Katelyn Morrison
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.
Katelyn Morrison, Philipp Spitzer, Violet Turri, Michelle Feng, Niklas Kühl, Adam Perer
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 participants to evaluate how incorrect explanations influence humans' decision-making behavior in a bird species identification task, taking into account their level of expertise and an explanation's level of assertiveness. Our findings reveal the influence of imperfect XAI and humans' level of expertise on their reliance on AI and human-AI team performance. We also discuss how explanations can deceive decision-makers during human-AI collaboration. Hence, we shed light on the impacts of imperfect XAI in the field of computer-supported cooperative work and provide guidelines for designers of human-AI collaboration systems.
Katelyn Morrison, Arpit Mathur, Aidan Bradshaw, Tom Wartmann, Steven Lundi, Afrooz Zandifar, Weichang Dai, Kayhan Batmanghelich, Motahhare Eslami, Adam Perer
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.
Katelyn Morrison, Shamsi Iqbal, Eric Horvitz
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 asynchronous collaborations. Through semi-structured interviews and surveys, we shed light on how AI-powered reminders are incorporated into workflows to support asynchronous collaborations. We identify what knowledge workers prefer AI-powered reminders to remind them about and how they would like to interact with these reminders. Using mixed methods and a self-assessment methodology, we investigate the relationship between information workers' work styles and the perceived value of the Viva Daily Briefing Email to identify users who are more likely to benefit from AI-powered reminders for asynchronous collaborations. We conclude by discussing the experiences and futures of AI-powered reminders for collaborative tasks and asynchronous collaborations.
Mouad Abrini, Omri Abend, Dina Acklin, Henny Admoni, Gregor Aichinger, Nitay Alon, Zahra Ashktorab, Ashish Atreja, Moises Auron, Alexander Aufreiter, Raghav Awasthi, Soumya Banerjee, Joe M. Barnby, Rhea Basappa, Severin Bergsmann, Djallel Bouneffouf, Patrick Callaghan, Marc Cavazza, Thierry Chaminade, Sonia Chernova, Mohamed Chetouan, Moumita Choudhury, Axel Cleeremans, Jacek B. Cywinski, Fabio Cuzzolin, Hokin Deng, N'yoma Diamond, Camilla Di Pasquasio, Guillaume Dumas, Max van Duijn, Mahapatra Dwarikanath, Qingying Gao, Ashok Goel, Rebecca Goldstein, Matthew Gombolay, Gabriel Enrique Gonzalez, Amar Halilovic, Tobias Halmdienst, Mahimul Islam, Julian Jara-Ettinger, Natalie Kastel, Renana Keydar, Ashish K. Khanna, Mahdi Khoramshahi, JiHyun Kim, MiHyeon Kim, YoungBin Kim, Senka Krivic, Nikita Krasnytskyi, Arun Kumar, JuneHyoung Kwon, Eunju Lee, Shane Lee, Peter R. Lewis, Xue Li, Yijiang Li, Michal Lewandowski, Nathan Lloyd, Matthew B. Luebbers, Dezhi Luo, Haiyun Lyu, Dwarikanath Mahapatra, Kamal Maheshwari, Mallika Mainali, Piyush Mathur, Patrick Mederitsch, Shuwa Miura, Manuel Preston de Miranda, Reuth Mirsky, Shreya Mishra, Nina Moorman, Katelyn Morrison, John Muchovej, Bernhard Nessler, Felix Nessler, Hieu Minh Jord Nguyen, Abby Ortego, Francis A. Papay, Antoine Pasquali, Hamed Rahimi, Charumathi Raghu, Amanda Royka, Stefan Sarkadi, Jaelle Scheuerman, Simon Schmid, Paul Schrater, Anik Sen, Zahra Sheikhbahaee, Ke Shi, Reid Simmons, Nishant Singh, Mason O. Smith, Ramira van der Meulen, Anthia Solaki, Haoran Sun, Viktor Szolga, Matthew E. Taylor, Travis Taylor, Sanne Van Waveren, Juan David Vargas, Rineke Verbrugge, Eitan Wagner, Justin D. Weisz, Ximing Wen, William Yeoh, Wenlong Zhang, Michelle Zhao, Shlomo Zilberstein
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.
Katelyn Morrison, Benjamin Gilby, Colton Lipchak, Adam Mattioli, Adriana Kovashka
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.
Venkatesh Sivaraman, Katelyn Morrison, Will Epperson, Adam Perer
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.
Philipp Spitzer, Joshua Holstein, Katelyn Morrison, Kenneth Holstein, Gerhard Satzger, Niklas Kühl
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 ran 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 explanations compromise human-AI team-performance during collaboration. With our work, we contribute to HCI by providing empirical evidence for the negative consequences of incorrect explanations on humans post-collaboration and outlining guidelines for designers of AI.
Yanwu Xu, Li Sun, Wei Peng, Shuyue Jia, Katelyn Morrison, Adam Perer, Afrooz Zandifar, Shyam Visweswaran, Motahhare Eslami, Kayhan Batmanghelich
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 anatomical plausibility of the generated samples, we provide further guidance by generating vascular, airway, and lobular segmentation masks in conjunction with the CT images. The model demonstrates the capability to use textual input and segmentation tasks to generate synthesized images. The results of comparative assessments indicate that our approach exhibits superior performance compared to the most advanced models based on GAN and diffusion techniques, especially in accurately retaining crucial anatomical features such as fissure lines, airways, and vascular structures. This innovation introduces novel possibilities. This study focuses on two main objectives: (1) the development of a method for creating images based on textual prompts and anatomical components, and (2) the capability to generate new images conditioning on anatomical elements. The advancements in image generation can be applied to enhance numerous downstream tasks.
Shantanu Ghosh, Vedant Parthesh Joshi, Rayan Syed, Param Budhraja, Aya Kassem, Katelyn C. Morrison, Alex Tang, Ho Cheung Aiden Wong, Abhishek Varshney, Payel Basak, Weicheng Dai, Judy Wawira Gichoya, Hari M. Trivedi, Imon Banerjee, Shyam Visweswaran, Clare B. Poynton, Kayhan Batmanghelich
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.