The responsible design and use of information systems and platforms

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Summary

The responsible design and use of information systems and platforms is becoming increasingly important, and not just due to the latest developments in the field of artificial intelligence. In this context, the term "digital responsibility" is used, referring in particular to efforts by individuals, companies or public institutions to create a sustainable, inclusive, fair and value-oriented digital world (cf. Trier et. al, 2023). The field is particularly interested in researching how organizations and their employees design and use (novel) information systems and platforms responsibly, taking into account the principles of digital responsibility. These include the principles of IT security, privacy, accountability, transparency, fairness and sustainability. These principles are a cornerstone for the digitalization of the economy and society and are indispensable for a successful and sustainable digital transformation.

Source: Trier, M., Kundisch, D., Beverungen, D. et al. (2023). Digital responsibility. Bus Inf Syst Eng 65, 463-474. doi.org/10.1007/s12599-023-00822-x

Research assistants

Guangyu Du, Yannick Heß, Long Nguyen, Eva Späthe, Rizana Jörs, Fabio Sobotzki

Alumni: Dr. Heiner Teigeler, Dr. Malte Greulich, Dr. Philipp Danylak, Dr. Kathrin Brecker

Main research areas

Compassion means that we not only recognize our own suffering or that of others, but also want to proactively do something about it. Within the framework of Compassionate Technologies (which includes Compassionate AI), technologies are developed, designed, and studied that proactively focus on reducing suffering and harm of all kinds.

Investigate how companies actually embed formal security standards (e.g. ISO/IEC 27001) into internal processes, culture and behavior.

Investigation of AI systems that are traceable and accountable. Creation of foundations for AI accountability, promotion of proactive behavior to increase AI accountability and effects of AI accountability on users.

Research into mechanisms and frameworks that actively promote data protection in accordance with legal requirements.

Development of test procedures and certification guidelines to validate trustworthy AI solutions. Consideration of the variability of AI systems.

Examination of how employees implement security guidelines in the company and (proactively) protect the company's IT resources.

Research into dynamic, continuous auditing and certification methods instead of selective audits.

Investigate how digital platforms and ecosystems can be created to strengthen national and organizational autonomy.

Exemplary publications

  • Lins, S., Greulich, M., Pienta, D. A., Thatcher, J. B., & Sunyaev, A. (2026). The Impact of Threatening Cybersecurity Situations on Employees: A Conceptualization of Security Perplexity. Information Systems Research.
  • Nguyen, L. H., Späthe, E., Lins, S., & Sunyaev, A. (2026). No One to Blame: A Framework of Constitutive AI Unaccountability. Proceedings of the 9th AAAI Conference on AI, Ethics, and Society
  • Späthe, E., Danylak, P., Lins, S., & Sunyaev, A. (2025). Demonstrating Data Protection Efforts in Companies: A Comparison of Assessment Methods and Their Verification Mechanisms. Data Protection and Data Security (DuD), 49(10), 649–656.
  • Brecker, K., Lins, S., Bena, N., Ardagna, C. A., Anisetti, M., & Sunyaev, A. (2025). AI Impermanence: Achilles’ Heel for AI Assessment?. IEEE Access, 13, 194435–194455.
  • Greulich, M.; Lins, S.; Pienta, D.; Thatcher, J. B.; Sunyaev, A. (2024). Exploring Contrasting Effects of Trust in Organizational Security Practices and Protective Structures on Employees’ Security-Related Precaution Taking. In: Information Systems Research 35 (4). Pages 1507–2085. doi:10.1287/isre.2021.0528
  • Du, G.; Lins, S.; Blohm, I.; Sunyaev, A. “My Fault, Not AI’s Fault: Self-Serving Bias Impacts Employees’ Attribution of AI Accountability.” In: Proceedings of the 45th International Conference on Information Systems (ICIS)
  • Nguyen, L. H.; Lins, S.; Sunyaev, A. (2024). “Unraveling the Nuances of AI Accountability: A Synthesis of Dimensions Across Disciplines,” In: Proceedings of the European Conference on Information Systems (ECIS).
  • Danylak, P.; Lins, S.; Greulich, M.; Sunyaev, A. (2022). “Toward a Unified Framework for the Internalization of Information Systems Certification.” Proceedings of the 24th IEEE Conference on Business Informatics (CBI)
  • Lins, S., Schneider, S., Szefer, J., Ibraheem, S., & Sunyaev, A. (2019). Designing Monitoring Systems for Continuous Certification of Cloud Services: Deriving Meta-Requirements and Design Guidelines. In: Communications of the AIS, 44, 10.17705/1CAIS.04425.