Slide
Centre Interdisciplinaire
de Recherche et d’Innovation
en Cybersécurité et Société
de Recherche et d’Innovation
en Cybersécurité et Société
1.
Santana, S. T. O. D.; Silva, J. C. Da; Hatano, Y.; Guerrero-Mendez, C. D.; Souza, M. L. De; Igasaki, T.; Bastos-Filho, T.; Dantas, A. F. O. De Azevedo; Falk, T. H.; Delisle-Rodriguez, D.
Cognitive Training Using a Brain-Computer Interface for θ/β Ratio Self-Regulation - Preliminary Results with Long Covid-19 Survivors Proceedings Article
In: IEEE Int. Conf. Hum.-Mach. Syst., ICHMS, pp. 646–650, 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: 'current, Behavioral research, Brain, brain computer interface, Brain mapping, cognitive impairment, Cognitive skill, Cognitive systems, Cognitive training, Computer games, COVID-19, Electroencephalography, Electrophysiology, Executive Function, Interfaces (computer), Memory functions, Neurotechnology, Personnel training, Self regulation, Sustained attention, working memory
@inproceedings{santanaCognitiveTrainingUsing2026,
title = {Cognitive Training Using a Brain-Computer Interface for θ/β Ratio Self-Regulation - Preliminary Results with Long Covid-19 Survivors},
author = {S. T. O. D. Santana and J. C. Da Silva and Y. Hatano and C. D. Guerrero-Mendez and M. L. De Souza and T. Igasaki and T. Bastos-Filho and A. F. O. De Azevedo Dantas and T. H. Falk and D. Delisle-Rodriguez},
url = {https://www.scopus.com/pages/publications/105045602964?origin=resultslist},
doi = {10.1109/ICHMS69701.2026.11602366},
isbn = {979-833154511-6 (ISBN)},
year = {2026},
date = {2026-01-01},
booktitle = {IEEE Int. Conf. Hum.-Mach. Syst., ICHMS},
pages = {646–650},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Long COVID-19 can generate cognitive impairments, affecting a person's attention, working memory, and executive functions. Previous studies have suggested that therapies based on neurotechnologies can be used to recover cognitive skills. This current research presents an electroencephalography brain-computer interface based serious game for sustained attention training. The system classifies attention levels while the user controls the speed of a virtual car by self-regulating the θ/β power band ratio across multiple brain locations. Pilot experiments were conducted with two COVID-19 adult survivors who experienced cognitive decline due to long COVID19 symptoms. Participants underwent 10 sessions across consecutive working days (2 sessions × week). Both participants enhanced brain wave oscillations in both frontal and central regions, reducing theta activity and simultaneously increasing beta oscillations) after the training, achieving therefore healthy brain behavior during a cognitive attention task. Furthermore, both individuals improved cognitive skills, such as attention, working memory, and executive functions. These preliminary findings are promising and motivate the further development of multichannel neurofeedback systems for cognitive training for long COVID-19 survivors. © 2026 IEEE.},
note = {Journal Abbreviation: IEEE Int. Conf. Hum.-Mach. Syst., ICHMS},
keywords = {'current, Behavioral research, Brain, brain computer interface, Brain mapping, cognitive impairment, Cognitive skill, Cognitive systems, Cognitive training, Computer games, COVID-19, Electroencephalography, Electrophysiology, Executive Function, Interfaces (computer), Memory functions, Neurotechnology, Personnel training, Self regulation, Sustained attention, working memory},
pubstate = {published},
tppubtype = {inproceedings}
}
Long COVID-19 can generate cognitive impairments, affecting a person's attention, working memory, and executive functions. Previous studies have suggested that therapies based on neurotechnologies can be used to recover cognitive skills. This current research presents an electroencephalography brain-computer interface based serious game for sustained attention training. The system classifies attention levels while the user controls the speed of a virtual car by self-regulating the θ/β power band ratio across multiple brain locations. Pilot experiments were conducted with two COVID-19 adult survivors who experienced cognitive decline due to long COVID19 symptoms. Participants underwent 10 sessions across consecutive working days (2 sessions × week). Both participants enhanced brain wave oscillations in both frontal and central regions, reducing theta activity and simultaneously increasing beta oscillations) after the training, achieving therefore healthy brain behavior during a cognitive attention task. Furthermore, both individuals improved cognitive skills, such as attention, working memory, and executive functions. These preliminary findings are promising and motivate the further development of multichannel neurofeedback systems for cognitive training for long COVID-19 survivors. © 2026 IEEE.
2.
Tiwari, A.; Arrabito, R.; Davoust, A.; Falk, T. H.
Bias in Physiology-Based Cognitive State Detection for Human–Autonomy Teaming: A Comprehensive Survey Proceedings Article
In: R.A., Sottilare; J., Schwarz (Ed.): Lect. Notes Comput. Sci., pp. 139–158, Springer Science and Business Media Deutschland GmbH, 2026, ISBN: 03029743 (ISSN); 978-303230014-0 (ISBN), (Journal Abbreviation: Lect. Notes Comput. Sci.).
Abstract | Links | BibTeX | Tags: Behavioral research, Bias, Biases, Biomedical signal processing, Cognitive state, Cognitive systems, Human Autonomy Teaming, Instructional system, Intelligent systems, Learning pathway, Learning systems, Personalized learning, Physiological models, Physiological signals, Population statistics, Psychophysiology, State Detection, Student feedback, Surveying, Wearable devices, Wearable technology
@inproceedings{tiwariBiasPhysiologyBasedCognitive2026,
title = {Bias in Physiology-Based Cognitive State Detection for Human–Autonomy Teaming: A Comprehensive Survey},
author = {A. Tiwari and R. Arrabito and A. Davoust and T. H. Falk},
editor = {Sottilare R.A. and Schwarz J.},
url = {https://www.scopus.com/pages/publications/105044000677?origin=resultslist},
doi = {10.1007/978-3-032-30015-7_9},
isbn = {03029743 (ISSN); 978-303230014-0 (ISBN)},
year = {2026},
date = {2026-01-01},
booktitle = {Lect. Notes Comput. Sci.},
volume = {16737 LNCS},
pages = {139–158},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {Advances in artificial intelligence (AI) are changing the role of adaptive instructional systems (AIS) from passive tools, that deliver personalized learning pathways by adapting to student feedback, to active team members and co-learners that dynamically adapt alongside human partners. In this emerging landscape of human–autonomy teaming (HAT), assessing the cognitive states of individuals interacting with highly intelligent systems has become a critical design consideration for improving team performance, collaboration, and learning outcomes. Physiological signals have emerged as a popular method for measurement of cognitive states in the past few decades. However, these signals can be strongly influenced by different user demographics, including age and biological sex. If unaccounted for, these differences could introduce systematic biases into AIS based learning pathways for different demographic groups. These biases can lead to discriminative performance of instructional systems and may also leave them vulnerable to adversarial exploitation. Aside from physiological signal-induced biases, there may also be major behavioural differences between different sex and/or age groups when interacting with and teaming with AIS, thus further confounding cognitive state monitoring. In this survey, we review emerging human-autonomy teaming studies that incorporate physiological data and analyze where biological sex- and age-related physiological or behavioral differences were documented and/or analyzed. We highlight evidence demonstrating that differences do exist during interaction with autonomous systems. We then discuss how biases can accumulate across study design and analysis modelling pipelines, and provide practical guidelines for mitigating these biases in future human-autonomy teaming research and applications. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.},
note = {Journal Abbreviation: Lect. Notes Comput. Sci.},
keywords = {Behavioral research, Bias, Biases, Biomedical signal processing, Cognitive state, Cognitive systems, Human Autonomy Teaming, Instructional system, Intelligent systems, Learning pathway, Learning systems, Personalized learning, Physiological models, Physiological signals, Population statistics, Psychophysiology, State Detection, Student feedback, Surveying, Wearable devices, Wearable technology},
pubstate = {published},
tppubtype = {inproceedings}
}
Advances in artificial intelligence (AI) are changing the role of adaptive instructional systems (AIS) from passive tools, that deliver personalized learning pathways by adapting to student feedback, to active team members and co-learners that dynamically adapt alongside human partners. In this emerging landscape of human–autonomy teaming (HAT), assessing the cognitive states of individuals interacting with highly intelligent systems has become a critical design consideration for improving team performance, collaboration, and learning outcomes. Physiological signals have emerged as a popular method for measurement of cognitive states in the past few decades. However, these signals can be strongly influenced by different user demographics, including age and biological sex. If unaccounted for, these differences could introduce systematic biases into AIS based learning pathways for different demographic groups. These biases can lead to discriminative performance of instructional systems and may also leave them vulnerable to adversarial exploitation. Aside from physiological signal-induced biases, there may also be major behavioural differences between different sex and/or age groups when interacting with and teaming with AIS, thus further confounding cognitive state monitoring. In this survey, we review emerging human-autonomy teaming studies that incorporate physiological data and analyze where biological sex- and age-related physiological or behavioral differences were documented and/or analyzed. We highlight evidence demonstrating that differences do exist during interaction with autonomous systems. We then discuss how biases can accumulate across study design and analysis modelling pipelines, and provide practical guidelines for mitigating these biases in future human-autonomy teaming research and applications. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
3.
Abdollahzadeh, S.; Allili, M. S.; Boulmerka, A.; Lapointe, J. -F.
Visual Safety Mapping for UAV Landings Using Ordinal Regression Networks Journal Article
In: IEEE Transactions on Artificial Intelligence, vol. 7, no. 6, pp. 3146–3159, 2026, ISSN: 26914581 (ISSN).
Abstract | Links | BibTeX | Tags: Aerial vehicle, Air navigation, Aircraft landing, Antennas, Automatic uncrewed aerial vehicle (UAV) navigation, Automatic unmanned aerial vehicle navigation, Autonomous navigation, Behavioral research, Computer vision, decision making, Deep learning, deep ordinal regression, Emergency services, Landing zones, Learning systems, Ordinal regression, regression analysis, Safe landing, Safe landing zone, safe landing zones (SLZs), Semantic segmentation, Semantics, Textures, Three dimensional computer graphics, Unmanned aerial vehicles (UAV), Vehicle navigation, Visual servoing
@article{abdollahzadehVisualSafetyMapping2026,
title = {Visual Safety Mapping for UAV Landings Using Ordinal Regression Networks},
author = {S. Abdollahzadeh and M. S. Allili and A. Boulmerka and J. -F. Lapointe},
url = {https://www.scopus.com/pages/publications/105023324811?origin=resultslist},
doi = {10.1109/TAI.2025.3635093},
issn = {26914581 (ISSN)},
year = {2026},
date = {2026-01-01},
journal = {IEEE Transactions on Artificial Intelligence},
volume = {7},
number = {6},
pages = {3146–3159},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {As uncrewed aerial vehicles (UAVs) see growing use in civilian applications, reliably identifying safe landing zones (SLZs) in varied environments is essential for autonomous navigation and emergency response. Passive vision sensors offer a low cost, lightweight solution for real-time terrain analysis and 3D scene reconstruction, making them ideal for onboard systems. We introduce ordinal regression safe landing zones netwrok (OR-SLZNet), an original deep learning model based on ordinal regression to predict SLZs from UAV imagery. Unlike prior approaches, OR-SLZNet produces dense, multilevel safety maps by jointly leveraging photometric (e.g., color and texture) and geometric cues (e.g., flatness, slope, and depth), assigning each pixel an ordinal safety score that reflects landing suitability. With real-time inference (textbackslashsim0.02 s/frame), the model supports onboard deployment and rapid decision-making in time-critical situations. Extensive experiments on five diverse datasets demonstrate OR-SLZNet effectiveness and strong generalization across a wide range of structural complexities. © 2020 IEEE.},
keywords = {Aerial vehicle, Air navigation, Aircraft landing, Antennas, Automatic uncrewed aerial vehicle (UAV) navigation, Automatic unmanned aerial vehicle navigation, Autonomous navigation, Behavioral research, Computer vision, decision making, Deep learning, deep ordinal regression, Emergency services, Landing zones, Learning systems, Ordinal regression, regression analysis, Safe landing, Safe landing zone, safe landing zones (SLZs), Semantic segmentation, Semantics, Textures, Three dimensional computer graphics, Unmanned aerial vehicles (UAV), Vehicle navigation, Visual servoing},
pubstate = {published},
tppubtype = {article}
}
As uncrewed aerial vehicles (UAVs) see growing use in civilian applications, reliably identifying safe landing zones (SLZs) in varied environments is essential for autonomous navigation and emergency response. Passive vision sensors offer a low cost, lightweight solution for real-time terrain analysis and 3D scene reconstruction, making them ideal for onboard systems. We introduce ordinal regression safe landing zones netwrok (OR-SLZNet), an original deep learning model based on ordinal regression to predict SLZs from UAV imagery. Unlike prior approaches, OR-SLZNet produces dense, multilevel safety maps by jointly leveraging photometric (e.g., color and texture) and geometric cues (e.g., flatness, slope, and depth), assigning each pixel an ordinal safety score that reflects landing suitability. With real-time inference (textbackslashsim0.02 s/frame), the model supports onboard deployment and rapid decision-making in time-critical situations. Extensive experiments on five diverse datasets demonstrate OR-SLZNet effectiveness and strong generalization across a wide range of structural complexities. © 2020 IEEE.
4.
Brideau-Duquette, M.; Côté, S. S. -P.; Boukhalfi, T.; Renaud, P.
Forensic Psychiatry and Big Data: Towards a Cyberphysical System in Service of Clinic, Research and Cybersecurity Proceedings Article
In: B., Boulay; T., Di Mascio; E., Tovar; C., Meinel (Ed.): International Conference on Computer Supported Education, CSEDU - Proceedings, pp. 856–864, Science and Technology Publications, Lda, 2025, ISBN: 21845026 (ISSN); 978-989758746-7 (ISBN), (Journal Abbreviation: International Conference on Computer Supported Education, CSEDU - Proceedings).
Abstract | Links | BibTeX | Tags: Artificial intelligence, Artificial life, Behavioral research, Computer forensics, Cyber security, Cyber-physical systems, Cyberphysical System, Cybersecurity, Data Lake, Data repositories, Extended Reality, Forensic engineering, Forensic psychiatry, Intelligent computing, prediction, Prevention, Research efforts, Social Engineering, Sociology, System in services
@inproceedings{brideau-duquette_forensic_2025,
title = {Forensic Psychiatry and Big Data: Towards a Cyberphysical System in Service of Clinic, Research and Cybersecurity},
author = {M. Brideau-Duquette and S. S. -P. Côté and T. Boukhalfi and P. Renaud},
editor = {Boulay B. and Di Mascio T. and Tovar E. and Meinel C.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105003621528&doi=10.5220%2f0013496100003932&partnerID=40&md5=ee442c9113f1104973b40ef698bc5d7d},
doi = {10.5220/0013496100003932},
isbn = {21845026 (ISSN); 978-989758746-7 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {International Conference on Computer Supported Education, CSEDU - Proceedings},
volume = {1},
pages = {856–864},
publisher = {Science and Technology Publications, Lda},
abstract = {The advent of big data and artificial intelligence has led to the elaboration of computational psychiatry. In parallel, great progress has been made with extended reality (XR) technologies. In this article, we propose to build a forensic cyberphysical system (CPS) that, with a data lake as its computational and data repository core, will support clinical and research efforts in forensic psychiatry, this in both intramural and extramural settings. The proposed CPS requires offender's data (notably clinical, behavioural and physiological), but also emphasises the collection of such data in various XR contexts. The same data would be used to train machine and deep learning, artificial intelligence, algorithms. Beyond the direct feedback these algorithms could give to forensic specialists, they could help build forensic digital twins. They could also serve in the fine tuning of XR usage with offenders. This paper concludes with human-centered cybersecurity concerns and opportunities the same CPS would imply. The proximity between a forensic and XR-supported CPS and social engineering will be addressed, and special consideration will be given to the opportunity for situational awareness training with offenders. We conclude by sketching ethical and implementation challenges that would require future inquiring. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.},
note = {Journal Abbreviation: International Conference on Computer Supported Education, CSEDU - Proceedings},
keywords = {Artificial intelligence, Artificial life, Behavioral research, Computer forensics, Cyber security, Cyber-physical systems, Cyberphysical System, Cybersecurity, Data Lake, Data repositories, Extended Reality, Forensic engineering, Forensic psychiatry, Intelligent computing, prediction, Prevention, Research efforts, Social Engineering, Sociology, System in services},
pubstate = {published},
tppubtype = {inproceedings}
}
The advent of big data and artificial intelligence has led to the elaboration of computational psychiatry. In parallel, great progress has been made with extended reality (XR) technologies. In this article, we propose to build a forensic cyberphysical system (CPS) that, with a data lake as its computational and data repository core, will support clinical and research efforts in forensic psychiatry, this in both intramural and extramural settings. The proposed CPS requires offender's data (notably clinical, behavioural and physiological), but also emphasises the collection of such data in various XR contexts. The same data would be used to train machine and deep learning, artificial intelligence, algorithms. Beyond the direct feedback these algorithms could give to forensic specialists, they could help build forensic digital twins. They could also serve in the fine tuning of XR usage with offenders. This paper concludes with human-centered cybersecurity concerns and opportunities the same CPS would imply. The proximity between a forensic and XR-supported CPS and social engineering will be addressed, and special consideration will be given to the opportunity for situational awareness training with offenders. We conclude by sketching ethical and implementation challenges that would require future inquiring. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.
5.
Nosrati, S.; Motaghi, H.
The AI Complacency Model: Integrating Bounded Rationality and Information Processing Proceedings Article
In: Am. Conf. Inf. Syst., AMCIS, pp. 4590–4599, Association for Information Systems, 2025, ISBN: 979-833132774-3 (ISBN).
Abstract | Links | BibTeX | Tags: AI complacency, AI systems, Artificial intelligence, Behavioral research, Bounded rationality, Cognitive bias, cognitive biases, decision-making, Decisions makings, Heuristic processing, Human computer interaction, Human-AI interaction, information processing, Information systems, Information use, Perceived AI reliability, Reliability theory, Systematic processing, Vigilance
@inproceedings{nosrati_ai_2025,
title = {The AI Complacency Model: Integrating Bounded Rationality and Information Processing},
author = {S. Nosrati and H. Motaghi},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105025349267&partnerID=40&md5=14d676bedd3ea18921ed24a830889a8b},
isbn = {979-833132774-3 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {Am. Conf. Inf. Syst., AMCIS},
volume = {7},
pages = {4590–4599},
publisher = {Association for Information Systems},
abstract = {This study addresses a critical gap in understanding why users exhibit reduced oversight when interacting with generative AI systems despite their known limitations. While existing research documents AI errors across domains, theoretical frameworks explaining the underlying psychological mechanisms remain underdeveloped. We propose a comprehensive model of AI complacency that extends beyond traditional automation complacency theories by integrating bounded rationality constraints with dual-process information processing models. Our framework demonstrates how Perceived AI Reliability triggers a shift from systematic to heuristic information processing, subsequently reducing vigilance and impairing task performance. The relationship between Perceived AI Reliability and information processing is moderated by three key factors derived from bounded rationality theory: knowledge limitations, cognitive processing capabilities, and time constraints. The proposed conceptual model of this study contributes to the literature by identifying unique psychological mechanisms underlying AI complacency, explaining the processing shifts in human-AI interaction, positioning vigilance as a critical mediating mechanism, and introducing the Vigilance-Reliability Matrix as a diagnostic tool for identifying different interaction patterns. This framework offers both theoretical insights and practical guidance for maintaining appropriate human oversight as AI systems become increasingly sophisticated and widespread across domains. Copyright © 2025 by Association for Information Systems (AIS). All rights reserved.},
keywords = {AI complacency, AI systems, Artificial intelligence, Behavioral research, Bounded rationality, Cognitive bias, cognitive biases, decision-making, Decisions makings, Heuristic processing, Human computer interaction, Human-AI interaction, information processing, Information systems, Information use, Perceived AI reliability, Reliability theory, Systematic processing, Vigilance},
pubstate = {published},
tppubtype = {inproceedings}
}
This study addresses a critical gap in understanding why users exhibit reduced oversight when interacting with generative AI systems despite their known limitations. While existing research documents AI errors across domains, theoretical frameworks explaining the underlying psychological mechanisms remain underdeveloped. We propose a comprehensive model of AI complacency that extends beyond traditional automation complacency theories by integrating bounded rationality constraints with dual-process information processing models. Our framework demonstrates how Perceived AI Reliability triggers a shift from systematic to heuristic information processing, subsequently reducing vigilance and impairing task performance. The relationship between Perceived AI Reliability and information processing is moderated by three key factors derived from bounded rationality theory: knowledge limitations, cognitive processing capabilities, and time constraints. The proposed conceptual model of this study contributes to the literature by identifying unique psychological mechanisms underlying AI complacency, explaining the processing shifts in human-AI interaction, positioning vigilance as a critical mediating mechanism, and introducing the Vigilance-Reliability Matrix as a diagnostic tool for identifying different interaction patterns. This framework offers both theoretical insights and practical guidance for maintaining appropriate human oversight as AI systems become increasingly sophisticated and widespread across domains. Copyright © 2025 by Association for Information Systems (AIS). All rights reserved.
6.
Brideau-Duquette, M.; Côté, S. S. -P.; Boukhalfi, T.; Renaud, P.
Forensic Psychiatry and Big Data: Towards a Cyberphysical System in Service of Clinic, Research and Cybersecurity Proceedings Article
In: B., Boulay; T., Di Mascio; E., Tovar; C., Meinel (Ed.): International Conference on Computer Supported Education, CSEDU - Proceedings, pp. 856–864, Science and Technology Publications, Lda, 2025, ISBN: 21845026 (ISSN); 978-989758746-7 (ISBN), (Journal Abbreviation: International Conference on Computer Supported Education, CSEDU - Proceedings).
Abstract | Links | BibTeX | Tags: Artificial intelligence, Artificial life, Behavioral research, Computer forensics, Cyber security, Cyber-physical systems, Cyberphysical System, Cybersecurity, Data Lake, Data repositories, Extended Reality, Forensic engineering, Forensic psychiatry, Intelligent computing, prediction, Prevention, Research efforts, Social Engineering, Sociology, System in services
@inproceedings{brideau-duquetteForensicPsychiatryBig2025a,
title = {Forensic Psychiatry and Big Data: Towards a Cyberphysical System in Service of Clinic, Research and Cybersecurity},
author = {M. Brideau-Duquette and S. S. -P. Côté and T. Boukhalfi and P. Renaud},
editor = {Boulay B. and Di Mascio T. and Tovar E. and Meinel C.},
url = {https://www.scopus.com/pages/publications/105003621528?origin=resultslist},
doi = {10.5220/0013496100003932},
isbn = {21845026 (ISSN); 978-989758746-7 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {International Conference on Computer Supported Education, CSEDU - Proceedings},
volume = {1},
pages = {856–864},
publisher = {Science and Technology Publications, Lda},
abstract = {The advent of big data and artificial intelligence has led to the elaboration of computational psychiatry. In parallel, great progress has been made with extended reality (XR) technologies. In this article, we propose to build a forensic cyberphysical system (CPS) that, with a data lake as its computational and data repository core, will support clinical and research efforts in forensic psychiatry, this in both intramural and extramural settings. The proposed CPS requires offender's data (notably clinical, behavioural and physiological), but also emphasises the collection of such data in various XR contexts. The same data would be used to train machine and deep learning, artificial intelligence, algorithms. Beyond the direct feedback these algorithms could give to forensic specialists, they could help build forensic digital twins. They could also serve in the fine tuning of XR usage with offenders. This paper concludes with human-centered cybersecurity concerns and opportunities the same CPS would imply. The proximity between a forensic and XR-supported CPS and social engineering will be addressed, and special consideration will be given to the opportunity for situational awareness training with offenders. We conclude by sketching ethical and implementation challenges that would require future inquiring. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.},
note = {Journal Abbreviation: International Conference on Computer Supported Education, CSEDU - Proceedings},
keywords = {Artificial intelligence, Artificial life, Behavioral research, Computer forensics, Cyber security, Cyber-physical systems, Cyberphysical System, Cybersecurity, Data Lake, Data repositories, Extended Reality, Forensic engineering, Forensic psychiatry, Intelligent computing, prediction, Prevention, Research efforts, Social Engineering, Sociology, System in services},
pubstate = {published},
tppubtype = {inproceedings}
}
The advent of big data and artificial intelligence has led to the elaboration of computational psychiatry. In parallel, great progress has been made with extended reality (XR) technologies. In this article, we propose to build a forensic cyberphysical system (CPS) that, with a data lake as its computational and data repository core, will support clinical and research efforts in forensic psychiatry, this in both intramural and extramural settings. The proposed CPS requires offender's data (notably clinical, behavioural and physiological), but also emphasises the collection of such data in various XR contexts. The same data would be used to train machine and deep learning, artificial intelligence, algorithms. Beyond the direct feedback these algorithms could give to forensic specialists, they could help build forensic digital twins. They could also serve in the fine tuning of XR usage with offenders. This paper concludes with human-centered cybersecurity concerns and opportunities the same CPS would imply. The proximity between a forensic and XR-supported CPS and social engineering will be addressed, and special consideration will be given to the opportunity for situational awareness training with offenders. We conclude by sketching ethical and implementation challenges that would require future inquiring. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.
7.
Nosrati, S.; Motaghi, H.
The AI Complacency Model: Integrating Bounded Rationality and Information Processing Proceedings Article
In: Am. Conf. Inf. Syst., AMCIS, pp. 4590–4599, Association for Information Systems, 2025, ISBN: 979-833132774-3 (ISBN), (Journal Abbreviation: Am. Conf. Inf. Syst., AMCIS).
Abstract | Links | BibTeX | Tags: AI complacency, AI systems, Artificial intelligence, Behavioral research, Bounded rationality, Cognitive bias, cognitive biases, decision-making, Decisions makings, Heuristic processing, Human computer interaction, Human-AI interaction, information processing, Information systems, Information use, Perceived AI reliability, Reliability theory, Systematic processing, Vigilance
@inproceedings{nosratiAIComplacencyModel2025,
title = {The AI Complacency Model: Integrating Bounded Rationality and Information Processing},
author = {S. Nosrati and H. Motaghi},
url = {https://www.scopus.com/pages/publications/105025349267?origin=resultslist},
isbn = {979-833132774-3 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {Am. Conf. Inf. Syst., AMCIS},
volume = {7},
pages = {4590–4599},
publisher = {Association for Information Systems},
abstract = {This study addresses a critical gap in understanding why users exhibit reduced oversight when interacting with generative AI systems despite their known limitations. While existing research documents AI errors across domains, theoretical frameworks explaining the underlying psychological mechanisms remain underdeveloped. We propose a comprehensive model of AI complacency that extends beyond traditional automation complacency theories by integrating bounded rationality constraints with dual-process information processing models. Our framework demonstrates how Perceived AI Reliability triggers a shift from systematic to heuristic information processing, subsequently reducing vigilance and impairing task performance. The relationship between Perceived AI Reliability and information processing is moderated by three key factors derived from bounded rationality theory: knowledge limitations, cognitive processing capabilities, and time constraints. The proposed conceptual model of this study contributes to the literature by identifying unique psychological mechanisms underlying AI complacency, explaining the processing shifts in human-AI interaction, positioning vigilance as a critical mediating mechanism, and introducing the Vigilance-Reliability Matrix as a diagnostic tool for identifying different interaction patterns. This framework offers both theoretical insights and practical guidance for maintaining appropriate human oversight as AI systems become increasingly sophisticated and widespread across domains. Copyright © 2025 by Association for Information Systems (AIS). All rights reserved.},
note = {Journal Abbreviation: Am. Conf. Inf. Syst., AMCIS},
keywords = {AI complacency, AI systems, Artificial intelligence, Behavioral research, Bounded rationality, Cognitive bias, cognitive biases, decision-making, Decisions makings, Heuristic processing, Human computer interaction, Human-AI interaction, information processing, Information systems, Information use, Perceived AI reliability, Reliability theory, Systematic processing, Vigilance},
pubstate = {published},
tppubtype = {inproceedings}
}
This study addresses a critical gap in understanding why users exhibit reduced oversight when interacting with generative AI systems despite their known limitations. While existing research documents AI errors across domains, theoretical frameworks explaining the underlying psychological mechanisms remain underdeveloped. We propose a comprehensive model of AI complacency that extends beyond traditional automation complacency theories by integrating bounded rationality constraints with dual-process information processing models. Our framework demonstrates how Perceived AI Reliability triggers a shift from systematic to heuristic information processing, subsequently reducing vigilance and impairing task performance. The relationship between Perceived AI Reliability and information processing is moderated by three key factors derived from bounded rationality theory: knowledge limitations, cognitive processing capabilities, and time constraints. The proposed conceptual model of this study contributes to the literature by identifying unique psychological mechanisms underlying AI complacency, explaining the processing shifts in human-AI interaction, positioning vigilance as a critical mediating mechanism, and introducing the Vigilance-Reliability Matrix as a diagnostic tool for identifying different interaction patterns. This framework offers both theoretical insights and practical guidance for maintaining appropriate human oversight as AI systems become increasingly sophisticated and widespread across domains. Copyright © 2025 by Association for Information Systems (AIS). All rights reserved.
8.
Nosrati, S.; Motaghi, H.
The AI Complacency Model: Integrating Bounded Rationality and Information Processing Proceedings Article
In: Am. Conf. Inf. Syst., AMCIS, pp. 4590–4599, Association for Information Systems, 2025, ISBN: 979-833132774-3 (ISBN).
Abstract | Links | BibTeX | Tags: AI complacency, AI systems, Artificial intelligence, Behavioral research, Bounded rationality, Cognitive bias, cognitive biases, decision-making, Decisions makings, Heuristic processing, Human computer interaction, Human-AI interaction, information processing, Information systems, Information use, Perceived AI reliability, Reliability theory, Systematic processing, Vigilance
@inproceedings{nosratiAIComplacencyModel2025a,
title = {The AI Complacency Model: Integrating Bounded Rationality and Information Processing},
author = {S. Nosrati and H. Motaghi},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105025349267&partnerID=40&md5=14d676bedd3ea18921ed24a830889a8b},
isbn = {979-833132774-3 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {Am. Conf. Inf. Syst., AMCIS},
volume = {7},
pages = {4590–4599},
publisher = {Association for Information Systems},
abstract = {This study addresses a critical gap in understanding why users exhibit reduced oversight when interacting with generative AI systems despite their known limitations. While existing research documents AI errors across domains, theoretical frameworks explaining the underlying psychological mechanisms remain underdeveloped. We propose a comprehensive model of AI complacency that extends beyond traditional automation complacency theories by integrating bounded rationality constraints with dual-process information processing models. Our framework demonstrates how Perceived AI Reliability triggers a shift from systematic to heuristic information processing, subsequently reducing vigilance and impairing task performance. The relationship between Perceived AI Reliability and information processing is moderated by three key factors derived from bounded rationality theory: knowledge limitations, cognitive processing capabilities, and time constraints. The proposed conceptual model of this study contributes to the literature by identifying unique psychological mechanisms underlying AI complacency, explaining the processing shifts in human-AI interaction, positioning vigilance as a critical mediating mechanism, and introducing the Vigilance-Reliability Matrix as a diagnostic tool for identifying different interaction patterns. This framework offers both theoretical insights and practical guidance for maintaining appropriate human oversight as AI systems become increasingly sophisticated and widespread across domains. Copyright © 2025 by Association for Information Systems (AIS). All rights reserved.},
keywords = {AI complacency, AI systems, Artificial intelligence, Behavioral research, Bounded rationality, Cognitive bias, cognitive biases, decision-making, Decisions makings, Heuristic processing, Human computer interaction, Human-AI interaction, information processing, Information systems, Information use, Perceived AI reliability, Reliability theory, Systematic processing, Vigilance},
pubstate = {published},
tppubtype = {inproceedings}
}
This study addresses a critical gap in understanding why users exhibit reduced oversight when interacting with generative AI systems despite their known limitations. While existing research documents AI errors across domains, theoretical frameworks explaining the underlying psychological mechanisms remain underdeveloped. We propose a comprehensive model of AI complacency that extends beyond traditional automation complacency theories by integrating bounded rationality constraints with dual-process information processing models. Our framework demonstrates how Perceived AI Reliability triggers a shift from systematic to heuristic information processing, subsequently reducing vigilance and impairing task performance. The relationship between Perceived AI Reliability and information processing is moderated by three key factors derived from bounded rationality theory: knowledge limitations, cognitive processing capabilities, and time constraints. The proposed conceptual model of this study contributes to the literature by identifying unique psychological mechanisms underlying AI complacency, explaining the processing shifts in human-AI interaction, positioning vigilance as a critical mediating mechanism, and introducing the Vigilance-Reliability Matrix as a diagnostic tool for identifying different interaction patterns. This framework offers both theoretical insights and practical guidance for maintaining appropriate human oversight as AI systems become increasingly sophisticated and widespread across domains. Copyright © 2025 by Association for Information Systems (AIS). All rights reserved.
9.
Brideau-Duquette, M.; Côté, S. S. -P.; Boukhalfi, T.; Renaud, P.
Forensic Psychiatry and Big Data: Towards a Cyberphysical System in Service of Clinic, Research and Cybersecurity Proceedings Article
In: B., Boulay; T., Di Mascio; E., Tovar; C., Meinel (Ed.): International Conference on Computer Supported Education, CSEDU - Proceedings, pp. 856–864, Science and Technology Publications, Lda, 2025, ISBN: 21845026 (ISSN); 978-989758746-7 (ISBN), (Journal Abbreviation: International Conference on Computer Supported Education, CSEDU - Proceedings).
Abstract | Links | BibTeX | Tags: Artificial intelligence, Artificial life, Behavioral research, Computer forensics, Cyber security, Cyber-physical systems, Cyberphysical System, Cybersecurity, Data Lake, Data repositories, Extended Reality, Forensic engineering, Forensic psychiatry, Intelligent computing, prediction, Prevention, Research efforts, Social Engineering, Sociology, System in services
@inproceedings{brideau-duquetteForensicPsychiatryBig2025,
title = {Forensic Psychiatry and Big Data: Towards a Cyberphysical System in Service of Clinic, Research and Cybersecurity},
author = {M. Brideau-Duquette and S. S. -P. Côté and T. Boukhalfi and P. Renaud},
editor = {Boulay B. and Di Mascio T. and Tovar E. and Meinel C.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105003621528&doi=10.5220%2f0013496100003932&partnerID=40&md5=ee442c9113f1104973b40ef698bc5d7d},
doi = {10.5220/0013496100003932},
isbn = {21845026 (ISSN); 978-989758746-7 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {International Conference on Computer Supported Education, CSEDU - Proceedings},
volume = {1},
pages = {856–864},
publisher = {Science and Technology Publications, Lda},
abstract = {The advent of big data and artificial intelligence has led to the elaboration of computational psychiatry. In parallel, great progress has been made with extended reality (XR) technologies. In this article, we propose to build a forensic cyberphysical system (CPS) that, with a data lake as its computational and data repository core, will support clinical and research efforts in forensic psychiatry, this in both intramural and extramural settings. The proposed CPS requires offender's data (notably clinical, behavioural and physiological), but also emphasises the collection of such data in various XR contexts. The same data would be used to train machine and deep learning, artificial intelligence, algorithms. Beyond the direct feedback these algorithms could give to forensic specialists, they could help build forensic digital twins. They could also serve in the fine tuning of XR usage with offenders. This paper concludes with human-centered cybersecurity concerns and opportunities the same CPS would imply. The proximity between a forensic and XR-supported CPS and social engineering will be addressed, and special consideration will be given to the opportunity for situational awareness training with offenders. We conclude by sketching ethical and implementation challenges that would require future inquiring. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.},
note = {Journal Abbreviation: International Conference on Computer Supported Education, CSEDU - Proceedings},
keywords = {Artificial intelligence, Artificial life, Behavioral research, Computer forensics, Cyber security, Cyber-physical systems, Cyberphysical System, Cybersecurity, Data Lake, Data repositories, Extended Reality, Forensic engineering, Forensic psychiatry, Intelligent computing, prediction, Prevention, Research efforts, Social Engineering, Sociology, System in services},
pubstate = {published},
tppubtype = {inproceedings}
}
The advent of big data and artificial intelligence has led to the elaboration of computational psychiatry. In parallel, great progress has been made with extended reality (XR) technologies. In this article, we propose to build a forensic cyberphysical system (CPS) that, with a data lake as its computational and data repository core, will support clinical and research efforts in forensic psychiatry, this in both intramural and extramural settings. The proposed CPS requires offender's data (notably clinical, behavioural and physiological), but also emphasises the collection of such data in various XR contexts. The same data would be used to train machine and deep learning, artificial intelligence, algorithms. Beyond the direct feedback these algorithms could give to forensic specialists, they could help build forensic digital twins. They could also serve in the fine tuning of XR usage with offenders. This paper concludes with human-centered cybersecurity concerns and opportunities the same CPS would imply. The proximity between a forensic and XR-supported CPS and social engineering will be addressed, and special consideration will be given to the opportunity for situational awareness training with offenders. We conclude by sketching ethical and implementation challenges that would require future inquiring. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.
10.
Davoust, A.; Rovatsos, M.
Social contracts for non-cooperative games Proceedings Article
In: AIES 2020 - Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 43–49, Association for Computing Machinery, Inc, 2020, ISBN: 978-1-4503-7110-0.
Abstract | Links | BibTeX | Tags: Agent society, Agents, Behavioral research, Ethical aspects, Game theory, Game-theoretic, Moral philosophy, Noncooperative game, Selfish behaviours, Social benefits, Social contract, Social welfare
@inproceedings{davoust_social_2020,
title = {Social contracts for non-cooperative games},
author = {A. Davoust and M. Rovatsos},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85082175399&doi=10.1145%2f3375627.3375829&partnerID=40&md5=972ba2201a1c2450895935dc03ec39b9},
doi = {10.1145/3375627.3375829},
isbn = {978-1-4503-7110-0},
year = {2020},
date = {2020-01-01},
booktitle = {AIES 2020 - Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society},
pages = {43–49},
publisher = {Association for Computing Machinery, Inc},
abstract = {In future agent societies, we might see AI systems engaging in selfish, calculated behavior, furthering their owners' interests instead of socially desirable outcomes. How can we promote morally sound behaviour in such settings, in order to obtain more desirable outcomes? A solution from moral philosophy is the concept of a social contract, a set of rules that people would voluntarily commit to in order to obtain better outcomes than those brought by anarchy. We adapt this concept to a game-theoretic setting, to systematically modify the payoffs of a non-cooperative game, so that agents will rationally pursue socially desirable outcomes. We show that for any game, a suitable social contract can be designed to produce an optimal outcome in terms of social welfare. We then investigate the limitations of applying this approach to alternative moral objectives, and establish that, for any alternative moral objective that is significantly different from social welfare, there are games for which no such social contract will be feasible that produces non-negligible social benefit compared to collective selfish behaviour. © 2020 Copyright held by the owner/author(s).},
keywords = {Agent society, Agents, Behavioral research, Ethical aspects, Game theory, Game-theoretic, Moral philosophy, Noncooperative game, Selfish behaviours, Social benefits, Social contract, Social welfare},
pubstate = {published},
tppubtype = {inproceedings}
}
In future agent societies, we might see AI systems engaging in selfish, calculated behavior, furthering their owners' interests instead of socially desirable outcomes. How can we promote morally sound behaviour in such settings, in order to obtain more desirable outcomes? A solution from moral philosophy is the concept of a social contract, a set of rules that people would voluntarily commit to in order to obtain better outcomes than those brought by anarchy. We adapt this concept to a game-theoretic setting, to systematically modify the payoffs of a non-cooperative game, so that agents will rationally pursue socially desirable outcomes. We show that for any game, a suitable social contract can be designed to produce an optimal outcome in terms of social welfare. We then investigate the limitations of applying this approach to alternative moral objectives, and establish that, for any alternative moral objective that is significantly different from social welfare, there are games for which no such social contract will be feasible that produces non-negligible social benefit compared to collective selfish behaviour. © 2020 Copyright held by the owner/author(s).