
month: '04'
oa: 1
oa_version: Accepted Version
page: 4689-4696
project:
- _id: 62781420-2b32-11ec-9570-8d9b63373d4d
call_identifier: H2020
grant_number: '101020093'
name: Vigilant Algorithmic Monitoring of Software
publication: Proceedings of the 18th International Conference on Agents and Artificial
Intelligence
publication_identifier:
eissn:
- 2184-433X
isbn:
- '9789897587962'
issn:
- 2184-3589
publication_status: published
publisher: Science and Technology Publications
quality_controlled: '1'
researchdata_availability: no
scopus_import: '1'
status: public
supplementarymaterial: no
title: 'Explaining decisions one conversation at a time: Opportunities and risks of
LLMs as explainability assistants'
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 5
year: '2026'
...
---
OA_place: repository
OA_type: green
_id: '22294'
abstract:
- lang: eng
text: 'Modern computer systems store vast amounts of personal data, enabling advances
in AI and ML but risking user privacy and trust. For privacy reasons, it is sometimes
desired for an ML model to forget part of the data it was trained on. In this
paper, we introduce a novel unlearning approach based on Forgetting Neural Networks
(FNNs), a neuroscience-inspired architecture that explicitly encodes forgetting
through multiplicative decay factors. While FNNs had previously been studied as
a theoretical construct, we provide the first concrete implementation and demonstrate
their effectiveness for targeted unlearning. We propose several variants with
per-neuron forgetting factors, including rank-based assignments guided by activation
levels, and evaluate them on MNIST and Fashion-MNIST benchmarks. Our method systematically
removes information associated with forget sets while preserving performance on
retained data. Membership inference attacks confirm the effectiveness of FNN-based
unlearning in erasing information about the training data from the neural network.
These results establish FNNs as a promising foundation for efficient and interpretable
unlearning. '
article_processing_charge: No
arxiv: 1
author:
- first_name: Amartya
full_name: Hatua, Amartya
last_name: Hatua
- first_name: Trung
full_name: Nguyen, Trung
last_name: Nguyen
- first_name: Filip
full_name: Cano Cordoba, Filip
id: 708cad98-e86a-11ef-8098-bdae2d7c6af1
last_name: Cano Cordoba
orcid: 0000-0002-0783-904X
- first_name: Andrew
full_name: Sung, Andrew
last_name: Sung
citation:
ama: 'Hatua A, Nguyen T, Cano Cordoba F, Sung A. Machine unlearning using forgetting
neural networks. In: Proceedings of the 18th International Conference on Agents
and Artificial Intelligence. Vol 2. SciTePress; 2026:1536-1546. doi:10.5220/0014326500004052'
apa: 'Hatua, A., Nguyen, T., Cano Cordoba, F., & Sung, A. (2026). Machine unlearning
using forgetting neural networks. In Proceedings of the 18th International
Conference on Agents and Artificial Intelligence (Vol. 2, pp. 1536–1546).
Marbella, Spain: SciTePress. https://doi.org/10.5220/0014326500004052'
chicago: Hatua, Amartya, Trung Nguyen, Filip Cano Cordoba, and Andrew Sung. “Machine
Unlearning Using Forgetting Neural Networks.” In Proceedings of the 18th International
Conference on Agents and Artificial Intelligence, 2:1536–46. SciTePress, 2026.
https://doi.org/10.5220/0014326500004052.
ieee: A. Hatua, T. Nguyen, F. Cano Cordoba, and A. Sung, “Machine unlearning using
forgetting neural networks,” in Proceedings of the 18th International Conference
on Agents and Artificial Intelligence, Marbella, Spain, 2026, vol. 2, pp.
1536–1546.
ista: 'Hatua A, Nguyen T, Cano Cordoba F, Sung A. 2026. Machine unlearning using
forgetting neural networks. Proceedings of the 18th International Conference on
Agents and Artificial Intelligence. ICAART: International Conference on Agents
and Artificial Intelligence vol. 2, 1536–1546.'
mla: Hatua, Amartya, et al. “Machine Unlearning Using Forgetting Neural Networks.”
Proceedings of the 18th International Conference on Agents and Artificial Intelligence,
vol. 2, SciTePress, 2026, pp. 1536–46, doi:10.5220/0014326500004052.
short: A. Hatua, T. Nguyen, F. Cano Cordoba, A. Sung, in:, Proceedings of the 18th
International Conference on Agents and Artificial Intelligence, SciTePress, 2026,
pp. 1536–1546.
conference:
end_date: 2026-03-08
location: Marbella, Spain
name: 'ICAART: International Conference on Agents and Artificial Intelligence'
start_date: 2026-03-05
das_tickbox: '1'
date_created: 2026-07-13T09:46:46Z
date_published: 2026-06-30T00:00:00Z
date_updated: 2026-07-16T09:02:53Z
day: '30'
department:
- _id: ToHe
doi: 10.5220/0014326500004052
external_id:
arxiv:
- '2410.22374'
intvolume: ' 2'
keyword:
- Machine Unlearning
- Neuroscience-Inspired Machine Learning
- Membership Inference Attacks
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2410.22374
month: '06'
oa: 1
oa_version: Preprint
page: 1536-1546
publication: Proceedings of the 18th International Conference on Agents and Artificial
Intelligence
publication_identifier:
eissn:
- 2184-433X
isbn:
- '9789897587962'
publication_status: published
publisher: SciTePress
quality_controlled: '1'
scopus_import: '1'
status: public
title: Machine unlearning using forgetting neural networks
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 2
year: '2026'
...
---
OA_place: publisher
OA_type: gold
_id: '22321'
abstract:
- lang: eng
text: 'Runtime fairness is not a one-time constraint but a dynamic property evaluated
over a sequence of decisions. To ensure fairness at runtime, it is necessary to
account for past decisions, information neglected by conventional, static classifiers.
Traditional fairness shields enforce runtime fairness abruptly, by intervening
deterministically whenever a sequence of decisions violates the target for a running
fairness measure. This motivates our main conceptual contribution: energy shields.
An energy shield is a novel, lightweight, adaptive controller that monitors a
sequence of decisions and intervenes probabilistically to ensure runtime fairness
smoothly, by utilizing physics-inspired energy functions to nudge the sequence
toward fairness: the more unfair the decisions, the stronger the nudging force
becomes. This makes energy shields the first fairness shields to provide both
short-term safety and long-term liveness guarantees. Safety ensures that the running
fairness measure stays within a running target interval with high probability,
and liveness ensures that the limit of the fairness measure lies within the limit
target interval. Intuitively, the short-term specifies the tolerated fairness
values and the long-term specifies the desired fairness values. We also provide
a synthesis procedure for constructing the least intrusive energy shield for a
given target specification, and demonstrate its efficiency experimentally. We
evaluate our energy shields against existing fairness shields through the lens
of short- and long-term fairness.'
acknowledgement: 'This work has been supported by the European Research Council under
Grant No.: ERC-2020-AdG 101020093.'
article_processing_charge: Yes
arxiv: 1
author:
- first_name: Filip
full_name: Cano Cordoba, Filip
id: 708cad98-e86a-11ef-8098-bdae2d7c6af1
last_name: Cano Cordoba
orcid: 0000-0002-0783-904X
- first_name: Thomas A
full_name: Henzinger, Thomas A
id: 40876CD8-F248-11E8-B48F-1D18A9856A87
last_name: Henzinger
orcid: 0000-0002-2985-7724
- first_name: Konstantin
full_name: Kueffner, Konstantin
id: 8121a2d0-dc85-11ea-9058-af578f3b4515
last_name: Kueffner
orcid: 0000-0001-8974-2542
citation:
ama: 'Cano Cordoba F, Henzinger TA, Kueffner K. Energy shields for fairness. In:
Proceedings of the 2026 ACM Conference on Fairness, Accountability, and Transparency.
Association for Computing Machinery; 2026:4243-4275. doi:10.1145/3805689.3806807'
apa: 'Cano Cordoba, F., Henzinger, T. A., & Kueffner, K. (2026). Energy shields
for fairness. In Proceedings of the 2026 ACM Conference on Fairness, Accountability,
and Transparency (pp. 4243–4275). Montreal, Canada: Association for Computing
Machinery. https://doi.org/10.1145/3805689.3806807'
chicago: Cano Cordoba, Filip, Thomas A Henzinger, and Konstantin Kueffner. “Energy
Shields for Fairness.” In Proceedings of the 2026 ACM Conference on Fairness,
Accountability, and Transparency, 4243–75. Association for Computing Machinery,
2026. https://doi.org/10.1145/3805689.3806807.
ieee: F. Cano Cordoba, T. A. Henzinger, and K. Kueffner, “Energy shields for fairness,”
in Proceedings of the 2026 ACM Conference on Fairness, Accountability, and
Transparency, Montreal, Canada, 2026, pp. 4243–4275.
ista: 'Cano Cordoba F, Henzinger TA, Kueffner K. 2026. Energy shields for fairness.
Proceedings of the 2026 ACM Conference on Fairness, Accountability, and Transparency.
FAccT: Conference on Fairness, Accountability and Transparency, 4243–4275.'
mla: Cano Cordoba, Filip, et al. “Energy Shields for Fairness.” Proceedings of
the 2026 ACM Conference on Fairness, Accountability, and Transparency, Association
for Computing Machinery, 2026, pp. 4243–75, doi:10.1145/3805689.3806807.
short: F. Cano Cordoba, T.A. Henzinger, K. Kueffner, in:, Proceedings of the 2026
ACM Conference on Fairness, Accountability, and Transparency, Association for
Computing Machinery, 2026, pp. 4243–4275.
conference:
end_date: 2026-06-28
location: Montreal, Canada
name: 'FAccT: Conference on Fairness, Accountability and Transparency'
start_date: 2026-06-25
corr_author: '1'
das_tickbox: '0'
date_created: 2026-07-14T05:32:45Z
date_published: 2026-07-01T00:00:00Z
date_updated: 2026-07-22T06:15:56Z
day: '01'
ddc:
- '000'
department:
- _id: ToHe
doi: 10.1145/3805689.3806807
ec_funded: 1
external_id:
arxiv:
- '2605.24926'
file:
- access_level: open_access
checksum: 21e648ea3b529f0df7545ad4b31b0ef4
content_type: application/pdf
creator: dernst
date_created: 2026-07-16T09:23:15Z
date_updated: 2026-07-16T09:23:15Z
file_id: '22348'
file_name: 2026_ACMFACCT_Cano.pdf
file_size: 3129128
relation: main_file
success: 1
file_date_updated: 2026-07-16T09:23:15Z
has_accepted_license: '1'
language:
- iso: eng
month: '07'
oa: 1
oa_version: Published Version
page: 4243 - 4275
project:
- _id: 62781420-2b32-11ec-9570-8d9b63373d4d
call_identifier: H2020
grant_number: '101020093'
name: Vigilant Algorithmic Monitoring of Software
publication: Proceedings of the 2026 ACM Conference on Fairness, Accountability, and
Transparency
publication_status: published
publisher: Association for Computing Machinery
quality_controlled: '1'
researchdata_availability: no
scopus_import: '1'
status: public
supplementarymaterial: yes
title: Energy shields for fairness
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2026'
...
---
OA_place: repository
OA_type: green
_id: '21090'
abstract:
- lang: eng
text: Fairness in AI is traditionally studied as a static property evaluated once,
over a fixed dataset. However, real-world AI systems operate sequentially, with
outcomes and environments evolving over time. This paper proposes a framework
for analysing fairness as a runtime property. Using a minimal yet expressive model
based on sequences of coin tosses with possibly evolving biases, we study the
problems of monitoring and enforcing fairness expressed in either toss outcomes
or coin biases. Since there is no one-size-fits-all solution for either problem,
we provide a summary of monitoring and enforcement strategies, parametrised by
environment dynamics, prediction horizon, and confidence thresholds. For both
problems, we present general results under simple or minimal assumptions. We survey
existing solutions for the monitoring problem for Markovian and additive dynamics,
and existing solutions for the enforcement problem in static settings with known
dynamics.
acknowledgement: 'This work is supported by the European Research Council under Grant
No.: ERC-2020-AdG 101020093.'
alternative_title:
- LNCS
article_processing_charge: No
arxiv: 1
author:
- first_name: Filip
full_name: Cano Cordoba, Filip
id: 708cad98-e86a-11ef-8098-bdae2d7c6af1
last_name: Cano Cordoba
orcid: 0000-0002-0783-904X
- first_name: Thomas A
full_name: Henzinger, Thomas A
id: 40876CD8-F248-11E8-B48F-1D18A9856A87
last_name: Henzinger
orcid: 0000-0002-2985-7724
- first_name: Konstantin
full_name: Kueffner, Konstantin
id: 8121a2d0-dc85-11ea-9058-af578f3b4515
last_name: Kueffner
orcid: 0000-0001-8974-2542
citation:
ama: 'Cano Cordoba F, Henzinger TA, Kueffner K. Algorithmic fairness: A runtime
perspective. In: 25th International Conference on Runtime Verification.
Vol 16087. Springer Nature; 2025:1-21. doi:10.1007/978-3-032-05435-7_1'
apa: 'Cano Cordoba, F., Henzinger, T. A., & Kueffner, K. (2025). Algorithmic
fairness: A runtime perspective. In 25th International Conference on Runtime
Verification (Vol. 16087, pp. 1–21). Graz, Austria: Springer Nature. https://doi.org/10.1007/978-3-032-05435-7_1'
chicago: 'Cano Cordoba, Filip, Thomas A Henzinger, and Konstantin Kueffner. “Algorithmic
Fairness: A Runtime Perspective.” In 25th International Conference on Runtime
Verification, 16087:1–21. Springer Nature, 2025. https://doi.org/10.1007/978-3-032-05435-7_1.'
ieee: 'F. Cano Cordoba, T. A. Henzinger, and K. Kueffner, “Algorithmic fairness:
A runtime perspective,” in 25th International Conference on Runtime Verification,
Graz, Austria, 2025, vol. 16087, pp. 1–21.'
ista: 'Cano Cordoba F, Henzinger TA, Kueffner K. 2025. Algorithmic fairness: A runtime
perspective. 25th International Conference on Runtime Verification. RV: Runtime
Verification, LNCS, vol. 16087, 1–21.'
mla: 'Cano Cordoba, Filip, et al. “Algorithmic Fairness: A Runtime Perspective.”
25th International Conference on Runtime Verification, vol. 16087, Springer
Nature, 2025, pp. 1–21, doi:10.1007/978-3-032-05435-7_1.'
short: F. Cano Cordoba, T.A. Henzinger, K. Kueffner, in:, 25th International Conference
on Runtime Verification, Springer Nature, 2025, pp. 1–21.
conference:
end_date: 2025-09-19
location: Graz, Austria
name: 'RV: Runtime Verification'
start_date: 2025-09-15
corr_author: '1'
date_created: 2026-01-29T16:01:41Z
date_published: 2025-09-13T00:00:00Z
date_updated: 2026-02-16T11:57:00Z
day: '13'
department:
- _id: ToHe
doi: 10.1007/978-3-032-05435-7_1
ec_funded: 1
external_id:
arxiv:
- '2507.20711'
intvolume: ' 16087'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2507.20711
month: '09'
oa: 1
oa_version: Preprint
page: 1-21
project:
- _id: 62781420-2b32-11ec-9570-8d9b63373d4d
call_identifier: H2020
grant_number: '101020093'
name: Vigilant Algorithmic Monitoring of Software
publication: 25th International Conference on Runtime Verification
publication_identifier:
eisbn:
- '9783032054357'
eissn:
- 1611-3349
issn:
- 0302-9743
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
status: public
title: 'Algorithmic fairness: A runtime perspective'
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 16087
year: '2025'
...
---
OA_place: repository
OA_type: green
_id: '19665'
abstract:
- lang: eng
text: As AI-based decision-makers increasingly influence human lives, it is a growing
concern that their decisions may be unfair or biased with respect to people's
protected attributes, such as gender and race. Most existing bias prevention measures
provide probabilistic fairness guarantees in the long run, and it is possible
that the decisions are biased on any decision sequence of fixed length. We introduce
*fairness shielding*, where a symbolic decision-maker---the fairness shield---continuously
monitors the sequence of decisions of another deployed black-box decision-maker,
and makes interventions so that a given fairness criterion is met while the total
intervention costs are minimized. We present four different algorithms for computing
fairness shields, among which one guarantees fairness over fixed horizons, and
three guarantee fairness periodically after fixed intervals. Given a distribution
over future decisions and their intervention costs, our algorithms solve different
instances of bounded-horizon optimal control problems with different levels of
computational costs and optimality guarantees. Our empirical evaluation demonstrates
the effectiveness of these shields in ensuring fairness while maintaining cost
efficiency across various scenarios.
acknowledgement: 'This work is partly supported by the European Research Council under
Grant No.: ERC-2020-AdG 101020093. It is also partially supported by the State Government
of Styria, Austria – Department Zukunftsfonds Steiermark.'
article_processing_charge: No
arxiv: 1
author:
- first_name: Filip
full_name: Cano Cordoba, Filip
id: 708cad98-e86a-11ef-8098-bdae2d7c6af1
last_name: Cano Cordoba
orcid: 0000-0002-0783-904X
- first_name: Thomas A
full_name: Henzinger, Thomas A
id: 40876CD8-F248-11E8-B48F-1D18A9856A87
last_name: Henzinger
orcid: 0000-0002-2985-7724
- first_name: Bettina
full_name: Könighofer, Bettina
last_name: Könighofer
- first_name: Konstantin
full_name: Kueffner, Konstantin
id: 8121a2d0-dc85-11ea-9058-af578f3b4515
last_name: Kueffner
orcid: 0000-0001-8974-2542
- first_name: Kaushik
full_name: Mallik, Kaushik
id: 0834ff3c-6d72-11ec-94e0-b5b0a4fb8598
last_name: Mallik
orcid: 0000-0001-9864-7475
citation:
ama: 'Cano Cordoba F, Henzinger TA, Könighofer B, Kueffner K, Mallik K. Fairness
shields: Safeguarding against biased decision makers. In: Proceedings of the
39th AAAI Conference on Artificial Intelligence. Vol 39. Association for the
Advancement of Artificial Intelligence; 2025:15659-15668. doi:10.1609/aaai.v39i15.33719'
apa: 'Cano Cordoba, F., Henzinger, T. A., Könighofer, B., Kueffner, K., & Mallik,
K. (2025). Fairness shields: Safeguarding against biased decision makers. In Proceedings
of the 39th AAAI Conference on Artificial Intelligence (Vol. 39, pp. 15659–15668).
Philadelphia, PA, United States: Association for the Advancement of Artificial
Intelligence. https://doi.org/10.1609/aaai.v39i15.33719'
chicago: 'Cano Cordoba, Filip, Thomas A Henzinger, Bettina Könighofer, Konstantin
Kueffner, and Kaushik Mallik. “Fairness Shields: Safeguarding against Biased Decision
Makers.” In Proceedings of the 39th AAAI Conference on Artificial Intelligence,
39:15659–68. Association for the Advancement of Artificial Intelligence, 2025.
https://doi.org/10.1609/aaai.v39i15.33719.'
ieee: 'F. Cano Cordoba, T. A. Henzinger, B. Könighofer, K. Kueffner, and K. Mallik,
“Fairness shields: Safeguarding against biased decision makers,” in Proceedings
of the 39th AAAI Conference on Artificial Intelligence, Philadelphia, PA,
United States, 2025, vol. 39, no. 15, pp. 15659–15668.'
ista: 'Cano Cordoba F, Henzinger TA, Könighofer B, Kueffner K, Mallik K. 2025. Fairness
shields: Safeguarding against biased decision makers. Proceedings of the 39th
AAAI Conference on Artificial Intelligence. AAAI: Conference on Artificial Intelligence
vol. 39, 15659–15668.'
mla: 'Cano Cordoba, Filip, et al. “Fairness Shields: Safeguarding against Biased
Decision Makers.” Proceedings of the 39th AAAI Conference on Artificial Intelligence,
vol. 39, no. 15, Association for the Advancement of Artificial Intelligence, 2025,
pp. 15659–68, doi:10.1609/aaai.v39i15.33719.'
short: F. Cano Cordoba, T.A. Henzinger, B. Könighofer, K. Kueffner, K. Mallik, in:,
Proceedings of the 39th AAAI Conference on Artificial Intelligence, Association
for the Advancement of Artificial Intelligence, 2025, pp. 15659–15668.
conference:
end_date: 2025-03-04
location: Philadelphia, PA, United States
name: 'AAAI: Conference on Artificial Intelligence'
start_date: 2025-02-25
corr_author: '1'
date_created: 2025-05-11T22:02:39Z
date_published: 2025-04-11T00:00:00Z
date_updated: 2026-02-16T12:24:30Z
day: '11'
department:
- _id: ToHe
doi: 10.1609/aaai.v39i15.33719
ec_funded: 1
external_id:
arxiv:
- '2412.11994'
intvolume: ' 39'
issue: '15'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2412.11994
month: '04'
oa: 1
oa_version: Preprint
page: 15659-15668
project:
- _id: 62781420-2b32-11ec-9570-8d9b63373d4d
call_identifier: H2020
grant_number: '101020093'
name: Vigilant Algorithmic Monitoring of Software
publication: Proceedings of the 39th AAAI Conference on Artificial Intelligence
publication_identifier:
eissn:
- 2374-3468
issn:
- 2159-5399
publication_status: published
publisher: Association for the Advancement of Artificial Intelligence
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'Fairness shields: Safeguarding against biased decision makers'
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 39
year: '2025'
...