About

Nikola Banovic is an Associate Professor in the Department of Electrical Engineering and Computer Science (EECS) at the University of Michigan, where he also serves as the Associate Director of the Michigan Institute for Data Science. His research interests include Human-Computer Interaction, Explainable AI, and Responsible AI. He is involved in advancing understanding and development of AI systems that are interpretable and ethically aligned, contributing to the fields of data science and AI with a focus on responsible and human-centered technology.

Research topics

• Computer Science
• Artificial Intelligence
• Management science
• Knowledge management
• Social psychology
• Physical therapy
• Medicine
• Surgery
• Data science
• Intensive care medicine
• Mathematics
• Internal medicine
• Psychology
• Engineering
• Emergency medicine
• Nursing

Selected publications

• AI CHAOS! 2nd Workshop on the Challenges for Human Oversight of AI Systems

  2026-04-13

  article

  As AI systems are increasingly adopted in high-stakes domains such as healthcare, autonomous driving, and criminal justice, their failures may threaten human safety and rights. Human oversight of AI systems is therefore critically important as a potential safeguard to prevent harmful consequences in high-risk AI applications. The global regulatory and policy landscape for AI governance remains understandably fragmented and diverse. While frameworks like the European AI Act require human oversight for high-risk AI systems, there is currently a lack of well-defined methodologies and conceptual clarity to operationalize such oversight effectively. Independent of policy and regulation, poorly designed oversight can create dangerous illusions of safety while obscuring accountability. This interdisciplinary workshop aims to bring together researchers from various disciplines, including AI, HCI, psychology, law, and policy, to address this critical gap. We will explore the following questions: (1) What are the greatest challenges to achieving effective human oversight of AI systems? (2) How can we design AI systems that enable meaningful human oversight? (3) How do we assign responsibilities to and support the various stakeholders involved in oversight? Through talks and interactive group discussions, participants will identify oversight challenges; examine stakeholder roles; discuss supporting tools, methods, and regulatory frameworks; and establish a collaborative research agenda. Our central goal is to further a roadmap that enables effective human oversight for the responsible deployment of AI in society.

  PublisherDOI

• Empire Amplifier: Uncovering and Contesting the Prioritization of Colonial Content on Platforms Through Community-Informed Algorithmic Auditing

  ArXiv.org · 2026-04-30

  articleOpen accessSenior author

  Though online platforms claim to amplify Indigenous voices, Indigenous communities are worried that these systems are instead eroding their language and culture. We conduct a community-informed algorithmic audit to explore whether online platforms sustain or endanger Indigenous cultural practice. First, we review ethnographic research pertaining to the cultural anxieties of a specific Indigenous community, as Indigenous peoples are not a monolith. We consider concerns from Kyrgyz communities who believe that platforms are expanding Russia's linguistic influence and threatening their language. Next, we construct and conduct an algorithmic audit in conversation with the community. Our audit investigates deep-seated fears among Kyrgyz caregivers that YouTube encourages their children to speak Russian instead of Kyrgyz, their heritage language. We measure how the YouTube recommendation algorithm prioritizes content across Indigenous and non-Indigenous languages for child users. Our results validate caregiver concerns, as we find that YouTube primarily recommends non-Kyrgyz content to Kyrgyz children, even when children signal clear preferences for Kyrgyz content. Thus, platform recommendations reinforce Kyrgyz children's offline uptake of colonial language ideologies. Finally, we evaluate strategies to align platform behavior with Indigenous values. We identify effective end-user practices for reducing the proportion of Russian-language YouTube recommendations, like cross-generational device sharing. Overall, our work uncovers how platforms can amplify colonial influence, rather than revitalizing Indigenous cultural heritage. We encourage researchers to consider how algorithmic systems can reimpose oppressive power structures that decolonial efforts have sought to dismantle.

  PublisherOA PDF

• Empire Amplifier: Uncovering and Contesting the Prioritization of Colonial Content on Platforms Through Community-Informed Algorithmic Auditing

  arXiv (Cornell University) · 2026-04-30

  preprintOpen accessSenior author

  Though online platforms claim to amplify Indigenous voices, Indigenous communities are worried that these systems are instead eroding their language and culture. We conduct a community-informed algorithmic audit to explore whether online platforms sustain or endanger Indigenous cultural practice. First, we review ethnographic research pertaining to the cultural anxieties of a specific Indigenous community, as Indigenous peoples are not a monolith. We consider concerns from Kyrgyz communities who believe that platforms are expanding Russia's linguistic influence and threatening their language. Next, we construct and conduct an algorithmic audit in conversation with the community. Our audit investigates deep-seated fears among Kyrgyz caregivers that YouTube encourages their children to speak Russian instead of Kyrgyz, their heritage language. We measure how the YouTube recommendation algorithm prioritizes content across Indigenous and non-Indigenous languages for child users. Our results validate caregiver concerns, as we find that YouTube primarily recommends non-Kyrgyz content to Kyrgyz children, even when children signal clear preferences for Kyrgyz content. Thus, platform recommendations reinforce Kyrgyz children's offline uptake of colonial language ideologies. Finally, we evaluate strategies to align platform behavior with Indigenous values. We identify effective end-user practices for reducing the proportion of Russian-language YouTube recommendations, like cross-generational device sharing. Overall, our work uncovers how platforms can amplify colonial influence, rather than revitalizing Indigenous cultural heritage. We encourage researchers to consider how algorithmic systems can reimpose oppressive power structures that decolonial efforts have sought to dismantle.

  PublisherDOI

• What Do People Want to Know about Artificial Intelligence (AI)? The Importance of Answering End-user Questions to Explain Autonomous Vehicle (AV) Decisions

  Proceedings of the ACM on Human-Computer Interaction · 2025-10-16

  articleOpen accessSenior author

  Improving end-users' understanding of decisions made by autonomous vehicles (AVs) driven by artificial intelligence (AI) can improve utilization and acceptance of AVs. However, current explanation mechanisms primarily help AI researchers and engineers in debugging and monitoring their AI systems, and may not address the specific questions of end-users, such as passengers, about AVs in various scenarios. In this paper, we conducted two user studies to investigate questions that potential AV passengers might pose while riding in an AV and evaluate how well answers to those questions improve their understanding of AI-driven AV decisions. Our initial formative study identified a range of questions about AI in autonomous driving that existing explanation mechanisms do not readily address. Our second study demonstrated that interactive text-based explanations effectively improved participants' comprehension of AV decisions compared to simply observing AV decisions. These findings inform the design of interactions that motivate end-users to engage with and inquire about the reasoning behind AI-driven AV decisions.

  PublisherDOI

• Playing 'Google's Game': How Educational YouTubers Manage Tensions Between Education and Monetization

  Proceedings of the ACM on Human-Computer Interaction · 2025-10-16

  articleOpen access

  YouTube has become an important part of the educational ecosystem, with millions of viewers seeking informative videos and help with coursework. Educational YouTubers create this content, often balancing pedagogical rigor and entertainment value. However, creators need not only to promote their content to find viewers, but also to monetize. In this study, we explore the tensions educational YouTubers face when making monetized educational content. We conduct a qualitative interview study with 12 popular educational YouTubers about their monetization strategies, perceptions of YouTube's algorithmic promotion of their content, and conception of their audience. We find that educational YouTubers are largely driven by a desire to share free and high-quality educational content, and that common monetization strategies like sponsorships and clickbait sometimes interfere with this mission. We describe the careful strategies our participants use to maintain educational integrity while making a living on an algorithmically-driven platform. We then use these findings to draw parallels between YouTubers' challenges with monetizing educational content and the history of educational public broadcast in the United States, which has followed a similar trajectory. In closing, we offer several recommendations for supporting educational YouTubers in creating the high-quality, publicly accessible educational content that is appreciated by a worldwide audience.

  PublisherDOI

• "Here the GPT made a choice, and every choice can be biased": How Students Critically Engage with LLMs through End-User Auditing Activity

  2025-04-25 · 12 citations

  articleOpen accessSenior author

  Despite recognizing that Large Language Models (LLMs) can generate inaccurate or unacceptable responses, universities are increasingly making such models available to their students.Existing university policies defer the responsibility of checking for correctness and appropriateness of LLM responses to students and assume that they will have the required knowledge and skills to do so on their own.In this work, we conducted a series of user studies with students (N=47) from a large North American public research university to understand if and how they critically engage with LLMs.Our participants evaluated an LLM provided by the university in a quasi-experimental setup; first by themselves, and then with a scaffolded design probe that guided them through an end-user auditing exercise.Qualitative analysis of participant think-aloud and LLM interaction data showed that students without basic AI literacy skills struggle to conceptualize and evaluate LLM biases on their own.However, they transition to focused thinking and purposeful interactions when provided with structured guidance.We highlight areas where current university policies may fall short and offer policy and design recommendations to better support students.

  PublisherOA PDFDOI

• "Do Your Guardrails Even Guard?'' Method for Evaluating Effectiveness of Moderation Guardrails in Aligning LLM Outputs with Expert User Expectations

  Proceedings of the AAAI/ACM Conference on AI Ethics and Society · 2025-10-15

  articleOpen accessSenior author

  Ensuring that large language models (LLMs) align with human values and goals is crucial for their adoption in high-stakes decision-making. To guard against incorrect, misleading, or otherwise unexpected or undesirable LLM outputs, guardrail engineers implement guardrails based on expert knowledge from subject-matter authorities to steer and align pre-trained LLMs. Existing evaluation methods assess LLM performance, with and without guardrails, but provide limited insight into the contribution of each individual guardrail and its interactions on alignment. Here, we present a method to evaluate and select guardrails that best align LLM outputs with empirical evidence representing expert knowledge. Through evaluation with real-world illustrative examples of resume quality and recidivism prediction, we show that our method effectively identifies useful moderation guardrails in a way that could help guardrail engineers interpret contributions of different guardrails to "user-LLM" alignment.

  PublisherOA PDFDOI

• On the Limits of Selective AI Prediction: A Case Study in Clinical Decision Making

  ArXiv.org · 2025-08-11

  preprintOpen access

  AI has the potential to augment human decision making. However, even high-performing models can produce inaccurate predictions when deployed. These inaccuracies, combined with automation bias, where humans overrely on AI predictions, can result in worse decisions. Selective prediction, in which potentially unreliable model predictions are hidden from users, has been proposed as a solution. This approach assumes that when AI abstains and informs the user so, humans make decisions as they would without AI involvement. To test this assumption, we study the effects of selective prediction on human decisions in a clinical context. We conducted a user study of 259 clinicians tasked with diagnosing and treating hospitalized patients. We compared their baseline performance without any AI involvement to their AI-assisted accuracy with and without selective prediction. Our findings indicate that selective prediction mitigates the negative effects of inaccurate AI in terms of decision accuracy. Compared to no AI assistance, clinician accuracy declined when shown inaccurate AI predictions (66% [95% CI: 56%-75%] vs. 56% [95% CI: 46%-66%]), but recovered under selective prediction (64% [95% CI: 54%-73%]). However, while selective prediction nearly maintains overall accuracy, our results suggest that it alters patterns of mistakes: when informed the AI abstains, clinicians underdiagnose (18% increase in missed diagnoses) and undertreat (35% increase in missed treatments) compared to no AI input at all. Our findings underscore the importance of empirically validating assumptions about how humans engage with AI within human-AI systems.

  PublisherOA PDFDOI

• What Do People Want to Know About Artificial Intelligence (AI)? The Importance of Answering End-User Questions to Explain Autonomous Vehicle (AV) Decisions

  ArXiv.org · 2025-05-09

  preprintOpen accessSenior author

  Improving end-users' understanding of decisions made by autonomous vehicles (AVs) driven by artificial intelligence (AI) can improve utilization and acceptance of AVs. However, current explanation mechanisms primarily help AI researchers and engineers in debugging and monitoring their AI systems, and may not address the specific questions of end-users, such as passengers, about AVs in various scenarios. In this paper, we conducted two user studies to investigate questions that potential AV passengers might pose while riding in an AV and evaluate how well answers to those questions improve their understanding of AI-driven AV decisions. Our initial formative study identified a range of questions about AI in autonomous driving that existing explanation mechanisms do not readily address. Our second study demonstrated that interactive text-based explanations effectively improved participants' comprehension of AV decisions compared to simply observing AV decisions. These findings inform the design of interactions that motivate end-users to engage with and inquire about the reasoning behind AI-driven AV decisions.

  PublisherOA PDFDOI

• Reducing Uncertainty in Multijurisdictional Urban Stormwater Systems Under Climate Extremes

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access
  PublisherDOI

Frequent coauthors

• Jennifer Mankoff

  University of Washington

  13 shared

• Anind K. Dey

  10 shared

• Nel Escher

  Michigan United

  10 shared

• Sumit Asthana

  University of Michigan–Ann Arbor

  10 shared

• Xun Huan

  University of Michigan–Ann Arbor

  8 shared

• Jane Im

  University of Michigan–Ann Arbor

  8 shared

• Snehal Prabhudesai

  7 shared

• Anindya Das Antar

  University of Michigan–Ann Arbor

  6 shared

Labs

• Michigan AI LabPI

Education

• PhD in Human-Computer Interaction, Human-Computer Interaction Institute

  Carnegie Mellon University

  2018