Slide
Centre Interdisciplinaire
de Recherche et d’Innovation
en Cybersécurité et Société
de Recherche et d’Innovation
en Cybersécurité et Société
1.
Côté, L.; Lamontagne, J.; Bellerose, A.; Blais, C.; Fiset, D.
The eyes are central to face detection: revisiting the foundations of face processing Journal Article
In: Vision Research, vol. 243, 2026, ISSN: 00426989 (ISSN).
Abstract | Links | BibTeX | Tags: adult, article, Black person, Bubbles, Categorization, Caucasian, Detection, emotion assessment, Faces, Facial Recognition, facies, female, human, human experiment, Image analysis, information processing, Information use, male, Noise, normal human, perception, Prosopagnosia, spatial frequency discrimination, task performance, visual discrimination, Young Adult
@article{cote_eyes_2026,
title = {The eyes are central to face detection: revisiting the foundations of face processing},
author = {L. Côté and J. Lamontagne and A. Bellerose and C. Blais and D. Fiset},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105030389147&doi=10.1016%2Fj.visres.2026.108785&partnerID=40&md5=752aa5d9923ac60539e36118ad41e1e6},
doi = {10.1016/j.visres.2026.108785},
issn = {00426989 (ISSN)},
year = {2026},
date = {2026-01-01},
journal = {Vision Research},
volume = {243},
abstract = {Face detection feels effortless, yet it requires finely tuned computations to extract socially meaningful signals from the visual stream. Here, we used the Bubbles method to isolate the facial features and spatial frequency information that support face categorization. Across three experiments varying in task demands and visual context, the eye region consistently emerged as the most diagnostic source of information, particularly in high spatial frequencies. This finding held whether participants distinguished faces from noise, from non-face objects, or from real-world categories—suggesting that the eyes serve as an anchor point for categorization across contexts. Strikingly, this diagnostic profile mirrors that found in face identification tasks, implying that detection and recognition may rely on shared perceptual mechanisms rather than sequential, independent processes. This overlap sheds light on longstanding ambiguities in the prosopagnosia literature, indicating that detection impairments found in patients may stem from a broader failure to extract critical eye information. More broadly, our results invite a rethinking of the early stages of face processing, suggesting that detection already involves selective use of diagnostic facial features that supports recognition, emotional decoding, and social perception. © 2026 The Author(s).},
keywords = {adult, article, Black person, Bubbles, Categorization, Caucasian, Detection, emotion assessment, Faces, Facial Recognition, facies, female, human, human experiment, Image analysis, information processing, Information use, male, Noise, normal human, perception, Prosopagnosia, spatial frequency discrimination, task performance, visual discrimination, Young Adult},
pubstate = {published},
tppubtype = {article}
}
Face detection feels effortless, yet it requires finely tuned computations to extract socially meaningful signals from the visual stream. Here, we used the Bubbles method to isolate the facial features and spatial frequency information that support face categorization. Across three experiments varying in task demands and visual context, the eye region consistently emerged as the most diagnostic source of information, particularly in high spatial frequencies. This finding held whether participants distinguished faces from noise, from non-face objects, or from real-world categories—suggesting that the eyes serve as an anchor point for categorization across contexts. Strikingly, this diagnostic profile mirrors that found in face identification tasks, implying that detection and recognition may rely on shared perceptual mechanisms rather than sequential, independent processes. This overlap sheds light on longstanding ambiguities in the prosopagnosia literature, indicating that detection impairments found in patients may stem from a broader failure to extract critical eye information. More broadly, our results invite a rethinking of the early stages of face processing, suggesting that detection already involves selective use of diagnostic facial features that supports recognition, emotional decoding, and social perception. © 2026 The Author(s).
2.
Irikefe, P. O.; Falahat, M.; Zainudin, A. D.; Motaghi, H.
In: Journal of Cleaner Production, vol. 574, 2026, ISSN: 09596526 (ISSN).
Abstract | Links | BibTeX | Tags: Digital readiness, Dynamic capabilities theory, Dynamic capability theory, Dynamics capability, Efficiency, Entrepreneurial leadership, Environmental management, Financial inclusion, Financial inclusions, Information use, Investments, Management efficiency, Resource allocation, Resource management, Resource management efficiency, Responsible Consumption and production (SDG 12), sustainable development, Triple Bottom Line, Triple-bottom-line sustainability
@article{irikefeEntrepreneurialLeadershipResource2026,
title = {Entrepreneurial leadership and resource management efficiency as necessary conditions for SME sustainability: A moderated mediation analysis of digital readiness and financial inclusion},
author = {P. O. Irikefe and M. Falahat and A. D. Zainudin and H. Motaghi},
url = {https://www.scopus.com/pages/publications/105046462855?origin=resultslist},
doi = {10.1016/j.jclepro.2026.149116},
issn = {09596526 (ISSN)},
year = {2026},
date = {2026-01-01},
journal = {Journal of Cleaner Production},
volume = {574},
publisher = {Elsevier Ltd},
abstract = {Small and medium enterprises (SMEs) are central to employment and value creation, yet pressures from sustainability transition expose endemic financing frictions and uneven digital readiness (DR), particularly in emerging markets. This study examines how SME capabilities viz.: entrepreneurial leadership (EL) and resource management efficiency (RME) associate with triple-bottom-line (TBL) sustainability across economic (ECOS), environmental (ENVS), and social (SOCS) pillars, whether firm-level financial inclusion (FI) statistically mediates capability-to-TBL associations, and whether DR conditions these associations. Using survey data from 429 Malaysian SMEs, we apply a sequential analytics design: Partial Least Square-Structural Equation Modelling to test direct, mediated, and interaction effects; followed by Necessary Condition Analysis (NCA) and Importance-Performance Map Analysis in the bid to identify bottlenecks and practical priorities. Results show that EL is positively associated with ECOS, ENVS, and SOCS; whereas RME is strongly positively associated with ECOS and ENVS but is not significant for SOCS. FI is TBL pillar-asymmetric: it bolsters ENVS and SOCS but exhibits a negative effect on ECOS, and the mediated effects through FI mirror the pattern. DR significantly strengthens the FI to TBL pathways, yet conditional indirect effects indicate that high DR can neutralise FI's economic drag without reliably improving ECOS. NCA sharpens the conclusion: high ECOS is unattainable without RME, while high ENVS and SOCS require EL-RME-FI jointly; DR is not a robust necessity condition. Practically, SMEs should tighten efficiency routines first, mobilise leadership for adoption, channel finance through verified upgrading milestones and invest in DR only where it improves monitoring, traceability and compliance. Copyright © 2026. Published by Elsevier Ltd.},
keywords = {Digital readiness, Dynamic capabilities theory, Dynamic capability theory, Dynamics capability, Efficiency, Entrepreneurial leadership, Environmental management, Financial inclusion, Financial inclusions, Information use, Investments, Management efficiency, Resource allocation, Resource management, Resource management efficiency, Responsible Consumption and production (SDG 12), sustainable development, Triple Bottom Line, Triple-bottom-line sustainability},
pubstate = {published},
tppubtype = {article}
}
Small and medium enterprises (SMEs) are central to employment and value creation, yet pressures from sustainability transition expose endemic financing frictions and uneven digital readiness (DR), particularly in emerging markets. This study examines how SME capabilities viz.: entrepreneurial leadership (EL) and resource management efficiency (RME) associate with triple-bottom-line (TBL) sustainability across economic (ECOS), environmental (ENVS), and social (SOCS) pillars, whether firm-level financial inclusion (FI) statistically mediates capability-to-TBL associations, and whether DR conditions these associations. Using survey data from 429 Malaysian SMEs, we apply a sequential analytics design: Partial Least Square-Structural Equation Modelling to test direct, mediated, and interaction effects; followed by Necessary Condition Analysis (NCA) and Importance-Performance Map Analysis in the bid to identify bottlenecks and practical priorities. Results show that EL is positively associated with ECOS, ENVS, and SOCS; whereas RME is strongly positively associated with ECOS and ENVS but is not significant for SOCS. FI is TBL pillar-asymmetric: it bolsters ENVS and SOCS but exhibits a negative effect on ECOS, and the mediated effects through FI mirror the pattern. DR significantly strengthens the FI to TBL pathways, yet conditional indirect effects indicate that high DR can neutralise FI's economic drag without reliably improving ECOS. NCA sharpens the conclusion: high ECOS is unattainable without RME, while high ENVS and SOCS require EL-RME-FI jointly; DR is not a robust necessity condition. Practically, SMEs should tighten efficiency routines first, mobilise leadership for adoption, channel finance through verified upgrading milestones and invest in DR only where it improves monitoring, traceability and compliance. Copyright © 2026. Published by Elsevier Ltd.
3.
Côté, L.; Lamontagne, J.; Bellerose, A.; Blais, C.; Fiset, D.
The eyes are central to face detection: revisiting the foundations of face processing Journal Article
In: Vision Research, vol. 243, 2026, ISSN: 00426989 (ISSN).
Abstract | Links | BibTeX | Tags: adult, article, Black person, Bubbles, Categorization, Caucasian, Detection, emotion assessment, Eye, Face, Faces, Facial Recognition, facies, female, human, human experiment, Humans, Image analysis, information processing, Information use, male, Noise, normal human, Pattern Recognition, perception, Photic Stimulation, photostimulation, physiology, procedures, Prosopagnosia, psychology, recognition, Social Perception, spatial frequency discrimination, task performance, Visual, visual discrimination, visual pattern recognition, Young Adult
@article{coteEyesAreCentral2026,
title = {The eyes are central to face detection: revisiting the foundations of face processing},
author = {L. Côté and J. Lamontagne and A. Bellerose and C. Blais and D. Fiset},
url = {https://www.scopus.com/pages/publications/105030389147?origin=resultslist},
doi = {10.1016/j.visres.2026.108785},
issn = {00426989 (ISSN)},
year = {2026},
date = {2026-01-01},
journal = {Vision Research},
volume = {243},
publisher = {Elsevier Ltd},
abstract = {Face detection feels effortless, yet it requires finely tuned computations to extract socially meaningful signals from the visual stream. Here, we used the Bubbles method to isolate the facial features and spatial frequency information that support face categorization. Across three experiments varying in task demands and visual context, the eye region consistently emerged as the most diagnostic source of information, particularly in high spatial frequencies. This finding held whether participants distinguished faces from noise, from non-face objects, or from real-world categories—suggesting that the eyes serve as an anchor point for categorization across contexts. Strikingly, this diagnostic profile mirrors that found in face identification tasks, implying that detection and recognition may rely on shared perceptual mechanisms rather than sequential, independent processes. This overlap sheds light on longstanding ambiguities in the prosopagnosia literature, indicating that detection impairments found in patients may stem from a broader failure to extract critical eye information. More broadly, our results invite a rethinking of the early stages of face processing, suggesting that detection already involves selective use of diagnostic facial features that supports recognition, emotional decoding, and social perception. © 2026 The Author(s).},
keywords = {adult, article, Black person, Bubbles, Categorization, Caucasian, Detection, emotion assessment, Eye, Face, Faces, Facial Recognition, facies, female, human, human experiment, Humans, Image analysis, information processing, Information use, male, Noise, normal human, Pattern Recognition, perception, Photic Stimulation, photostimulation, physiology, procedures, Prosopagnosia, psychology, recognition, Social Perception, spatial frequency discrimination, task performance, Visual, visual discrimination, visual pattern recognition, Young Adult},
pubstate = {published},
tppubtype = {article}
}
Face detection feels effortless, yet it requires finely tuned computations to extract socially meaningful signals from the visual stream. Here, we used the Bubbles method to isolate the facial features and spatial frequency information that support face categorization. Across three experiments varying in task demands and visual context, the eye region consistently emerged as the most diagnostic source of information, particularly in high spatial frequencies. This finding held whether participants distinguished faces from noise, from non-face objects, or from real-world categories—suggesting that the eyes serve as an anchor point for categorization across contexts. Strikingly, this diagnostic profile mirrors that found in face identification tasks, implying that detection and recognition may rely on shared perceptual mechanisms rather than sequential, independent processes. This overlap sheds light on longstanding ambiguities in the prosopagnosia literature, indicating that detection impairments found in patients may stem from a broader failure to extract critical eye information. More broadly, our results invite a rethinking of the early stages of face processing, suggesting that detection already involves selective use of diagnostic facial features that supports recognition, emotional decoding, and social perception. © 2026 The Author(s).
4.
Côté, L.; Lamontagne, J.; Bellerose, A.; Blais, C.; Fiset, D.
The eyes are central to face detection: revisiting the foundations of face processing Journal Article
In: Vision Research, vol. 243, 2026, ISSN: 00426989 (ISSN).
Abstract | Links | BibTeX | Tags: adult, article, Black person, Bubbles, Categorization, Caucasian, Detection, emotion assessment, Faces, Facial Recognition, facies, female, human, human experiment, Image analysis, information processing, Information use, male, Noise, normal human, perception, Prosopagnosia, spatial frequency discrimination, task performance, visual discrimination, Young Adult
@article{coteEyesAreCentral2026a,
title = {The eyes are central to face detection: revisiting the foundations of face processing},
author = {L. Côté and J. Lamontagne and A. Bellerose and C. Blais and D. Fiset},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105030389147&doi=10.1016%2Fj.visres.2026.108785&partnerID=40&md5=752aa5d9923ac60539e36118ad41e1e6},
doi = {10.1016/j.visres.2026.108785},
issn = {00426989 (ISSN)},
year = {2026},
date = {2026-01-01},
journal = {Vision Research},
volume = {243},
abstract = {Face detection feels effortless, yet it requires finely tuned computations to extract socially meaningful signals from the visual stream. Here, we used the Bubbles method to isolate the facial features and spatial frequency information that support face categorization. Across three experiments varying in task demands and visual context, the eye region consistently emerged as the most diagnostic source of information, particularly in high spatial frequencies. This finding held whether participants distinguished faces from noise, from non-face objects, or from real-world categories—suggesting that the eyes serve as an anchor point for categorization across contexts. Strikingly, this diagnostic profile mirrors that found in face identification tasks, implying that detection and recognition may rely on shared perceptual mechanisms rather than sequential, independent processes. This overlap sheds light on longstanding ambiguities in the prosopagnosia literature, indicating that detection impairments found in patients may stem from a broader failure to extract critical eye information. More broadly, our results invite a rethinking of the early stages of face processing, suggesting that detection already involves selective use of diagnostic facial features that supports recognition, emotional decoding, and social perception. © 2026 The Author(s).},
keywords = {adult, article, Black person, Bubbles, Categorization, Caucasian, Detection, emotion assessment, Faces, Facial Recognition, facies, female, human, human experiment, Image analysis, information processing, Information use, male, Noise, normal human, perception, Prosopagnosia, spatial frequency discrimination, task performance, visual discrimination, Young Adult},
pubstate = {published},
tppubtype = {article}
}
Face detection feels effortless, yet it requires finely tuned computations to extract socially meaningful signals from the visual stream. Here, we used the Bubbles method to isolate the facial features and spatial frequency information that support face categorization. Across three experiments varying in task demands and visual context, the eye region consistently emerged as the most diagnostic source of information, particularly in high spatial frequencies. This finding held whether participants distinguished faces from noise, from non-face objects, or from real-world categories—suggesting that the eyes serve as an anchor point for categorization across contexts. Strikingly, this diagnostic profile mirrors that found in face identification tasks, implying that detection and recognition may rely on shared perceptual mechanisms rather than sequential, independent processes. This overlap sheds light on longstanding ambiguities in the prosopagnosia literature, indicating that detection impairments found in patients may stem from a broader failure to extract critical eye information. More broadly, our results invite a rethinking of the early stages of face processing, suggesting that detection already involves selective use of diagnostic facial features that supports recognition, emotional decoding, and social perception. © 2026 The Author(s).
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.
Gagnon, S.
"Use of AI to Fight Disinformation and Corruption" Proceedings Article
In: Am. Conf. Inf. Syst., AMCIS, pp. 4853–4854, Association for Information Systems, 2025, ISBN: 979-833132774-3 (ISBN).
Abstract | Links | BibTeX | Tags: Action research, Audio videos, Copyrights, Crime, Due diligence, Information leakage, Information use, Institutional change, Integrated strategy, Knowledge graph, Knowledge graphs, Open source intelligence, Open systems, Research advances
@inproceedings{gagnon_use_2025,
title = {"Use of AI to Fight Disinformation and Corruption"},
author = {S. Gagnon},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105025353954&partnerID=40&md5=fc3ca73e0de02b06fd218e03588f3e22},
isbn = {979-833132774-3 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {Am. Conf. Inf. Syst., AMCIS},
volume = {7},
pages = {4853–4854},
publisher = {Association for Information Systems},
abstract = {Disinformation (false information disseminated with the intent to cause harm) takes many forms, including fake news, audio-video impersonations, data falsification, and more. It is linked to allegations of corruption and fraud against elected officials, which, once disambiguated (whether true or false), will in all cases require immediate action to preserve democracy. An integrated strategy to combat disinformation and corruption is needed, requiring constant vigilance, due diligence, and open-source intelligence (OSINT) collaboration. Our PDS will explore how AI, especially LLMs and Knowledge Graphs, can serve this purpose and automate the necessary means to combat hybrid threats to democracy. The PDS will cover the following issues and latest research advances, allowing also interactive roundtables to ensure participants can contribute insight to the debate: 1. Presentation: fighting jointly against disinformation and corruption 2. Roundtable: what innovations are most effective in combating hybrid threats? 3. Demo: ai and knowledge graphs to disambiguate allegations against elected officials 4. Roundtable: what institutional changes are required to fight hybrid threats? 5. Conclusion: action research helping parliamentary institutions develop AI capabilities. Copyright © 2025 by Association for Information Systems (AIS). All rights reserved.},
keywords = {Action research, Audio videos, Copyrights, Crime, Due diligence, Information leakage, Information use, Institutional change, Integrated strategy, Knowledge graph, Knowledge graphs, Open source intelligence, Open systems, Research advances},
pubstate = {published},
tppubtype = {inproceedings}
}
Disinformation (false information disseminated with the intent to cause harm) takes many forms, including fake news, audio-video impersonations, data falsification, and more. It is linked to allegations of corruption and fraud against elected officials, which, once disambiguated (whether true or false), will in all cases require immediate action to preserve democracy. An integrated strategy to combat disinformation and corruption is needed, requiring constant vigilance, due diligence, and open-source intelligence (OSINT) collaboration. Our PDS will explore how AI, especially LLMs and Knowledge Graphs, can serve this purpose and automate the necessary means to combat hybrid threats to democracy. The PDS will cover the following issues and latest research advances, allowing also interactive roundtables to ensure participants can contribute insight to the debate: 1. Presentation: fighting jointly against disinformation and corruption 2. Roundtable: what innovations are most effective in combating hybrid threats? 3. Demo: ai and knowledge graphs to disambiguate allegations against elected officials 4. Roundtable: what institutional changes are required to fight hybrid threats? 5. Conclusion: action research helping parliamentary institutions develop AI capabilities. Copyright © 2025 by Association for Information Systems (AIS). All rights reserved.
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.
Gagnon, S.
"Use of AI to Fight Disinformation and Corruption" Proceedings Article
In: Am. Conf. Inf. Syst., AMCIS, pp. 4853–4854, Association for Information Systems, 2025, ISBN: 979-833132774-3 (ISBN), (Journal Abbreviation: Am. Conf. Inf. Syst., AMCIS).
Abstract | Links | BibTeX | Tags: Action research, Audio videos, Copyrights, Crime, Due diligence, Information leakage, Information use, Institutional change, Integrated strategy, Knowledge graph, Knowledge graphs, Open source intelligence, Open systems, Research advances
@inproceedings{gagnonUseAIFight2025,
title = {"Use of AI to Fight Disinformation and Corruption"},
author = {S. Gagnon},
url = {https://www.scopus.com/pages/publications/105025353954?origin=resultslist},
isbn = {979-833132774-3 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {Am. Conf. Inf. Syst., AMCIS},
volume = {7},
pages = {4853–4854},
publisher = {Association for Information Systems},
abstract = {Disinformation (false information disseminated with the intent to cause harm) takes many forms, including fake news, audio-video impersonations, data falsification, and more. It is linked to allegations of corruption and fraud against elected officials, which, once disambiguated (whether true or false), will in all cases require immediate action to preserve democracy. An integrated strategy to combat disinformation and corruption is needed, requiring constant vigilance, due diligence, and open-source intelligence (OSINT) collaboration. Our PDS will explore how AI, especially LLMs and Knowledge Graphs, can serve this purpose and automate the necessary means to combat hybrid threats to democracy. The PDS will cover the following issues and latest research advances, allowing also interactive roundtables to ensure participants can contribute insight to the debate: 1. Presentation: fighting jointly against disinformation and corruption 2. Roundtable: what innovations are most effective in combating hybrid threats? 3. Demo: ai and knowledge graphs to disambiguate allegations against elected officials 4. Roundtable: what institutional changes are required to fight hybrid threats? 5. Conclusion: action research helping parliamentary institutions develop AI capabilities. Copyright © 2025 by Association for Information Systems (AIS). All rights reserved.},
note = {Journal Abbreviation: Am. Conf. Inf. Syst., AMCIS},
keywords = {Action research, Audio videos, Copyrights, Crime, Due diligence, Information leakage, Information use, Institutional change, Integrated strategy, Knowledge graph, Knowledge graphs, Open source intelligence, Open systems, Research advances},
pubstate = {published},
tppubtype = {inproceedings}
}
Disinformation (false information disseminated with the intent to cause harm) takes many forms, including fake news, audio-video impersonations, data falsification, and more. It is linked to allegations of corruption and fraud against elected officials, which, once disambiguated (whether true or false), will in all cases require immediate action to preserve democracy. An integrated strategy to combat disinformation and corruption is needed, requiring constant vigilance, due diligence, and open-source intelligence (OSINT) collaboration. Our PDS will explore how AI, especially LLMs and Knowledge Graphs, can serve this purpose and automate the necessary means to combat hybrid threats to democracy. The PDS will cover the following issues and latest research advances, allowing also interactive roundtables to ensure participants can contribute insight to the debate: 1. Presentation: fighting jointly against disinformation and corruption 2. Roundtable: what innovations are most effective in combating hybrid threats? 3. Demo: ai and knowledge graphs to disambiguate allegations against elected officials 4. Roundtable: what institutional changes are required to fight hybrid threats? 5. Conclusion: action research helping parliamentary institutions develop AI capabilities. Copyright © 2025 by Association for Information Systems (AIS). All rights reserved.
9.
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.
10.
Gagnon, S.
"Use of AI to Fight Disinformation and Corruption" Proceedings Article
In: Am. Conf. Inf. Syst., AMCIS, pp. 4853–4854, Association for Information Systems, 2025, ISBN: 979-833132774-3 (ISBN).
Abstract | Links | BibTeX | Tags: Action research, Audio videos, Copyrights, Crime, Due diligence, Information leakage, Information use, Institutional change, Integrated strategy, Knowledge graph, Knowledge graphs, Open source intelligence, Open systems, Research advances
@inproceedings{gagnonUseAIFight2025a,
title = {"Use of AI to Fight Disinformation and Corruption"},
author = {S. Gagnon},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105025353954&partnerID=40&md5=fc3ca73e0de02b06fd218e03588f3e22},
isbn = {979-833132774-3 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {Am. Conf. Inf. Syst., AMCIS},
volume = {7},
pages = {4853–4854},
publisher = {Association for Information Systems},
abstract = {Disinformation (false information disseminated with the intent to cause harm) takes many forms, including fake news, audio-video impersonations, data falsification, and more. It is linked to allegations of corruption and fraud against elected officials, which, once disambiguated (whether true or false), will in all cases require immediate action to preserve democracy. An integrated strategy to combat disinformation and corruption is needed, requiring constant vigilance, due diligence, and open-source intelligence (OSINT) collaboration. Our PDS will explore how AI, especially LLMs and Knowledge Graphs, can serve this purpose and automate the necessary means to combat hybrid threats to democracy. The PDS will cover the following issues and latest research advances, allowing also interactive roundtables to ensure participants can contribute insight to the debate: 1. Presentation: fighting jointly against disinformation and corruption 2. Roundtable: what innovations are most effective in combating hybrid threats? 3. Demo: ai and knowledge graphs to disambiguate allegations against elected officials 4. Roundtable: what institutional changes are required to fight hybrid threats? 5. Conclusion: action research helping parliamentary institutions develop AI capabilities. Copyright © 2025 by Association for Information Systems (AIS). All rights reserved.},
keywords = {Action research, Audio videos, Copyrights, Crime, Due diligence, Information leakage, Information use, Institutional change, Integrated strategy, Knowledge graph, Knowledge graphs, Open source intelligence, Open systems, Research advances},
pubstate = {published},
tppubtype = {inproceedings}
}
Disinformation (false information disseminated with the intent to cause harm) takes many forms, including fake news, audio-video impersonations, data falsification, and more. It is linked to allegations of corruption and fraud against elected officials, which, once disambiguated (whether true or false), will in all cases require immediate action to preserve democracy. An integrated strategy to combat disinformation and corruption is needed, requiring constant vigilance, due diligence, and open-source intelligence (OSINT) collaboration. Our PDS will explore how AI, especially LLMs and Knowledge Graphs, can serve this purpose and automate the necessary means to combat hybrid threats to democracy. The PDS will cover the following issues and latest research advances, allowing also interactive roundtables to ensure participants can contribute insight to the debate: 1. Presentation: fighting jointly against disinformation and corruption 2. Roundtable: what innovations are most effective in combating hybrid threats? 3. Demo: ai and knowledge graphs to disambiguate allegations against elected officials 4. Roundtable: what institutional changes are required to fight hybrid threats? 5. Conclusion: action research helping parliamentary institutions develop AI capabilities. Copyright © 2025 by Association for Information Systems (AIS). All rights reserved.