Corresponding Manager: Pietro Vito (pietro.vito@uniroma1.it)
Track Manager(s): Pietro Vito, Francesca Iandolo, Antonio La Sala, Giuliano Maielli
Description
AI-enabled digital transformation has multiple, interacting dark sides—epistemic (hallucinations, echo chambers, filter bubbles, enclosure of information), organizational (automation bias, deskilling, brittle workflows), strategic/economic (path dependence, vendor lock-in, IP leakage), legal/ethical (privacy breaches, discrimination, accountability gaps), security/safety (adversarial and cyber risks, model drift), and environmental (compute intensity and footprint). In R&D, these forces distort opportunity discovery, concept selection, due diligence, supplier scouting, and validation, turning small errors into costly cascades.
This track foregrounds metrics, governance, and resilience. We invite conceptual, empirical, and design-science contributions that: (i) map specific risks to decision points across the R&D pipeline; (ii) measure impact using information-diversity indexes, verification/hallucination rates, exploration-breadth metrics, and red-team findings; and (iii) test mitigations—data provenance and lineage, plural sourcing, retrieval-augmented generation with verification, AI red-teaming and safety cases, human-in-the-loop escalation and burden-of-proof rules, post-deployment monitoring and incident reporting. We particularly welcome work that operationalizes governance (NIST AI RMF, ISO/IEC 42001, EU AI Act) with SME-appropriate controls, roles, and KPIs.
Keywords
Mistaken AI;Digital transformation governance;Information diversity metrics;R&D decision-making;Responsible AI standards
Key References
[1] NIST. (2023). AI Risk Management Framework (AI RMF 1.0).
[2] ISO/IEC. (2023). 42001: Artificial intelligence—Management system.
[3] MITRE. (2024). AI Red Teaming: Advancing Safe & Secure AI Systems.
[4] Huang, L. et al. (2023). “A Survey on Hallucination in Large Language Models.” arXiv:2311.05232.
[5] Arguedas, A. R., Fletcher, R., & Nielsen, R. K. (2022). “Echo Chambers, Filter Bubbles, and Polarisation: A Literature Review.” Reuters Institute / Royal Society.
[6] Agudo, U. et al. (2024). “The Impact of AI Errors in a Human-in-the-Loop Process.” Cognitive Research: Principles and Implications.
[7] Longpre, S. et al. (2024). Bridging the Data Provenance Gap Across Text, Speech, and Video. Data Provenance Initiative.
[8] Bail, C. A. (2021). Breaking the Social Media Prism: How to Make Our Platforms Less Polarizing. Princeton University Press.
[9] Lewis, M., Perez, E., & Black, S. (2024). “Retrieval-Augmented Generation for Knowledge-Intensive Tasks.” Proceedings of ACL 2024.
[10] Klein, G., & Hoffman, R. R. (2023). “Red Teaming AI Systems: Cognitive and Organizational Dimensions.” AI Magazine, 44(3).
[11] Dietvorst, B. J., Simmons, J. P., & Massey, C. (2015). “Algorithm Aversion: People Err in Avoiding Algorithms after Seeing Them Err.” Journal of Experimental Psychology: General, 144(1), 114–126.
[12] NIST (2024). Generative AI Profile.
[13] European Commission (2024). AI Act – Governance and Risk Provisions.
[14] von Krogh, G., & Haefliger, S. (2024). “Human–AI Collaboration in Innovation: Promise and Peril.” R&D Management Conference 2024, Bath.
[15] Magnusson, M., & Tell, F. (2023). “Artificial Intelligence and Knowledge Recombination in R&D Processes.” R&D Management, 53(6), 1078–1093.
[16] von Delft, S., & Baumgartner, R. J. (2022). “Responsible Digital Innovation: Managing the Ethical Dimension of AI in R&D.” R&D Management Conference 2022, Trento.
[17] Dodgson, M., & Gann, D. (2021). “AI and the Future of Innovation Management.” R&D Management, 51(4), 401–409.
[18] Haefner, N., et al. (2021). “Artificial Intelligence and Innovation Management: A Review and Research Agenda.” Technological Forecasting and Social Change, 162, 120392.
[19] Bogers, M., Chesbrough, H., & Moedas, C. (2022). “Open Innovation: Research, Practices, and Policies.” R&D Management, 52(5), 687–703.
[20] Ritala, P., & Huizingh, E. (2023). “Innovation Ecosystems and AI Integration: New Challenges for R&D Governance.” R&D Management Conference 2023, Seville.
[21] RADMA (2025). R&D Management Conference 2025: Digital and Emerging Technologies for Sustainability. University of Nottingham.