Slide
Centre Interdisciplinaire
de Recherche et d’Innovation
en Cybersécurité et Société
de Recherche et d’Innovation
en Cybersécurité et Société
1.
Tiwari, A.; Arrabito, R.; Davoust, A.; Falk, T. H.
Quantifying Biological Sex Leakage in Electroencephalography-Based Mental Workload Measurement and Its Impact on Model Performance Proceedings Article
In: IEEE Int. Conf. Hum.-Mach. Syst., ICHMS, pp. 392–397, Institute of Electrical and Electronics Engineers Inc., 2026, ISBN: 979-833154511-6 (ISBN), (Journal Abbreviation: IEEE Int. Conf. Hum.-Mach. Syst., ICHMS).
Abstract | Links | BibTeX | Tags: Biomedical signal processing, Demographic information, Designing systems, Economic and social effects, Electroencephalography, Electrophysiology, Human operator, Mental workload, Modeling performance, Optimal performance, Performance, Population statistics, Real- time, Well being, Workload measurements
@inproceedings{tiwariQuantifyingBiologicalSex2026,
title = {Quantifying Biological Sex Leakage in Electroencephalography-Based Mental Workload Measurement and Its Impact on Model Performance},
author = {A. Tiwari and R. Arrabito and A. Davoust and T. H. Falk},
url = {https://www.scopus.com/pages/publications/105045575255?origin=resultslist},
doi = {10.1109/ICHMS69701.2026.11602273},
isbn = {979-833154511-6 (ISBN)},
year = {2026},
date = {2026-01-01},
booktitle = {IEEE Int. Conf. Hum.-Mach. Syst., ICHMS},
pages = {392–397},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Detection of mental workload is essential for designing systems that can monitor and adapt to the needs of human operators, thereby ensuring their optimal performance and well-being. Features derived from neurophysiological signals, particularly electroencephalograms (EEG), provide a realtime, unobtrusive, and objective indicator of mental workload. However, EEG signals are often correlated with demographic variables, including biological sex and age, which can introduce systematic performance differences across demographic groups, while unintentionally encoding demographic information into models trained on EEG data. In this work, we investigate and quantify the influence of user demographics on mental workload detection models using fairness analysis. Furthermore, we propose domain-adversarial training, in which an adversary penalizes the model for retaining sex-related information, to mitigate leakage of private demographic information (biological sex). The proposed approach simultaneously increases the odds ratio for sex from 0.83 to 0.90 (values closer to 1 indicate reduced demographic disparity) while reducing statistical significance. This approach, however, also leads to a reduction in mental workload detection performance, with a 12.7% decrease in balanced accuracy (BACC), suggesting an inherent trade-off between performance and privacy protection. © 2026 IEEE.},
note = {Journal Abbreviation: IEEE Int. Conf. Hum.-Mach. Syst., ICHMS},
keywords = {Biomedical signal processing, Demographic information, Designing systems, Economic and social effects, Electroencephalography, Electrophysiology, Human operator, Mental workload, Modeling performance, Optimal performance, Performance, Population statistics, Real- time, Well being, Workload measurements},
pubstate = {published},
tppubtype = {inproceedings}
}
Detection of mental workload is essential for designing systems that can monitor and adapt to the needs of human operators, thereby ensuring their optimal performance and well-being. Features derived from neurophysiological signals, particularly electroencephalograms (EEG), provide a realtime, unobtrusive, and objective indicator of mental workload. However, EEG signals are often correlated with demographic variables, including biological sex and age, which can introduce systematic performance differences across demographic groups, while unintentionally encoding demographic information into models trained on EEG data. In this work, we investigate and quantify the influence of user demographics on mental workload detection models using fairness analysis. Furthermore, we propose domain-adversarial training, in which an adversary penalizes the model for retaining sex-related information, to mitigate leakage of private demographic information (biological sex). The proposed approach simultaneously increases the odds ratio for sex from 0.83 to 0.90 (values closer to 1 indicate reduced demographic disparity) while reducing statistical significance. This approach, however, also leads to a reduction in mental workload detection performance, with a 12.7% decrease in balanced accuracy (BACC), suggesting an inherent trade-off between performance and privacy protection. © 2026 IEEE.
2.
Papapanagiotou, P.; Manataki, A.; Davoust, A.; Kleek, M. Van; Robertson, D.; Murray-Rust, D.; Shadbolt, N.
Social machines for all: Blue sky ideas track Proceedings Article
In: Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS, pp. 1208–1212, International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS), 2018, ISSN: 15488403, (ISSN: 15488403).
Abstract | Links | BibTeX | Tags: Agent based simulation, Analysis, Autonomous agents, Design, Development method, Distributed agents, Distributed computer systems, Easy-to-use systems, Economic and social effects, Electronic institutions, Intelligent agents, Model driven development, Model-driven Engineering, Models, Multi agent systems, Systems analysis
@inproceedings{papapanagiotou_social_2018,
title = {Social machines for all: Blue sky ideas track},
author = {P. Papapanagiotou and A. Manataki and A. Davoust and M. Van Kleek and D. Robertson and D. Murray-Rust and N. Shadbolt},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85054668594&partnerID=40&md5=77eba348dbafa30aef9d016186b46804},
issn = {15488403},
year = {2018},
date = {2018-01-01},
booktitle = {Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS},
volume = {2},
pages = {1208–1212},
publisher = {International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)},
abstract = {In today's interconnected world, people interact to a unprecedented degree through the use of digital platforms and services, forming complex 'social machines'. These are now homes to autonomous agents as well as people, providing an open space where human and computational intelligence can mingle-a new frontier for distributed agent systems. However, participants typically have limited autonomy to define and shape the machines they are part of. In this paper, we envision a future where individuals are able to develop their own Social Machines, enabling them to interact in a trustworthy, decentralized way. To make this possible, development methods and tools must see their barriers-to-entry dramatically lowered. People should be able to specify the agent roles and inte-raction patterns in an intuitive, visual way, analyse and test their designs and deploy them as easy to use systems. We argue that this is a challenging but realistic goal, which should be tackled by navigating the trade-off between the accessibility of the design methods -primarily the modelling formalisms- And their expressive power. We support our arguments by drawing ideas from different research areas including electronic institutions, agent-based simulation, process modelling, formal verification, and model-driven engineering. © 2018 International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved.},
note = {ISSN: 15488403},
keywords = {Agent based simulation, Analysis, Autonomous agents, Design, Development method, Distributed agents, Distributed computer systems, Easy-to-use systems, Economic and social effects, Electronic institutions, Intelligent agents, Model driven development, Model-driven Engineering, Models, Multi agent systems, Systems analysis},
pubstate = {published},
tppubtype = {inproceedings}
}
In today's interconnected world, people interact to a unprecedented degree through the use of digital platforms and services, forming complex 'social machines'. These are now homes to autonomous agents as well as people, providing an open space where human and computational intelligence can mingle-a new frontier for distributed agent systems. However, participants typically have limited autonomy to define and shape the machines they are part of. In this paper, we envision a future where individuals are able to develop their own Social Machines, enabling them to interact in a trustworthy, decentralized way. To make this possible, development methods and tools must see their barriers-to-entry dramatically lowered. People should be able to specify the agent roles and inte-raction patterns in an intuitive, visual way, analyse and test their designs and deploy them as easy to use systems. We argue that this is a challenging but realistic goal, which should be tackled by navigating the trade-off between the accessibility of the design methods -primarily the modelling formalisms- And their expressive power. We support our arguments by drawing ideas from different research areas including electronic institutions, agent-based simulation, process modelling, formal verification, and model-driven engineering. © 2018 International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved.
3.
Papapanagiotou, P.; Manataki, A.; Davoust, A.; Kleek, M. Van; Robertson, D.; Murray-Rust, D.; Shadbolt, N.
Social machines for all: Blue sky ideas track Proceedings Article
In: Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS, pp. 1208–1212, International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS), 2018, ISSN: 15488403.
Abstract | Links | BibTeX | Tags: Agent based simulation, Analysis, Autonomous agents, Design, Development method, Distributed agents, Distributed computer systems, Easy-to-use systems, Economic and social effects, Electronic institutions, Intelligent agents, Model driven development, Model-driven Engineering, Models, Multi agent systems, Systems analysis
@inproceedings{papapanagiotouSocialMachinesAll2018,
title = {Social machines for all: Blue sky ideas track},
author = {P. Papapanagiotou and A. Manataki and A. Davoust and M. Van Kleek and D. Robertson and D. Murray-Rust and N. Shadbolt},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85054668594&partnerID=40&md5=77eba348dbafa30aef9d016186b46804},
issn = {15488403},
year = {2018},
date = {2018-01-01},
booktitle = {Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS},
volume = {2},
pages = {1208–1212},
publisher = {International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)},
abstract = {In today's interconnected world, people interact to a unprecedented degree through the use of digital platforms and services, forming complex 'social machines'. These are now homes to autonomous agents as well as people, providing an open space where human and computational intelligence can mingle-a new frontier for distributed agent systems. However, participants typically have limited autonomy to define and shape the machines they are part of. In this paper, we envision a future where individuals are able to develop their own Social Machines, enabling them to interact in a trustworthy, decentralized way. To make this possible, development methods and tools must see their barriers-to-entry dramatically lowered. People should be able to specify the agent roles and inte-raction patterns in an intuitive, visual way, analyse and test their designs and deploy them as easy to use systems. We argue that this is a challenging but realistic goal, which should be tackled by navigating the trade-off between the accessibility of the design methods -primarily the modelling formalisms- And their expressive power. We support our arguments by drawing ideas from different research areas including electronic institutions, agent-based simulation, process modelling, formal verification, and model-driven engineering. © 2018 International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved.},
keywords = {Agent based simulation, Analysis, Autonomous agents, Design, Development method, Distributed agents, Distributed computer systems, Easy-to-use systems, Economic and social effects, Electronic institutions, Intelligent agents, Model driven development, Model-driven Engineering, Models, Multi agent systems, Systems analysis},
pubstate = {published},
tppubtype = {inproceedings}
}
In today's interconnected world, people interact to a unprecedented degree through the use of digital platforms and services, forming complex 'social machines'. These are now homes to autonomous agents as well as people, providing an open space where human and computational intelligence can mingle-a new frontier for distributed agent systems. However, participants typically have limited autonomy to define and shape the machines they are part of. In this paper, we envision a future where individuals are able to develop their own Social Machines, enabling them to interact in a trustworthy, decentralized way. To make this possible, development methods and tools must see their barriers-to-entry dramatically lowered. People should be able to specify the agent roles and inte-raction patterns in an intuitive, visual way, analyse and test their designs and deploy them as easy to use systems. We argue that this is a challenging but realistic goal, which should be tackled by navigating the trade-off between the accessibility of the design methods -primarily the modelling formalisms- And their expressive power. We support our arguments by drawing ideas from different research areas including electronic institutions, agent-based simulation, process modelling, formal verification, and model-driven engineering. © 2018 International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved.