publications

2026

1. Conference Paper

   Wavelength.AI: Extending the Collaborative Game Wavelength as a Testbed for Studying Shared Understanding in Human-Agent Collaboration

   Morrison, Katelyn, Gonzalez, Gabriel Enrique, Ashktorab, Zahra, Riemer, Matt, Anderson, Andrew, Bouneffouf, Djallel, and Weisz, Justin D.

   In Proceedings of the 35th International Joint Conference on Artificial Intelligence (IJCAI 2026), Demo Track 2026

   Abs Bib HTML PDF

   AI’s increasing role as a personal agent assisting knowledge workers in everyday tasks underscores the need to investigate how to help human-agent teams build a shared understanding. We extend the collaborative "mind-reading" game Wavelength to include an AI teammate, presenting the first demonstration of an LLM capable of playing this game. Based on our agent-agent play experiments, we developed Wavelength.AI, which implements two strategies to support shared understanding: an initial team grounding conversation and post-game reflective explanations. We interpret higher team scores as evidence for better shared understanding in a preliminary user study with 24 human-AI teams. Our findings reveal that Wavelength.AI can help researchers evaluate and design different strategies to shape human-agent teams’ shared understanding.

   @inproceedings{morrison2026wavelengthai,
     abbr = {Conference Paper},
     bibtex_show = {true},
     html = {https://play-wavelength-ai.com},
     pdf = {ijcai_ecai_26_wavelength_demo.pdf},
     title = {Wavelength.AI: Extending the Collaborative Game Wavelength as a Testbed for Studying Shared Understanding in Human-Agent Collaboration},
     author = {Morrison, Katelyn and Gonzalez, Gabriel Enrique and Ashktorab, Zahra and Riemer, Matt and Anderson, Andrew and Bouneffouf, Djallel and Weisz, Justin D.},
     booktitle = {Proceedings of the 35th International Joint Conference on Artificial Intelligence (IJCAI 2026), Demo Track},
     year = {2026},
     pub_year = {2026},
     citation = {IJCAI 2026 Demo Track, 2026}
   }

2. Conference Paper

   Don’t be fooled: the misinformation effect of explanations in human–ai collaboration

   Spitzer, Philipp, Holstein, Joshua, Morrison, Katelyn, Holstein, Kenneth, Satzger, Gerhard, and Kühl, Niklas

   International Journal of Human–Computer Interaction 2026

   Abs Bib HTML

   Across various applications, humans increasingly use black-box artificial intelligence (AI) systems without insight into these systems’ reasoning. To counter this opacity, explainable AI (XAI) methods promise enhanced transparency and interpretability. While recent studies have explored how XAI affects human–AI collaboration, few have examined the potential pitfalls caused by incorrect explanations. The implications for humans can be far-reaching but have not been explored extensively. To investigate this, we conducted a study (n = 160) on AI-assisted decision-making in which humans were supported by XAI. Our findings reveal a misinformation effect when incorrect explanations accompany correct AI advice with implications post-collaboration. This effect causes humans to infer flawed reasoning strategies, hindering task execution and demonstrating impaired procedural knowledge. Additionally, incorrect ...

   @article{spitzer2026dontbefool,
     abbr = {Conference Paper},
     bibtex_show = {true},
     html = {https://www.tandfonline.com/doi/abs/10.1080/10447318.2025.2574511},
     title = {Don’t be fooled: the misinformation effect of explanations in human–ai collaboration},
     author = {Spitzer, Philipp and Holstein, Joshua and Morrison, Katelyn and Holstein, Kenneth and Satzger, Gerhard and Kühl, Niklas},
     journal = {International Journal of Human–Computer Interaction},
     publisher = {Taylor & Francis},
     volume = {42},
     number = {12},
     pages = {8996-9024},
     year = {2026},
     pub_year = {2026},
     citation = {International Journal of Human–Computer Interaction 42 (12), 8996-9024, 2026}
   }

3. Conference Paper

   Surfacing Design Tensions and Opportunities for AI-Mediated Pre-diagnostic Risk Communication for Breast Cancer Care

   Kim, Seyun, Morrison, Katelyn, Tan, Nina, Turner, Kimberly, Zhu, Haiyi, and Eslami, Motahhare

   2026

   Abs Bib HTML

   AI development for healthcare aims to enhance medical decision-making through risk evaluation. Scholars have focused on improving the accuracy of Breast Cancer AI Risk Assessment Tools (BC-AIRAT), yet these tools remain underutilized in clinical practices. This provides an opportunity to explore how these tools are used and how they may support risk communications. We conducted a three-phase study, with clinicians and patients, in the context of the United States healthcare system, including formative interviews that surface the challenges of BC-AIRAT practices, design probe re-purposing BC-AIRAT as supporting risk communications, and design probe-driven interviews with diverse stakeholders. Our findings surface the gap and opportunity for designing AI-mediated risk communication tool, highlighting the participants’ reflections on AI for managing risk assessment workflows, mediating fragmented ...

   @misc{kim2026surfacingd,
     abbr = {Conference Paper},
     bibtex_show = {true},
     html = {https://dl.acm.org/doi/abs/10.1145/3800645.3813100},
     title = {Surfacing Design Tensions and Opportunities for AI-Mediated Pre-diagnostic Risk Communication for Breast Cancer Care},
     author = {Kim, Seyun and Morrison, Katelyn and Tan, Nina and Turner, Kimberly and Zhu, Haiyi and Eslami, Motahhare},
     pages = {1684-1701},
     year = {2026},
     pub_year = {2026},
     citation = {Proceedings of the 2026 Designing Interactive Systems Conference, 1684-1701, 2026}
   }

4. Conference Paper

   Refining the definition for “low risk” in pulmonary arterial hypertension: time to reduce morbidity and mortality

   Fauvel, Charles, Jaque, Priscilla Correa, Liu, Yongqi, Lee, Sang Wan, Sahay, Sandeep, Hernandez, Nelson A Villasmil, Weatherald, Jason, White, James, Everett, Allen, Griffith, Megan, and al.,

   JACC: Heart Failure 2026

   Abs Bib HTML

   ackground“Low-risk” pulmonary arterial hypertension (PAH) patients are defined as having a risk of death <5% within 1 year. As treatment improves, accounting for the negative impact of short-term morbidity and considering extending the duration of <5% mortality in this definition seem prudent.ObjectivesThis study sought to propose a more stringent definition of low risk that extends the low mortality risk to 3 years and specifies a low risk of morbidity at 1 year.MethodsThis study used patient-level data from a harmonized data set of 8 PAH randomized controlled trials that included patients with a REVEAL (Registry to Evaluate Early and Long-Term PAH Disease Management) 2.0 risk score ≤6 (ie, low risk using contemporary scores). Low risk was a priori defined as “risk of death <5% at 3 years and risk of clinical worsening <10% at 1 year.” Clinical worsening was defined at the individual trial level and ...

   @article{fauvel2026refiningth,
     abbr = {Conference Paper},
     bibtex_show = {true},
     html = {https://www.sciencedirect.com/science/article/pii/S2213177926002519},
     title = {Refining the definition for “low risk” in pulmonary arterial hypertension: time to reduce morbidity and mortality},
     author = {Fauvel, Charles and Jaque, Priscilla Correa and Liu, Yongqi and Lee, Sang Wan and Sahay, Sandeep and Hernandez, Nelson A Villasmil and Weatherald, Jason and White, James and Everett, Allen and Griffith, Megan and et al.},
     journal = {JACC: Heart Failure},
     publisher = {Elsevier},
     pages = {103151},
     year = {2026},
     pub_year = {2026},
     citation = {JACC: Heart Failure, 103151, 2026}
   }

5. Conference Paper

   It’s Not Just for Trust: Designing for Emerging Uses of Explainable AI in Clinical Decision-Making

   Morrison, Katelyn, Li, Zexuan, Kim, Minsuk, Hong, Chengqi, Kraisangka, Jidapa, Correa-Jaque, Priscilla, Fauvel, Charles, Sahay, Sandeep, Vanderpool, Rebecca R, Everett, Allen, and al.,

   ACM Transactions on Computing for Healthcare 2026

   Abs Bib HTML

   Explainable AI (XAI) is often viewed as a mechanism to promote the transparency and interpretability of AI recommendations in high-stakes domains, such as healthcare. This has led many studies to focus on designing and evaluating XAI to foster trust, calibrate reliance, enable algorithmic recourse, and support model understanding. However, this limited design scope restricts our understanding of how XAI can be used more broadly to support critical tasks in complex workflows, such as facilitating shared decision-making and supporting communication between stakeholders. Our work aims to address these critical gaps in the design for and understanding of XAI’s emerging uses by iteratively prototyping XAI designs for an AI-powered clinical decision-support system with clinical stakeholders. We then created a high-fidelity prototype from those iterative sessions and used it as a design probe to uncover four ...

   @article{morrison2026itsnotjust,
     abbr = {Conference Paper},
     bibtex_show = {true},
     html = {https://dl.acm.org/doi/abs/10.1145/3815175},
     title = {It’s Not Just for Trust: Designing for Emerging Uses of Explainable AI in Clinical Decision-Making},
     author = {Morrison, Katelyn and Li, Zexuan and Kim, Minsuk and Hong, Chengqi and Kraisangka, Jidapa and Correa-Jaque, Priscilla and Fauvel, Charles and Sahay, Sandeep and Vanderpool, Rebecca R and Everett, Allen and et al.},
     journal = {ACM Transactions on Computing for Healthcare},
     publisher = {ACM},
     year = {2026},
     pub_year = {2026},
     citation = {ACM Transactions on Computing for Healthcare, 2026}
   }

6. Workshop

   Agentic Automation Experiences—Rethinking the Interaction of Humans and AI Agents

   Spitzer, Philipp, Baldauf, Matthias, Palanque, Philippe, Roto, Virpi, Morrison, Katelyn, Gomez-Beldarrain, Garoa, Westphal, Monika, and Holstein, Joshua

   2026

   Abs Bib HTML

   Recent advances in Artificial Intelligence (AI) have enabled agentic AI systems that coordinate multiple, specialized agents behind unified interfaces. These systems can independently initiate actions and solve complex problems. In traditional automation systems within organizations, workers maintained clear oversight—they could see which system handled each task and trace outcomes to specific processes. The integration of agentic AI, however, obscures this relationship and makes it more difficult for humans to identify which agent is responsible for a given outcome. This creates novel research challenges in the field of “Automation Experience”, particularly in terms of transparency, human agency, and long-term human-AI collaboration dynamics. This workshop focuses on these three critical research dimensions. First, multi-agent transparency and attribution explore how humans understand decision-making ...

   @misc{spitzer2026agenticaut,
     abbr = {Workshop},
     bibtex_show = {true},
     html = {https://dl.acm.org/doi/abs/10.1145/3772363.3778732},
     title = {Agentic Automation Experiences—Rethinking the Interaction of Humans and AI Agents},
     author = {Spitzer, Philipp and Baldauf, Matthias and Palanque, Philippe and Roto, Virpi and Morrison, Katelyn and Gomez-Beldarrain, Garoa and Westphal, Monika and Holstein, Joshua},
     pages = {1-6},
     year = {2026},
     pub_year = {2026},
     citation = {Proceedings of the Extended Abstracts of the 2026 CHI Conference on Human ..., 2026}
   }

7. Workshop Paper

   “On the Same Wavelength”: Exploring Human–Agent Collaboration Strategies through the Cooperative Game Wavelength

   Morrison, Katelyn, Gonzalez, Gabriel Enrique, Ashktorab, Zahra, Riemer, Matthew, Anderson, Andrew, and Weisz, Justin D

   2026

   Abs Bib HTML

   Large language model (LLM) agents are increasingly embedded into workplace tools (eg, Microsoft 365) to support everyday tasks, from drafting emails to building slide decks. However, collaboration with these LLM agents can be challenging when humans and agents lack shared understanding. To investigate strategies that help human–agent teams build shared understanding, we study 24 human–agent teams playing Wavelength, a cooperative game that requires shared understanding for successful play. We compare three strategies:(1) implicit in-context learning across rounds,(2) an initial grounding conversation before gameplay, and (3) initial grounding conversations paired with post-round reflective explanations, where teammates describe how they interpreted clues and made decisions. Our preliminary analyses suggest that grounding conversations can improve team collaboration, but alone may be insufficient. Teams performed best when initial grounding conversations were paired with reflective explanations. We discuss implications for designing human–agent systems that better support shared understanding and coordination in knowledge work.

   @misc{morrison2026onthesamew,
     abbr = {Workshop Paper},
     bibtex_show = {true},
     html = {https://chi26workshop-human-agent-collaboration.hailab.io/assets/papers/27%20-%20On%20the%20Same%20Wavelength%20Exploring%20HumanAgent%20Collaboration%20Strategies%20through%20the%20Cooperative%20Game%20Wavelength.pdf},
     title = {“On the Same Wavelength”: Exploring Human–Agent Collaboration Strategies through the Cooperative Game Wavelength},
     author = {Morrison, Katelyn and Gonzalez, Gabriel Enrique and Ashktorab, Zahra and Riemer, Matthew and Anderson, Andrew and Weisz, Justin D},
     year = {2026},
     pub_year = {2026},
     citation = {CHI ’26 Workshop on Human–Agent Collaboration, 2026},
     conference = {CHI ’26 Workshop on Human–Agent Collaboration}
   }

8. Workshop Paper

   Reframing Explainable Generative AI as a Tool for Thought

   Morrison, Katelyn, Mason, Eric, Chen, Maggie, Lundi, Steven, Zandifar, Afrooz, Kim, Daesung, Dai, Weicheng, Eslami, Motahhare, and Perer, Adam

   2026

   Abs Bib HTML

   Generative AI (GenAI) has become ubiquitous across our personal and professional lives, able to assist us with nearly any task thrown its way. To help end users calibrate their reliance on and better understand the outputs of GenAI, researchers have developed explainable generative AI (GenXAI). However, this output-oriented framing of XAI positions the user to focus on validating model behavior, distracting them from engaging in their own reasoning process. To mitigate this, we propose reframing GenXAI as a design space that reframes XAI techniques to support reasoning throughout workflows, which we call process-oriented XAI. In this workshop paper, we instantiate process-oriented XAI in the first GenXAI Tool for Thought (TfT), which incorporates four XAI techniques to support users in interpreting chest CT scans. Through a think-aloud and semi-structured interview study with five medical students and five radiology residents, we show how our Tool for

   @misc{morrison2026reframinge,
     abbr = {Workshop Paper},
     bibtex_show = {true},
     html = {https://ai-tools-for-thought.github.io/workshop/documents/chi26/Morrison_et_al_Reframing_Explainable_Generative_AI_TfT_CHI26.pdf},
     title = {Reframing Explainable Generative AI as a Tool for Thought},
     author = {Morrison, Katelyn and Mason, Eric and Chen, Maggie and Lundi, Steven and Zandifar, Afrooz and Kim, Daesung and Dai, Weicheng and Eslami, Motahhare and Perer, Adam},
     year = {2026},
     pub_year = {2026},
     citation = {Tools for Thought Workshop at ACM CHI, 2026},
     conference = {Tools for Thought Workshop at ACM CHI}
   }

2025

1. Conference Paper

   Mammo-FM: Breast-specific foundational model for Integrated Mammographic Diagnosis, Prognosis, and Reporting

   Ghosh, Shantanu, Joshi, Vedant Parthesh, Syed, Rayan, Budhraja, Param, Kassem, Aya, Morrison, Katelyn C, Tang, Alex, Wong, Ho Cheung Aiden, Varshney, Abhishek, Basak, Payel, and al.,

   arXiv preprint arXiv:2512.00198 2025

   Abs Bib HTML

   Breast cancer is one of the leading causes of death among women worldwide. We introduce Mammo-FM, the first foundation model specifically for mammography, pretrained on the largest and most diverse dataset to date - 140,677 patients (821,326 mammograms) across four U.S. institutions. Mammo-FM provides a unified foundation for core clinical tasks in breast imaging, including cancer diagnosis, pathology localization, structured report generation, and cancer risk prognosis within a single framework. Its alignment between images and text enables both visual and textual interpretability, improving transparency and clinical auditability, which are essential for real-world adoption. We rigorously evaluate Mammo-FM across diagnosis, prognosis, and report-generation tasks in in- and out-of-distribution datasets. Despite operating on native-resolution mammograms and using only one-third of the parameters of state-of-the-art generalist FMs, Mammo-FM consistently outperforms them across multiple public and private benchmarks. These results highlight the efficiency and value of domain-specific foundation models designed around the full spectrum of tasks within a clinical domain and emphasize the importance of rigorous, domain-aligned evaluation.

   @article{ghosh2025mammofmbre,
     abbr = {Conference Paper},
     bibtex_show = {true},
     html = {https://arxiv.org/abs/2512.00198},
     title = {Mammo-FM: Breast-specific foundational model for Integrated Mammographic Diagnosis, Prognosis, and Reporting},
     author = {Ghosh, Shantanu and Joshi, Vedant Parthesh and Syed, Rayan and Budhraja, Param and Kassem, Aya and Morrison, Katelyn C and Tang, Alex and Wong, Ho Cheung Aiden and Varshney, Abhishek and Basak, Payel and et al.},
     journal = {arXiv preprint arXiv:2512.00198},
     year = {2025},
     pub_year = {2025},
     citation = {arXiv preprint arXiv:2512.00198, 2025}
   }

2. Workshop Paper

   Proceedings of 1st Workshop on Advancing Artificial Intelligence through Theory of Mind

   Abrini, Mouad, Abend, Omri, Acklin, Dina, Admoni, Henny, Aichinger, Gregor, Alon, Nitay, Ashktorab, Zahra, Atreja, Ashish, Auron, Moises, Aufreiter, Alexander, and al.,

   arXiv preprint arXiv:2505.03770 2025

   Abs Bib HTML

   This volume includes a selection of papers presented at the Workshop on Advancing Artificial Intelligence through Theory of Mind held at AAAI 2025 in Philadelphia US on 3rd March 2025. The purpose of this volume is to provide an open access and curated anthology for the ToM and AI research community.

   @article{abrini2025proceeding,
     abbr = {Workshop Paper},
     bibtex_show = {true},
     title = {Proceedings of 1st Workshop on Advancing Artificial Intelligence through Theory of Mind},
     author = {Abrini, Mouad and Abend, Omri and Acklin, Dina and Admoni, Henny and Aichinger, Gregor and Alon, Nitay and Ashktorab, Zahra and Atreja, Ashish and Auron, Moises and Aufreiter, Alexander and et al.},
     journal = {arXiv preprint arXiv:2505.03770},
     year = {2025},
     pub_year = {2025},
     citation = {arXiv preprint arXiv:2505.03770, 2025},
     html = {https://arxiv.org/abs/2505.03770}
   }

3. Workshop Paper

   Over-Relying on Reliance: Towards Realistic Evaluations of AI-Based Clinical Decision Support

   Sivaraman, Venkatesh, Morrison, Katelyn, Epperson, Will, and Perer, Adam

   Workshop on Envisioning the Future of Interactive Health 2025

   Abs Bib HTML

   As AI-based clinical decision support (AI-CDS) is introduced in more and more aspects of healthcare services, HCI research plays an increasingly important role in designing for complementarity between AI and clinicians. However, current evaluations of AI-CDS often fail to capture when AI is and is not useful to clinicians. This position paper reflects on our work and influential AI-CDS literature to advocate for moving beyond evaluation metrics like Trust, Reliance, Acceptance, and Performance on the AI’s task (what we term the "trap" of human-AI collaboration). Although these metrics can be meaningful in some simple scenarios, we argue that optimizing for them ignores important ways that AI falls short of clinical benefit, as well as ways that clinicians successfully use AI. As the fields of HCI and AI in healthcare develop new ways to design and evaluate CDS tools, we call on the community to prioritize ecologically valid, domain-appropriate study setups that measure the emergent forms of value that AI can bring to healthcare professionals.

   @article{sivaraman2025overrelyin,
     abbr = {Workshop Paper},
     bibtex_show = {true},
     title = {Over-Relying on Reliance: Towards Realistic Evaluations of AI-Based Clinical Decision Support},
     author = {Sivaraman, Venkatesh and Morrison, Katelyn and Epperson, Will and Perer, Adam},
     journal = {Workshop on Envisioning the Future of Interactive Health},
     year = {2025},
     pub_year = {2025},
     html = {https://arxiv.org/pdf/2504.07423?}
   }

4. Conference Paper

   Toward Interpretable 3D Diffusion in Radiology: Token-Wise Attribution for Text-to-CT Synthesis

   Bradshaw, Aidan, Morrison, Katelyn, Mathur, Arpit, Dai, Weicheng, Eslami, Motahhare, and Perer, Adam

   2025

   Abs Bib PDF

   Diffusion-based generative models have emerged as powerful tools for synthesizing anatomically realistic computed tomography (CT) scans from free-text prompts but remain opaque when delineating token influence on the conditioned CT volume. This lack of interpretability limits their clinical applicability, trustworthiness, and adoption across diagnostic and decision-support scenarios. We present a token-wise voxel attribution method for 3D text-to-image diffusion models that leverages cross-attention in U-Net–based architectures to extract individual token attention maps for synthetic CT scans. Our method visualizes individual, joint, or aggregated token-level voxel attributions during CT synthesis, helping to alleviate concerns about model transparency. This lays the groundwork for practical methods and structured explanations illustrating what aspects of attribution work well, where current limitations lie, and how researchers might approach explainable AI for 3D text-to-image diffusion models in radiology moving forward.

   @misc{bradshaw2025towardinte,
     abbr = {Conference Paper},
     bibtex_show = {true},
     title = {Toward Interpretable 3D Diffusion in Radiology: Token-Wise Attribution for Text-to-CT Synthesis},
     author = {Bradshaw, Aidan and Morrison, Katelyn and Mathur, Arpit and Dai, Weicheng and Eslami, Motahhare and Perer, Adam},
     year = {2025},
     pub_year = {2025},
     citation = {Medical Imaging with Deep Learning-Short Papers, 2025},
     conference = {Medical Imaging with Deep Learning-Short Papers},
     pdf = {https://openreview.net/pdf?id=DTYFRzRPQn}
   }

5. Conference Paper

   A Human-Centered Approach to Identifying Promises, Risks, & Challenges of Text-to-Image Generative AI in Radiology

   Morrison, Katelyn, Mathur, Arpit, Bradshaw, Aidan, Wartmann, Tom, Lundi, Steven, Zandifar, Afrooz, Dai, Weichang, Batmanghelich, Kayhan, Eslami, Motahhare, and Perer, Adam

   Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society 2025

   Abs Bib PDF

   As text-to-image generative models rapidly improve, AI researchers are making significant advances in developing domain-specific models capable of generating complex medical imagery from text prompts. Despite this, these technical advancements have overlooked whether and how medical professionals would benefit from and use text-to-image generative AI (GenAI) in practice. By developing domain-specific GenAI without involving stakeholders, we risk the potential of building models that are either not useful or even more harmful than helpful. In this paper, we adopt a human-centered approach to responsible model development by involving stakeholders in evaluating and reflecting on the promises, risks, and challenges of a novel text-to-CT Scan GenAI model. Through exploratory model prompting activities, we uncover the perspectives of medical students, radiology trainees, and radiologists on the role that text-to-CT Scan GenAI can play across medical education, training, and practice. This human-centered approach additionally enabled us to surface technical challenges and domain-specific risks of generating synthetic medical images. We conclude by reflecting on the implications of medical text-to-image GenAI.

   @article{morrison2025ahumancent,
     abbr = {Conference Paper},
     bibtex_show = {true},
     selected = {true},
     title = {A Human-Centered Approach to Identifying Promises, Risks, & Challenges of Text-to-Image Generative AI in Radiology},
     author = {Morrison, Katelyn and Mathur, Arpit and Bradshaw, Aidan and Wartmann, Tom and Lundi, Steven and Zandifar, Afrooz and Dai, Weichang and Batmanghelich, Kayhan and Eslami, Motahhare and Perer, Adam},
     journal = {Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society},
     volume = {8},
     number = {2},
     pages = {1758-1770},
     year = {2025},
     pub_year = {2025},
     citation = {Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society 8 (2 ..., 2025},
     pdf = {https://ojs.aaai.org/index.php/AIES/article/download/36672/38810}
   }

6. Journal Paper

   Imperfections of XAI: Phenomena Influencing AI-Assisted Decision-Making

   Spitzer, Philipp, Morrison, Katelyn, Turri, Violet, Feng, Michelle, Perer, Adam, and Kühl, Niklas

   ACM Transactions on Interactive Intelligent Systems 2025

   Abs Bib HTML

   With the increasing use of AI, recent research in human–computer interaction explores Explainable AI (XAI) to make AI advice more interpretable. While research addresses the effects of incorrect AI advice on AI-assisted decision-making, the impact of incorrect explanations is neglected so far. Additionally, recent work shows that not only different explanation modalities impact decision-makers, but also human factors play a critical role. To analyze relevant phenomena influencing AI-assisted decision-making, this work explores the impacting factors by conceptualizing theories of appropriate reliance and taking the first steps toward empirical evidence. We show that humans’ reliance on AI and the human–AI team performance are impacted by imperfect XAI in a study with 136 participants. Additionally, we find that cognitive styles affect decision-making in different explanation modalities. Hence, we shed light on ...

   @article{spitzer2025imperfecti,
     abbr = {Journal Paper},
     bibtex_show = {true},
     title = {Imperfections of XAI: Phenomena Influencing AI-Assisted Decision-Making},
     author = {Spitzer, Philipp and Morrison, Katelyn and Turri, Violet and Feng, Michelle and Perer, Adam and Kühl, Niklas},
     journal = {ACM Transactions on Interactive Intelligent Systems},
     publisher = {ACM},
     volume = {15},
     number = {3},
     pages = {1-40},
     year = {2025},
     pub_year = {2025},
     citation = {ACM Transactions on Interactive Intelligent Systems 15 (3), 1-40, 2025},
     html = {https://dl.acm.org/doi/pdf/10.1145/3750052}
   }

7. Workshop Paper

   Establishing the Cooperative Game Wavelength as a Testbed to Explore Mutual Theory of Mind

   Morrison, Katelyn, Ashktorab, Zahra, Bouneffouf, Djallel, and Enrique, Gabriel

   ToM4AI 2025 2025

   Abs Bib HTML

   Machine learning (ML) and humancentered AI (HCAI) researchers have considered numerous methods to evaluate Theory of Mind (ToM)-like capabilities in artiĄcial intelligence (AI). These methods have independently captured multiple aspects of ToM capabilities (ie, beliefs, knowledge). Recent research has proposed exploring Mutual Theory of Mind (MToM) as a way to understand how a humanŠs mental model and an AIŠs user model can be mutually shaped to beneĄt future interactions. However, there is a lack of methods for understanding the development and impact of MToM-like capabilities in human-AI teams. We propose using a collaborative party game called Wavelength as a testbed to explore the complexities of MToM-like capabilities in human-AI teams. We compare Wavelength to other methods (ie, Overcooked, Hanabi) and discuss how game mechanics help players mutually construct, recognize, and revise their models of their teammates. Lastly, we brieĆy suggest how future work can explore MToM with Wavelength.

   @article{morrison2025establishi,
     abbr = {Workshop Paper},
     bibtex_show = {true},
     title = {Establishing the Cooperative Game Wavelength as a Testbed to Explore Mutual Theory of Mind},
     author = {Morrison, Katelyn and Ashktorab, Zahra and Bouneffouf, Djallel and Enrique, Gabriel},
     journal = {ToM4AI 2025},
     pages = {54},
     year = {2025},
     pub_year = {2025},
     citation = {ToM4AI 2025, 54, 2025},
     html = {https://bit.ly/4op0ui8}
   }

8. Workshop

   Hybrid Automation Experiences–Communication, Coordination, and Collaboration within Human-AI Teams

   Spitzer, Philipp, Baldauf, Matthias, Palanque, Philippe, Roto, Virpi, Morrison, Katelyn, Zipperling, Domenique, and Holstein, Joshua

   2025

   Abs Bib HTML

   Automated systems and AI-assisted workflows are evolving from support tools to sophisticated collaborators in so-called “hybrid” teams with human and AI-based members. This evolution presents new challenges and opportunities in the field of “Automation Experience”, particularly in how these team members communicate, coordinate, and collaborate. This workshop aims to contribute to the design of automated systems that augment humans’ capabilities, maintain human autonomy, and create positive automation experiences in human-AI teams. Through presentations, a keynote talk, discussions, and interactive collaborative activities, researchers and practitioners from different fields will address challenges regarding communication, coordination, and collaboration within hybrid human-AI teams. The workshop aims to spark innovative research directions and foster collaborative interdisciplinary initiatives that ...

   @misc{spitzer2025hybridauto,
     abbr = {Workshop},
     bibtex_show = {true},
     title = {Hybrid Automation Experiences–Communication, Coordination, and Collaboration within Human-AI Teams},
     author = {Spitzer, Philipp and Baldauf, Matthias and Palanque, Philippe and Roto, Virpi and Morrison, Katelyn and Zipperling, Domenique and Holstein, Joshua},
     pages = {1-6},
     year = {2025},
     pub_year = {2025},
     citation = {Proceedings of the Extended Abstracts of the CHI Conference on Human Factors ..., 2025},
     html = {https://dl.acm.org/doi/abs/10.1145/3706599.3706738}
   }

2024

1. Conference Paper

   MedSyn: Text-guided Anatomy-aware Synthesis of High-Fidelity 3D CT Images

   Xu, Yanwu, Sun, Li, Peng, Wei, Jia, Shuyue, Morrison, Katelyn, Perer, Adam, Zandifar, Afrooz, Visweswaran, Shyam, Eslami, Motahhare, and Batmanghelich, Kayhan

   IEEE Transactions on Medical Imaging 2024

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   This paper introduces an innovative methodology for producing high-quality 3D lung CT images guided by textual information. While diffusion-based generative models are increasingly used in medical imaging, current state-of-the-art approaches are limited to low-resolution outputs and underutilize radiology reports’ abundant information. The radiology reports can enhance the generation process by providing additional guidance and offering fine-grained control over the synthesis of images. Nevertheless, expanding text-guided generation to high-resolution 3D images poses significant memory and anatomical detail-preserving challenges. Addressing the memory issue, we introduce a hierarchical scheme that uses a modified UNet architecture. We start by synthesizing low-resolution images conditioned on the text, serving as a foundation for subsequent generators for complete volumetric data. To ensure the ...

   @article{xu2024medsyntext,
     abbr = {Conference Paper},
     bibtex_show = {true},
     title = {MedSyn: Text-guided Anatomy-aware Synthesis of High-Fidelity 3D CT Images},
     author = {Xu, Yanwu and Sun, Li and Peng, Wei and Jia, Shuyue and Morrison, Katelyn and Perer, Adam and Zandifar, Afrooz and Visweswaran, Shyam and Eslami, Motahhare and Batmanghelich, Kayhan},
     journal = {IEEE Transactions on Medical Imaging},
     publisher = {IEEE},
     year = {2024},
     pub_year = {2024},
     citation = {IEEE Transactions on Medical Imaging, 2024},
     pdf = {https://arxiv.org/pdf/2310.03559}
   }

2. Conference Paper

   The Impact of Imperfect XAI on Human-AI Decision-Making

   Morrison, Katelyn, Spitzer, Philipp, Turri, Violet, Feng, Michelle, Kühl, Niklas, and Perer, Adam

   ACM SIGCHI Conference on Computer-Supported Cooperative Work & Social Computing (CSCW) 2024

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   Explainability techniques are rapidly being developed to improve human-AI decision-making across various cooperative work settings. Consequently, previous research has evaluated how decision-makers collaborate with imperfect AI by investigating appropriate reliance and task performance with the aim of designing more human-centered computer-supported collaborative tools. Several human-centered explainable AI (XAI) techniques have been proposed in hopes of improving decision-makers’ collaboration with AI; however, these techniques are grounded in findings from previous studies that primarily focus on the impact of incorrect AI advice. Few studies acknowledge the possibility of the explanations being incorrect even if the AI advice is correct. Thus, it is crucial to understand how imperfect XAI affects human-AI decision-making. In this work, we contribute a robust, mixed-methods user study with 136 ...

   @article{morrison2024theimpacto,
     abbr = {Conference Paper},
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     selected = {true},
     title = {The Impact of Imperfect XAI on Human-AI Decision-Making},
     author = {Morrison, Katelyn and Spitzer, Philipp and Turri, Violet and Feng, Michelle and Kühl, Niklas and Perer, Adam},
     journal = {ACM SIGCHI Conference on Computer-Supported Cooperative Work & Social Computing (CSCW)},
     year = {2024},
     pub_year = {2024},
     citation = {ACM SIGCHI Conference on Computer-Supported Cooperative Work & Social ..., 2024},
     html = {https://arxiv.org/pdf/2307.13566.pdf}
   }

3. Conference Paper

   Transparency in the wild: Navigating transparency in a deployed ai system to broaden need-finding approaches

   Turri, Violet, Morrison, Katelyn, Robinson, Katherine-Marie, Abidi, Collin, Perer, Adam, Forlizzi, Jodi, and Dzombak, Rachel

   2024

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   Transparency is a critical component when building artificial intelligence (AI) decision-support tools, especially for contexts in which AI outputs impact people or policy. Effectively identifying and addressing user transparency needs in practice remains a challenge. While a number of guidelines and processes for identifying transparency needs have emerged, existing methods tend to approach need-finding with a limited focus that centers around a narrow set of stakeholders and transparency techniques. To broaden this perspective, we employ numerous need-finding methods to investigate transparency mechanisms in a widely deployed AI-decision support tool developed by a wildlife conservation non-profit. Throughout our 5-month case study, we conducted need-finding through semi-structured interviews with end-users, analysis of the tool’s community forum, experiments with their ML model, and analysis of ...

   @misc{turri2024transparen,
     abbr = {Conference Paper},
     bibtex_show = {true},
     title = {Transparency in the wild: Navigating transparency in a deployed ai system to broaden need-finding approaches},
     author = {Turri, Violet and Morrison, Katelyn and Robinson, Katherine-Marie and Abidi, Collin and Perer, Adam and Forlizzi, Jodi and Dzombak, Rachel},
     pages = {1494-1514},
     year = {2024},
     pub_year = {2024},
     citation = {Proceedings of the 2024 ACM Conference on Fairness, Accountability, and ..., 2024},
     pdf = {https://dl.acm.org/doi/pdf/10.1145/3630106.3658985}
   }

4. Conference Paper

   AI-Powered Reminders for Collaborative Tasks: Experiences and Futures

   Morrison, Katelyn, Iqbal, Shamsi T, and Horvitz, Eric

   Proceedings of the ACM on Human-Computer Interaction 2024

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   Email continues to serve as a central medium for managing collaborations. While unstructured email messaging is lightweight and conducive to coordination, it is easy to overlook commitments and requests for collaborations that are embedded in the text of free-flowing communications. Twenty-one years ago, Bellotti et al. proposed TaskMaster with the goal of redesigning the email interface to have explicit task management capabilities. Recently, AI-based task recognition and reminder services have been introduced in major email systems as one approach to managing asynchronous collaborations. While these services have been provided to millions of people around the world, there is little understanding of how people interact with and benefit from them. We explore knowledge workers’ experiences with Microsoft’s Viva Daily Briefing Email to better understand how AI-powered reminders can support ...

   @article{morrison2024aipoweredr,
     abbr = {Conference Paper},
     bibtex_show = {true},
     title = {AI-Powered Reminders for Collaborative Tasks: Experiences and Futures},
     author = {Morrison, Katelyn and Iqbal, Shamsi T and Horvitz, Eric},
     journal = {Proceedings of the ACM on Human-Computer Interaction},
     publisher = {ACM},
     volume = {8},
     number = {CSCW1},
     pages = {1-20},
     year = {2024},
     pub_year = {2024},
     citation = {Proceedings of the ACM on Human-Computer Interaction 8 (CSCW1), 1-20, 2024},
     html = {}
   }

5. Workshop Paper

   Imperfect Natural Language Explanations in Human-AI Decision-Making

   Morrison, K., Spitzer, P., Turri, V., Feng, M., Kühl, N., and Perer, A.

   TREW Workshop at ACM CHI 2024 2024

   Bib PDF

   @article{morrison2024trew,
     bibtex_show = {true},
     abbr = {Workshop Paper},
     title = {Imperfect Natural Language Explanations in Human-AI Decision-Making},
     author = {Morrison, K. and Spitzer, P. and Turri, V. and Feng, M. and Kühl, N. and Perer, A.},
     journal = {TREW Workshop at ACM CHI 2024},
     year = {2024},
     pdf = {24_Chi_Trew_ImperfectXAI.pdf}
   }

2023

1. Conference Paper

   Evaluating the Impact of Human Explanation Strategies on Human-AI Visual Decision-Making

   Morrison, Katelyn, Shin, Donghoon, Holstein, Kenneth, and Perer, Adam

   2023

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   Artificial intelligence (AI) is increasingly being deployed in high-stakes domains, such as disaster relief and radiology, to aid practitioners during the decision-making process. Explainable AI techniques have been developed and deployed to provide users insights into why the AI made certain predictions. However, recent research suggests that these techniques may confuse or mislead users. We conducted a series of two studies to uncover strategies that humans use to explain decisions and then understand how those explanation strategies impact visual decision-making. In our first study, we elicit explanations from humans when assessing and localizing damaged buildings after natural disasters from satellite imagery and identify four core explanation strategies that humans employed. We then follow up by studying the impact of these explanation strategies by framing the explanations from Study 1 as if they were ...

   @misc{morrison2023evaluating,
     abbr = {Conference Paper},
     bibtex_show = {true},
     title = {Evaluating the Impact of Human Explanation Strategies on Human-AI Visual Decision-Making},
     author = {Morrison, Katelyn and Shin, Donghoon and Holstein, Kenneth and Perer, Adam},
     year = {2023},
     pub_year = {2023},
     citation = {ACM SIGCHI Conference on Computer-Supported Cooperative Work & Social ..., 2023},
     conference = {ACM SIGCHI Conference on Computer-Supported Cooperative Work & Social Computing (CSCW)},
     pdf = {23_cscw_explanation_characteristics.pdf}
   }

2. Conference Paper

   Eye into AI: Evaluating the Interpretability of Explainable AI Techniques through a Game With a Purpose

   Morrison, Katelyn, Jain, Mayank, Hammer, Jessica, and Perer, Adam

   ACM SIGCHI Conference on Computer-Supported Cooperative Work & Social Computing (CSCW) 2023

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   Recent developments in explainable AI (XAI) aim to improve the transparency of black-box models. However, empirically evaluating the interpretability of these XAI techniques is still an open challenge. The most common evaluation method is algorithmic performance, but such an approach may not accurately represent how interpretable these techniques are to people. A less common but growing evaluation strategy is to leverage crowd-workers to provide feedback on multiple XAI techniques to compare them. However, these tasks often feel like work and may limit participation. We propose a novel, playful, human-centered method for evaluating XAI techniques: a Game With a Purpose (GWAP), Eye into AI, that allows researchers to collect human evaluations of XAI at scale. We provide an empirical study demonstrating how our GWAP supports evaluating and comparing the agreement between three popular XAI ...

   @article{morrison2023eyeintoaie,
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     bibtex_show = {true},
     title = {Eye into AI: Evaluating the Interpretability of Explainable AI Techniques through a Game With a Purpose},
     author = {Morrison, Katelyn and Jain, Mayank and Hammer, Jessica and Perer, Adam},
     journal = {ACM SIGCHI Conference on Computer-Supported Cooperative Work & Social Computing (CSCW)},
     year = {2023},
     pub_year = {2023},
     citation = {ACM SIGCHI Conference on Computer-Supported Cooperative Work & Social ..., 2023},
     html = {}
   }

3. Conference Paper

   Shared interest... sometimes: Understanding the alignment between human perception, vision architectures, and saliency map techniques

   Morrison, Katelyn, Mehra, Ankita, and Perer, Adam

   2023

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   Empirical studies have shown that attention-based architectures outperform traditional convolutional neural networks (CNN) in terms of accuracy and robustness. As a result, attention-based architectures are increasingly used in high-stakes domains such as radiology and wildlife conservation to aid in decision-making. However, understanding how attention-based architectures compare to CNNs regarding alignment with human perception is still under-explored. Previous studies exploring how vision architectures align with human perception evaluate a single architecture with multiple explainability techniques or multiple architectures with a single explainability technique. Through an empirical analysis, we investigate how two attention-based architectures and two CNNs for two saliency map techniques align with the ground truth for human perception on 100 images from an interpretability benchmark dataset. Using the Shared Interest metrics, we found that CNNs align more with human perception when using the XRAI saliency map technique. However, we found the opposite for Grad-CAM. We discuss the implications of our analysis for human-centered explainable AI and introduce directions for future work.

   @misc{morrison2023sharedinte,
     abbr = {Conference Paper},
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     title = {Shared interest... sometimes: Understanding the alignment between human perception, vision architectures, and saliency map techniques},
     author = {Morrison, Katelyn and Mehra, Ankita and Perer, Adam},
     pages = {3776-3781},
     year = {2023},
     pub_year = {2023},
     citation = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern ..., 2023},
     conference = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
     pdf = {23_XAI4CV_cvpr.pdf}
   }

2022

1. Workshop Paper

   Towards Generating Human-Centered Saliency Maps without Significantly Sacrificing Accuracy

   Aswal, Vivek, Kao, Gore, Kim, Seo Young, and Morrison, Katelyn

   NeuroVision 2022 Workshop at CVPR 2022

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   As deep neural networks make significant advances in computer vision tasks, they are being deployed in several high-stakes domains. However, these models are not always semantically meaningful to humans as traditional interpretability techniques are quantitatively driven. Therefore, we explore how to generate saliency maps that are more similar to human attention without significantly sacrificing the model performance. We conduct an empirical study to understand how current object detection models compare to human centered saliency maps. Additionally, we present different data augmentation techniques such as Selective Erasing and Selective Inpainting along with the prevelant non-trivial transforms to evaluate the impact of human-centered data augmentation. With less than 3% mAP difference, we observe that data augmentations that are derived from predicted human attention improves the MAE and IoU between the model saliency and predicted attention. Visualization and more details are at here.

   @article{aswal2022towardsgen,
     abbr = {Workshop Paper},
     bibtex_show = {true},
     title = {Towards Generating Human-Centered Saliency Maps without Significantly Sacrificing Accuracy},
     author = {Aswal, Vivek and Kao, Gore and Kim, Seo Young and Morrison, Katelyn},
     journal = {NeuroVision 2022 Workshop at CVPR},
     year = {2022},
     pub_year = {2022},
     citation = {NeuroVision 2022 Workshop at CVPR, 2022},
     html = {http://katelyn98.github.io/blog/2022/vlr-project/},
     pdf = {19_CameraReady_CVPR2022_NeuroVision_SHORT_CameraReady.pdf},
     slides = {CVPR_Lightning_Talk.pdf}
   }

2021

1. Workshop Paper

   Exploring Corruption Robustness: Inductive Biases in Vision Transformers and MLP-Mixers

   Morrison, Katelyn, Gilby, Benjamin, Lipchak, Colton, Mattioli, Adam, and Kovashka, Adriana

   2021

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   Recently, vision transformers and MLP-based models have been developed in order to address some of the prevalent weaknesses in convolutional neural networks. Due to the novelty of transformers being used in this domain along with the self-attention mechanism, it remains unclear to what degree these architectures are robust to corruptions. Despite some works proposing that data augmentation remains essential for a model to be robust against corruptions, we propose to explore the impact that the architecture has on corruption robustness. We find that vision transformer architectures are inherently more robust to corruptions than the ResNet-50 and MLP-Mixers. We also find that vision transformers with 5 times fewer parameters than a ResNet-50 have more shape bias. Our code is available to reproduce.

   @misc{morrison2021exploringc,
     abbr = {Workshop Paper},
     bibtex_show = {true},
     title = {Exploring Corruption Robustness: Inductive Biases in Vision Transformers and MLP-Mixers},
     author = {Morrison, Katelyn and Gilby, Benjamin and Lipchak, Colton and Mattioli, Adam and Kovashka, Adriana},
     year = {2021},
     pub_year = {2021},
     citation = {Uncertainty and Robustness in Deep Learning Workshop at ICML 2021, 2021},
     conference = {Uncertainty and Robustness in Deep Learning Workshop at ICML 2021},
     html = {https://arxiv.org/abs/2106.13122},
     pdf = {exploring_corruption.pdf},
     code = {https://github.com/katelyn98/CorruptionRobustness}
   }

2020

1. Conference Paper

   Using Object Tracking Techniques to Non-Invasively Measure Thoracic Rotation Range of Motion

   Morrison, Katelyn, Yates, Daniel, Roman, Maya, and Clark, William W

   Adjunct Proceedings of the ACM International Conference on Multimodal Interaction (ICMI 2020), Utrecht, the Netherlands 2020

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   Different measuring instruments, such as a goniometer, have been used by clinicians to measure a patient’s ability to rotate their thoracic spine. Despite the simplicity of goniometers, this instrument requires the user to decipher the resulting measurement properly. The correctness of these measurements are imperative for clinicians to properly identify and evaluate injuries or help athletes enhance their overall performance. This paper introduces a goniometer-free, noninvasive measuring technique using a Raspberry Pi, a Pi Camera module, and software for clinicians to measure a subject’s thoracic rotation range of motion (ROM) when administering the seated rotation technique with immediate measurement feedback. Determining this measurement is achieved by applying computer vision object tracking techniques on a live video feed from the Pi Camera that is secured on the ceiling above the subject ...

   @article{morrison2020usingobjec,
     abbr = {Conference Paper},
     bibtex_show = {true},
     title = {Using Object Tracking Techniques to Non-Invasively Measure Thoracic Rotation Range of Motion},
     author = {Morrison, Katelyn and Yates, Daniel and Roman, Maya and Clark, William W},
     journal = {Adjunct Proceedings of the ACM International Conference on Multimodal Interaction (ICMI 2020), Utrecht, the Netherlands},
     year = {2020},
     pub_year = {2020},
     citation = {Adjunct Proceedings of the ACM International Conference on Multimodal ..., 2020},
     html = {}
   }

2. Workshop Paper

   Reducing Discrimination in Learning Algorithms for Social Good in Sociotechnical Systems

   Morrison, Katelyn

   AI for Social Good Workshop at IJCAI-PRICAI 2020 2020

   Abs Bib HTML

   Sociotechnical systems within cities are now equipped with machine learning algorithms in hopes to increase efficiency and functionality by modeling and predicting trends. Machine learning algorithms have been applied in these domains to address challenges such as balancing the distribution of bikes throughout a city and identifying demand hotspots for ride sharing drivers. However, these algorithms applied to challenges in sociotechnical systems have exacerbated social inequalities due to previous bias in data sets or the lack of data from marginalized communities. In this paper, I will address how smart mobility initiatives in cities use machine learning algorithms to address challenges. I will also address how these algorithms unintentionally discriminate against features such as socioeconomic status to motivate the importance of algorithmic fairness. Using the bike sharing program in Pittsburgh, PA, I will present a position on how discrimination can be eliminated from the pipeline using Bayesian Optimization.

   @article{morrison2020reducingdi,
     abbr = {Workshop Paper},
     bibtex_show = {true},
     title = {Reducing Discrimination in Learning Algorithms for Social Good in Sociotechnical Systems},
     author = {Morrison, Katelyn},
     journal = {AI for Social Good Workshop at IJCAI-PRICAI 2020},
     year = {2020},
     pub_year = {2020},
     citation = {AI for Social Good Workshop at IJCAI-PRICAI 2020, 2020},
     html = {https://arxiv.org/abs/2011.13988}
   }