Michaela B. Benk

I am an interdisciplinary researcher with a background in Computational Linguistics and Multilingual Text Analysis, and over eight years of experience in Human-Computer Interaction and Human-Centered AI. I study how people make decisions, how AI systems influence those decisions, and what that means for society. I strongly believe that for AI systems to become valuable tools for all, humans (and not just the WEIRD ones) and their well-being must be central to their design, evaluation, and governance.

I have presented my work at top-tier international AI conferences and periodically contribute as an expert and speaker at international roundtables and AI forums. While my expertise spans a range of topics, I am particularly passionate about the intersection of AI and the protection and promotion of human rights - an area where I believe interdisciplinary collaboration is especially needed.

I am always open to discussions, collaborations, and opportunities to expand my knowledge and impact in this exciting field. Feel free to connect with me to exchange ideas or explore potential collaborations.

News

Selected Publications

• [2025] Bridging the Knowledge Gap: Understanding User Expectations for Trustworthy LLM Standards. Benk, M.,Wettstein, L., Schlicker, N.,Wangenheim, F., Scharowski, N. AAAI Conference on Artificial Intelligence.
  TL;DR: Mixed-methods web-based study (interviews N=12, survey N=379) identifying 68 user-centric trust criteria for LLMs. Reveals priorities for transparent governance, communication, and non-technical complements to techno-focused standards.


• [2024] Twenty-Four Years of Empirical Research on Trust in AI: A Bibliometric Review of Trends, Overlooked Issues, and Future Directions. Benk, M., Kerstan, S., Wangenheim, F., Ferrario, A. AI & Society
  TL;DR: Bibliometric and content analysis comprising 1’156 core articles and 36’306 cited articles across multiple disciplines. Shows growth trends, shifting thematic landscape, and continuous gaps in the development of well-defined and contextualized theoretical models to understand trust in AI. (link)


• [2023] Certification Labels for Trustworthy AI: Insights From an Empirical Mixed-Method Study Scharowski, N., Benk, M., Kühne, S.J., Wettstein, L., Brühlmann, F. Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency (FAccT).
  TL;DR: User interviews (N=12) and survey (N=302) show certification labels boost trust and intent to use AI, especially in high‑stakes contexts. (link)


• [2022] "Is It My Turn?" Assessing Teamwork and Taskwork in Collaborative Immersive Analytics\n Benk, M., Weibel, R., Feuerriegel, S., Ferrario, A. Proceedings of the 25th ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW).
  TL;DR: Empirical study of user collaboration in immersive analytics, investigating how teams navigate machine learning tasks in an immersive analytics environment. (link)


• [2022] Investigating Employees’ Concerns and Wishes Regarding Digital Stress Management Interventions With Value Sensitive Design: Mixed Methods Study, Kerr, J., Naegelin, M., Benk, M., Wangenheim, F., Meins, E., Viganò, E., Ferrario, A. Journal of Medical Internet Research.
  TL;DR: Mixed-method exploration using Value-Sensitive Design to uncover employee preferences and privacy concerns around digital stress management tools. (link)


• [2021] The Value of Measuring Trust in AI – A Socio-Technical System Perspective, Benk, M., Tolmeijer, S., Wangenheim, F., Ferrario, A. CHI 2022 Workshop on Trust and Reliance in AI-Human Teams (TRAIT).
  TL;DR: Critiques the predominant focus on trust as an evaluation framework for AI research. Proposes a socio-technical systems approach to measuring trust, advocating for holistic assessment beyond individual-level metrics. (link)


• [2021] Creative Uses of AI Systems and their Explanations: A Case Study from Insurance, Benk, M., Weibel, R., Ferrario, A. CHI 2022 Workshop on Human-Centered Explainable AI (HCXAI).
  TL;DR: Case study in insurance reveals novel creative workflows enabled by AI systems and explores how explanation mechanisms support users in these settings. (link)

Talks and Guest Lectures

• [July 2025 (upcoming)] AI for Human Rights: Smarter, Faster, Fairer Monitoring (Invited Expert), AI for Good 2025 Global Summit.
  Topic: Leveraging artificial intelligence to improve speed, fairness, and effectiveness in global human rights monitoring.
• [Nov 2024] Transparency Challenges in Human Rights Monitoring (Invited Speaker), GHRP Annual Conference – Expert Roundtable on Digital Human Rights Tracking Tools and Databases.
  Topic: Transparency issues and limitations in the use of digital tools and databases for human rights monitoring.
• [Apr 2024] Guest Lecture on Human-Centered Explainable AI in course ‘KI und Ethik’, University of Basel.
  Topic: Basics on human-centered design principles in explainable AI and their ethical implications.
• [Nov 2023] Welcoming Sentient AI (Panelist), Zurich Independent Art Exhibition.
  Topic: Artistic and philosophical perspectives on sentient AI and its societal impacts.
• [May 2023] Explainability and Trust: The Importance of the Human Factor (Panelist), European Data Protection Supervisor IPEN event on Explainable Artificial Intelligence (XAI).
  Topic: Discussion on the critical role of human factors in the design and implementation of explainable AI systems.
• [Sep 2022] Data Science zum Anfassen (Invited Speaker), Scientifica: Zurich Science Days.
  Topic: Making machine learning accessible and engaging for the public through interactive visual analytics.
• [Apr 2019] Monitoring Hate Speech Online (Invited Speaker), Annual meeting of the United Nations High Commissioner for Human Rights.
  Topic: Approaches and challenges in using machine learning to monitor hate speech online.

CV

Download my CV (PDF)