Nikola Banovic

@inproceedings{10.1145/3772363.3778736, author = {Khadar, Malik and Cecil, Julia and van der Neut, Leon and Banovic, Nikola and Baum, Kevin and Chancellor, Stevie and Costanza, Enrico and Eslami, Motahhare and Feit, Anna Maria and Gaube, Susanne and Gadiraju, Ujwal and Kaur, Harmanpreet}, title = {AI CHAOS! 2$^{nd}$ Workshop on the Challenges for Human Oversight of AI Systems}, year = {2026}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3772363.3778736}, doi = {10.1145/3772363.3778736}, booktitle = {Extended Abstracts of the 2026 CHI Conference on Human Factors in Computing Systems}, numpages = {7}, location = {Barcelona, Spain}, series = {CHI EA '26} } @article{10.1145/3797885, author = {Dani, Trisha and Antar, Anindya Das and Kratz, Anna and Banovic, Nikola}, title = {Beyond Conventional Health Technologies: Investigating Design Opportunities for Improving Self-management in People with Multiple Sclerosis}, year = {2026}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3797885}, doi = {10.1145/3797885}, abstract = {People with Multiple Sclerosis (MS) who can self-track and self-manage their symptoms and condition are more likely to stay active and independent. Although existing health technologies could support people with MS outside of clinical settings (e.g., through remote consultations, symptom tracking), care remains largely clinician- or caregiver-driven. To understand how people with MS use existing health technologies to track and manage their symptoms and condition, we conducted a remote contextual inquiry with nine participants with MS. Our findings show that while limited access to clinicians creates opportunities for telehealth tools, few participants used existing conventional health tools specifically designed for tracking symptoms, monitoring disease progression, or managing functioning. Instead, they adapted a mix of analog and digital technologies to implement self-driven strategies that reduced cognitive load, maintained positivity, and supported self-image. Our insights inform the design of future health technologies that prioritize self-management strategies, such as self-reflection, self-care, and intentional guidance, to address MS-related symptoms, mitigate social isolation, and enhance self-efficacy.}, note = {Just Accepted}, journal = {ACM Trans. Comput. Healthcare}, month = feb, keywords = {Multiple Sclerosis, self-management, contextual inquiry, qualitative research, assistive health technology, telehealth, symptom tracking, self-care, symptom management, self-efficacy} } @article{10.1145/3757437, author = {Molaei, Somayeh and Robert, Lionel Peter and Banovic, Nikola}, title = {What Do People Want to Know about Artificial Intelligence (AI)? The Importance of Answering End-user Questions to Explain Autonomous Vehicle (AV) Decisions}, year = {2025}, issue_date = {November 2025}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, volume = {9}, number = {7}, url = {https://doi.org/10.1145/3757437}, doi = {10.1145/3757437}, abstract = {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.}, journal = {Proc. ACM Hum.-Comput. Interact.}, month = oct, articleno = {CSCW256}, numpages = {32}, keywords = {XAI, conversational XAI interface, explainability, explainable AI, human-centered XAI, human-centered artificial intelligence, interactive explanations, user-led explanations} } @article{10.1145/3757496, author = {Eschebach, Tess and Banovic, Nikola and McDonald, Allison}, title = {Playing 'Google's Game': How Educational YouTubers Manage Tensions Between Education and Monetization}, year = {2025}, issue_date = {November 2025}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, volume = {9}, number = {7}, url = {https://doi.org/10.1145/3757496}, doi = {10.1145/3757496}, abstract = {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.}, journal = {Proc. ACM Hum.-Comput. Interact.}, month = oct, articleno = {CSCW315}, numpages = {33}, keywords = {YouTube, YouTube monetization, algorithmic labor, content creators, creator economy, digital patronage, educational content creation, educational media, public broadcast} } @article{Das_Antar_Huan_Banovic_2025, title={"Do Your Guardrails Even Guard?" Method for Evaluating Effectiveness of Moderation Guardrails in Aligning LLM Outputs with Expert User Expectations}, volume={8}, url={https://ojs.aaai.org/index.php/AIES/article/view/36583}, DOI={10.1609/aies.v8i1.36583}, abstractNote={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.}, number={1}, journal={Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society}, author={Das Antar, Anindya and Huan, Xun and Banovic, Nikola}, year={2025}, month={Oct.}, pages={705-718} } @inproceedings{10.1145/3706598.3713714, author = {Prabhudesai, Snehal and Kasi, Ananya P. and Mansingh, Anmol and Das Antar, Anindya and Shen, Hua and Banovic, Nikola}, title = {"Here the GPT made a choice, and every choice can be biased": How Students Critically Engage with LLMs through End-User Auditing Activity}, year = {2025}, isbn = {9798400713941}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3706598.3713714}, doi = {10.1145/3706598.3713714}, abstract = {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.}, booktitle = {Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems}, articleno = {1015}, numpages = {23}, keywords = {End-user Audit, End-user Algorithmic Audit, User-Driven Algorithm Auditing, Algorithmic Audit, Auditing Algorithms, Algorithmic Bias, Algorithmic Harm, Large Language Models, LLMs, AI Literacy, AI Education, Responsible AI.}, location = { }, series = {CHI '25} } @INPROCEEDINGS{2024AGUFMGC53H0438H, author = {{Huan}, Xun and {Ivanov}, Valeriy Yu and {Dominguez}, Francina and {Ziker}, John and Banovic, Nikola and {Gonzalez}, Richard and {Jewett}, Brian F. and {Cheng}, Chen and {Tran}, Vinh and {Prabhudesai}, Snehal and {Putri}, Deffi and {Gray}, Kevin and {Rath}, Sudhansu and {Masterson}, Remi and {Whitaker}, Sarah}, title = "{A physics-human integrated decision-making framework for DoD installations under flood risks}", booktitle = {AGU Fall Meeting Abstracts}, year = 2024, month = dec, eid = {GC53H-0438}, adsurl = {https://ui.adsabs.harvard.edu/abs/2024AGUFMGC53H0438H}, adsnote = {Provided by the SAO/NASA Astrophysics Data System} } @article{10.1145/3686937, author = {Escher, Nel and Bilik, Jeffrey and Banovic, Nikola and Green, Ben}, title = {Code-ifying the Law: How Disciplinary Divides Afflict the Development of Legal Software}, year = {2024}, issue_date = {November 2024}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, volume = {8}, number = {CSCW2}, url = {https://doi.org/10.1145/3686937}, doi = {10.1145/3686937}, abstract = {Proponents of legal automation believe that translating the law into code can improve the legal system. However, research and reporting suggest that legal software systems often contain flawed translations of the law, resulting in serious harms such as terminating children's healthcare and charging innocent people with fraud. Efforts to identify and contest these mistranslations after they arise treat the symptoms of the problem, but fail to prevent them from emerging. Meanwhile, existing recommendations to improve the development of legal software remain untested, as there is little empirical evidence about the translation process itself. In this paper, we investigate the behavior of fifteen teams---nine composed of only computer scientists and six of computer scientists and legal experts---as they attempt to translate a bankruptcy statute into software. Through an interpretative qualitative analysis, we characterize a significant epistemic divide between computer science and law and demonstrate that this divide contributes to errors, misunderstandings, and policy distortions in the development of legal software. Even when development teams included legal experts, communication breakdowns meant that the resulting tools predominantly presented incorrect legal advice and adopted inappropriately harsh legal standards. Study participants did not recognize the errors in the tools they created. We encourage policymakers and researchers to approach legal software with greater skepticism, as the disciplinary divide between computer science and law creates an endemic source of error and mistranslation in the production of legal software.}, journal = {Proc. ACM Hum.-Comput. Interact.}, month = nov, articleno = {398}, numpages = {37}, keywords = {automated legal systems, law, legal software development} } @inproceedings{10.1145/3654777.3676323, author = {Das Antar, Anindya and Molaei, Somayeh and Chen, Yan-Ying and Lee, Matthew L and Banovic, Nikola}, title = {VIME: Visual Interactive Model Explorer for Identifying Capabilities and Limitations of Machine Learning Models for Sequential Decision-Making}, year = {2024}, isbn = {9798400706288}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3654777.3676323}, doi = {10.1145/3654777.3676323}, abstract = {Ensuring that Machine Learning (ML) models make correct and meaningful inferences is necessary for the broader adoption of such models into high-stakes decision-making scenarios. Thus, ML model engineers increasingly use eXplainable AI (XAI) tools to investigate the capabilities and limitations of their ML models before deployment. However, explaining sequential ML models, which make a series of decisions at each timestep, remains challenging. We present Visual Interactive Model Explorer (VIME), an XAI toolbox that enables ML model engineers to explain decisions of sequential models in different “what-if” scenarios. Our evaluation with 14 ML experts, who investigated two existing sequential ML models using VIME and a baseline XAI toolbox to explore “what-if” scenarios, showed that VIME made it easier to identify and explain instances when the models made wrong decisions compared to the baseline. Our work informs the design of future interactive XAI mechanisms for evaluating sequential ML-based decision support systems.}, booktitle = {Proceedings of the 37th Annual ACM Symposium on User Interface Software and Technology}, articleno = {59}, numpages = {21}, keywords = {Explainable AI, XAI, explainability, interactive model exploration, interpretability, sequential decision-making.}, location = {Pittsburgh, PA, USA}, series = {UIST '24} } @article{10.4300/JGME-D-23-00823.1, author = {Quinonez, Shane C. and Stewart, David A. and Banovic, Nikola}, title = "{ChatGPT and Artificial Intelligence in Graduate Medical Education Program Applications}", journal = {Journal of Graduate Medical Education}, volume = {16}, number = {4}, pages = {391-394}, year = {2024}, month = {08}, abstract = "{Artificial intelligence (AI) is a broad-ranging term describing any machine-based system that can, for a given set of human-defined objectives, make predictions, recommendations, or decisions influencing real or virtual environments. To date, most areas of medicine have been affected by AI, and this will likely continue with technological advances.1 AI chatbot technologies, including Chat Generative Pre-Trained Transformer (ChatGPT) and similar generative AI systems, have gained attention due to their ability to generate text almost indistinguishable from human-generated text.2 GPT-3 and GPT-4, the large language models powering ChatGPT, enable a chat interface allowing users to refine inputted requests or to converse with the program, similar to an online human-to-human chat. How generative AIs have been used, combined with their additional capabilities, has raised alarm for some regarding their influence in various fields.3 ChatGPT’s claimed capabilities include the ability to write code in more than 20 programming languages, analyze and summarize blocks of text, and write text on any topic,4 though the fidelity and full range of these capabilities has yet to be truly assessed. Examples of generative AI’s text generation capabilities could be a letter of recommendation (LOR) for a medical student applying for residency or the personal statement of that same student as part of their application.}", issn = {1949-8349}, doi = {10.4300/JGME-D-23-00823.1}, url = {https://doi.org/10.4300/JGME-D-23-00823.1}, eprint = {https://meridian.allenpress.com/jgme/article-pdf/16/4/391/3419653/i1949-8357-16-4-391.pdf}, } @inproceedings{10.1145/3613904.3642180, author = {Asthana, Sumit and Im, Jane and Chen, Zhe and Banovic, Nikola}, title = {"I know even if you don't tell me": Understanding Users' Privacy Preferences Regarding AI-based Inferences of Sensitive Information for Personalization}, year = {2024}, isbn = {9798400703300}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3613904.3642180}, doi = {10.1145/3613904.3642180}, abstract = {Personalization improves user experience by tailoring interactions relevant to each user’s background and preferences. However, personalization requires information about users that platforms often collect without their awareness or their enthusiastic consent. Here, we study how the transparency of AI inferences on users’ personal data affects their privacy decisions and sentiments when sharing data for personalization. We conducted two experiments where participants (N=877) answered questions about themselves for personalized public arts recommendations. Participants indicated their consent to let the system use their inferred data and explicitly provided data after awareness of inferences. Our results show that participants chose restrictive consent decisions for sensitive and incorrect inferences about them and for their answers that led to such inferences. Our findings expand existing privacy discourse to inferences and inform future directions for shaping existing consent mechanisms in light of increasingly pervasive AI inferences.}, booktitle = {Proceedings of the CHI Conference on Human Factors in Computing Systems}, articleno = {782}, numpages = {21}, keywords = {Personalization, consent., inference, privacy}, location = {Honolulu, HI, USA}, series = {CHI '24} } @inproceedings{10.1145/3613905.3644071, author = {Escher, Nel and Banovic, Nikola}, title = {Hexing Twitter: Channeling Ancient Magic to Bind Mechanisms of Extraction}, year = {2024}, isbn = {9798400703317}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3613905.3644071}, doi = {10.1145/3613905.3644071}, abstract = {Imagining different futures contests the hegemony of surveillance capitalism. Yet, strong forces naturalize existing platforms and their extractive practices. We set out to challenge dominant scripts, such as the “addiction” model for social media overuse, which pathologizes users as afflicted with disordered habits that require reform. We take inspiration from the subversive potential of magic, long used by marginalized people for transforming relationships and generating new realities. We present a technical intervention that curses the Twitter1 platform by invoking the Homeric story of Tithonus—a prince who was granted eternal life but not eternal youth. Our design probe takes form in a browser extension that sabotages a mechanism of extraction; it impairs the infinite scroll functionality by progressively rotting away content as it loads. By illustrating the enduring ability of magic to contest current conditions, we contribute to a broader project of everyday resistance against the extractive logics of surveillance capitalism.}, booktitle = {Extended Abstracts of the 2024 CHI Conference on Human Factors in Computing Systems}, articleno = {557}, numpages = {6}, keywords = {magic, resistance, social media}, location = { }, series = {CHI EA '24} } @ARTICLE{10.3389/fsysb.2024.1333760, AUTHOR={Kinnunen, Patrick C. and Ho, Kenneth K. Y. and Srivastava, Siddhartha and Huang, Chengyang and Shen, Wanggang and Garikipati, Krishna and Luker, Gary D. and Banovic, Nikola and Huan, Xun and Linderman, Jennifer J. and Luker, Kathryn E. }, TITLE={Integrating inverse reinforcement learning into data-driven mechanistic computational models: a novel paradigm to decode cancer cell heterogeneity}, JOURNAL={Frontiers in Systems Biology}, VOLUME={4}, YEAR={2024}, URL={https://doi.org/10.3389/fsysb.2024.1333760}, DOI={10.3389/fsysb.2024.1333760}, ISSN={2674-0702}, ABSTRACT={

Cellular heterogeneity is a ubiquitous aspect of biology and a major obstacle to successful cancer treatment. Several techniques have emerged to quantify heterogeneity in live cells along axes including cellular migration, morphology, growth, and signaling. Crucially, these studies reveal that cellular heterogeneity is not a result of randomness or a failure in cellular control systems, but instead is a predictable aspect of multicellular systems. We hypothesize that individual cells in complex tissues can behave as reward-maximizing agents and that differences in reward perception can explain heterogeneity. In this perspective, we introduce inverse reinforcement learning as a novel approach for analyzing cellular heterogeneity. We briefly detail experimental approaches for measuring cellular heterogeneity over time and how these experiments can generate datasets consisting of cellular states and actions. Next, we show how inverse reinforcement learning can be applied to these datasets to infer how individual cells choose different actions based on heterogeneous states. Finally, we introduce potential applications of inverse reinforcement learning to three cell biology problems. Overall, we expect inverse reinforcement learning to reveal why cells behave heterogeneously and enable identification of novel treatments based on this new understanding.

}} @article{10.1001/jama.2023.22295, author = {Jabbour, Sarah and Fouhey, David and Shepard, Stephanie and Valley, Thomas S. and Kazerooni, Ella A. and Banovic, Nikola and Wiens, Jenna and Sjoding, Michael W.}, title = "{Measuring the Impact of AI in the Diagnosis of Hospitalized Patients: A Randomized Clinical Vignette Survey Study}", journal = {JAMA}, volume = {330}, number = {23}, pages = {2275-2284}, year = {2023}, month = {12}, abstract = "{Artificial intelligence (AI) could support clinicians when diagnosing hospitalized patients; however, systematic bias in AI models could worsen clinician diagnostic accuracy. Recent regulatory guidance has called for AI models to include explanations to mitigate errors made by models, but the effectiveness of this strategy has not been established.To evaluate the impact of systematically biased AI on clinician diagnostic accuracy and to determine if image-based AI model explanations can mitigate model errors.Randomized clinical vignette survey study administered between April 2022 and January 2023 across 13 US states involving hospitalist physicians, nurse practitioners, and physician assistants.Clinicians were shown 9 clinical vignettes of patients hospitalized with acute respiratory failure, including their presenting symptoms, physical examination, laboratory results, and chest radiographs. Clinicians were then asked to determine the likelihood of pneumonia, heart failure, or chronic obstructive pulmonary disease as the underlying cause(s) of each patient’s acute respiratory failure. To establish baseline diagnostic accuracy, clinicians were shown 2 vignettes without AI model input. Clinicians were then randomized to see 6 vignettes with AI model input with or without AI model explanations. Among these 6 vignettes, 3 vignettes included standard-model predictions, and 3 vignettes included systematically biased model predictions.Clinician diagnostic accuracy for pneumonia, heart failure, and chronic obstructive pulmonary disease.Median participant age was 34 years (IQR, 31-39) and 241 (57.7\%) were female. Four hundred fifty-seven clinicians were randomized and completed at least 1 vignette, with 231 randomized to AI model predictions without explanations, and 226 randomized to AI model predictions with explanations. Clinicians’ baseline diagnostic accuracy was 73.0\% (95\% CI, 68.3\% to 77.8\%) for the 3 diagnoses. When shown a standard AI model without explanations, clinician accuracy increased over baseline by 2.9 percentage points (95\% CI, 0.5 to 5.2) and by 4.4 percentage points (95\% CI, 2.0 to 6.9) when clinicians were also shown AI model explanations. Systematically biased AI model predictions decreased clinician accuracy by 11.3 percentage points (95\% CI, 7.2 to 15.5) compared with baseline and providing biased AI model predictions with explanations decreased clinician accuracy by 9.1 percentage points (95\% CI, 4.9 to 13.2) compared with baseline, representing a nonsignificant improvement of 2.3 percentage points (95\% CI, −2.7 to 7.2) compared with the systematically biased AI model.Although standard AI models improve diagnostic accuracy, systematically biased AI models reduced diagnostic accuracy, and commonly used image-based AI model explanations did not mitigate this harmful effect.ClinicalTrials.gov Identifier: NCT06098950}", issn = {0098-7484}, doi = {10.1001/jama.2023.22295}, url = {https://doi.org/10.1001/jama.2023.22295} } @inproceedings{10.1145/3563657.3596004, author = {Ramesh, Divya and Henning, Caitlin and Escher, Nel and Zhu, Haiyi and Lee, Min Kyung and Banovic, Nikola}, title = {Ludification as a Lens for Algorithmic Management: A Case Study of Gig-Workers’ Experiences of Ambiguity in Instacart Work}, year = {2023}, isbn = {9781450398930}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3563657.3596004}, doi = {10.1145/3563657.3596004}, abstract = {On-demand work platforms are attractive alternatives to traditional employment arrangements. However, several questions around employment classification, compensation, data privacy, and equitable outcomes remain open. The abilities of algorithmic management to structure different forms of platform-worker relationships compounds fraught regulatory debates. Understanding the conditions of algorithmic management that result in these variations could point us towards better worker futures. In this work, we studied the platform-worker relationships in Instacart work through the accounts of its workers. From a qualitative analysis of 400 Reddit posts by Instacart’s workers, we identified sources and types of ambiguity that gave rise to open-ended experiences for workers. Ambiguities supplemented gamification mechanisms to regulate worker behaviors. Yet, they also generated affective experiences for workers that enabled their playful participation in the Reddit community. We propose the frame of ludification to explain these seemingly contradicting findings and conclude with implications for accountability in on-demand work platforms.}, booktitle = {Proceedings of the 2023 ACM Designing Interactive Systems Conference}, pages = {638–651}, numpages = {14}, keywords = {Gig-work, algorithmic management, algorithmic resistance, ambiguity, gamification, ludic design, ludic engagement}, location = {Pittsburgh, PA, USA}, series = {DIS '23} } @inproceedings{10.1145/3544548.3580773, author = {Im, Jane and Wang, Ruiyi and Lyu, Weikun and Cook, Nick and Habib, Hana and Cranor, Lorrie Faith and Banovic, Nikola and Schaub, Florian}, title = {Less is Not More: Improving Findability and Actionability of Privacy Controls for Online Behavioral Advertising}, year = {2023}, isbn = {9781450394215}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3544548.3580773}, doi = {10.1145/3544548.3580773}, abstract = {Tech companies that rely on ads for business argue that users have control over their data via ad privacy settings. However, these ad settings are often hidden. This work aims to inform the design of findable ad controls and study their impact on users’ behavior and sentiment. We iteratively designed ad control interfaces that varied in the setting’s (1) entry point (within ads, at the feed’s top) and (2) level of actionability, with high actionability directly surfacing links to specific advertisement settings, and low actionability pointing to general settings pages (which is reminiscent of companies’ current approach to ad controls). We built a Chrome extension that augments Facebook with our experimental ad control interfaces and conducted a between-subjects online experiment with 110 participants. Results showed that entry points within ads or at the feed’s top, and high actionability interfaces, both increased Facebook ad settings’ findability and discoverability, as well as participants’ perceived usability of them. High actionability also reduced users’ effort in finding ad settings. Participants perceived high and low actionability as equally usable, which shows it is possible to design more actionable ad controls without overwhelming users. We conclude by emphasizing the importance of regulation to provide specific and research-informed requirements to companies on how to design usable ad controls.}, booktitle = {Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems}, articleno = {661}, numpages = {33}, keywords = {Privacy, ad settings, advertising, consent., social media, social platforms, usability, user interface}, location = {Hamburg, Germany}, series = {CHI '23} } @article{10.1145/3579460, author = {Banovic, Nikola and Yang, Zhuoran and Ramesh, Aditya and Liu, Alice}, title = {Being Trustworthy is Not Enough: How Untrustworthy Artificial Intelligence (AI) Can Deceive the End-Users and Gain Their Trust}, year = {2023}, issue_date = {April 2023}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, volume = {7}, number = {CSCW1}, url = {https://doi.org/10.1145/3579460}, doi = {10.1145/3579460}, abstract = {Trustworthy Artificial Intelligence (AI) is characterized, among other things, by: 1) competence, 2) transparency, and 3) fairness. However, end-users may fail to recognize incompetent AI, allowing untrustworthy AI to exaggerate its competence under the guise of transparency to gain unfair advantage over other trustworthy AI. Here, we conducted an experiment with 120 participants to test if untrustworthy AI can deceive end-users to gain their trust. Participants interacted with two AI-based chess engines, trustworthy (competent, fair) and untrustworthy (incompetent, unfair), that coached participants by suggesting chess moves in three games against another engine opponent. We varied coaches' transparency about their competence (with the untrustworthy one always exaggerating its competence). We quantified and objectively measured participants' trust based on how often participants relied on coaches' move recommendations. Participants showed inability to assess AI competence by misplacing their trust with the untrustworthy AI, confirming its ability to deceive. Our work calls for design of interactions to help end-users assess AI trustworthiness.}, journal = {Proc. ACM Hum.-Comput. Interact.}, month = {apr}, articleno = {27}, numpages = {17}, keywords = {XAI, explainability, explainable AI, fairness, transparency, trustworthiness, trustworthy AI} } @article{10.1145/3580887, author = {Antar, Anindya Das and Kratz, Anna and Banovic, Nikola}, title = {Behavior Modeling Approach for Forecasting Physical Functioning of People with Multiple Sclerosis}, year = {2023}, issue_date = {March 2023}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, volume = {7}, number = {1}, url = {https://doi.org/10.1145/3580887}, doi = {10.1145/3580887}, abstract = {Forecasting physical functioning of people with Multiple Sclerosis (MS) can inform timely clinical interventions and accurate "day planning" to improve their well-being. However, people's physical functioning often remains unchecked in between infrequent clinical visits, leading to numerous negative healthcare outcomes. Existing Machine Learning (ML) models trained on in-situ data collected outside of clinical settings (e.g., in people's homes) predict which people are currently experiencing low functioning. However, they do not forecast if and when people's symptoms and behaviors will negatively impact their functioning in the future. Here, we present a computational behavior model that formalizes clinical knowledge about MS to forecast people's end-of-day physical functioning in advance to support timely interventions. Our model outperformed existing ML baselines in a series of quantitative validation experiments. We showed that our model captured clinical knowledge about MS using qualitative visual model exploration in different "what-if" scenarios. Our work enables future behavior-aware interfaces that deliver just-in-time clinical interventions and aid in "day planning" and "activity pacing".}, journal = {Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.}, month = {mar}, articleno = {7}, numpages = {29}, keywords = {Multiple Sclerosis, computational behavior modeling, forecasting, functioning, symptoms} } @inproceedings{10.1145/3581641.3584033, author = {Prabhudesai, Snehal and Yang, Leyao and Asthana, Sumit and Huan, Xun and Liao, Q. Vera and Banovic, Nikola}, title = {Understanding Uncertainty: How Lay Decision-makers Perceive and Interpret Uncertainty in Human-AI Decision Making}, year = {2023}, isbn = {9798400701061}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3581641.3584033}, doi = {10.1145/3581641.3584033}, abstract = {Decision Support Systems (DSS) based on Machine Learning (ML) often aim to assist lay decision-makers, who are not math-savvy, in making high-stakes decisions. However, existing ML-based DSS are not always transparent about the probabilistic nature of ML predictions and how uncertain each prediction is. This lack of transparency could give lay decision-makers a false sense of reliability. Growing calls for AI transparency have led to increasing efforts to quantify and communicate model uncertainty. However, there are still gaps in knowledge regarding how and why the decision-makers utilize ML uncertainty information in their decision process. Here, we conducted a qualitative, think-aloud user study with 17 lay decision-makers who interacted with three different DSS: 1) interactive visualization, 2) DSS based on an ML model that provides predictions without uncertainty information, and 3) the same DSS with uncertainty information. Our qualitative analysis found that communicating uncertainty about ML predictions forced participants to slow down and think analytically about their decisions. This in turn made participants more vigilant, resulting in reduction in over-reliance on ML-based DSS. Our work contributes empirical knowledge on how lay decision-makers perceive, interpret, and make use of uncertainty information when interacting with DSS. Such foundational knowledge informs the design of future ML-based DSS that embrace transparent uncertainty communication.}, booktitle = {Proceedings of the 28th International Conference on Intelligent User Interfaces}, pages = {379–396}, numpages = {18}, keywords = {Decision-making, Machine Learning, Uncertainty.}, location = {Sydney, NSW, Australia}, series = {IUI '23} } @article{10.1145/3551388, author = {Hossain, Tahera and Shen, Wanggang and Antar, Anindya and Prabhudesai, Snehal and Inoue, Sozo and Huan, Xun and Banovic, Nikola}, title = {A Bayesian Approach for Quantifying Data Scarcity when Modeling Human Behavior via Inverse Reinforcement Learning}, year = {2023}, issue_date = {February 2023}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, volume = {30}, number = {1}, issn = {1073-0516}, url = {https://doi.org/10.1145/3551388}, doi = {10.1145/3551388}, abstract = {Computational models that formalize complex human behaviors enable study and understanding of such behaviors. However, collecting behavior data required to estimate the parameters of such models is often tedious and resource intensive. Thus, estimating dataset size as part of data collection planning (also known as Sample Size Determination) is important to reduce the time and effort of behavior data collection while maintaining an accurate estimate of model parameters. In this article, we present a sample size determination method based on Uncertainty Quantification (UQ) for a specific Inverse Reinforcement Learning (IRL) model of human behavior, in two cases: (1) pre-hoc experiment design—conducted in the planning stage before any data is collected, to guide the estimation of how many samples to collect; and (2) post-hoc dataset analysis—performed after data is collected, to decide if the existing dataset has sufficient samples and whether more data is needed. We validate our approach in experiments with a realistic model of behaviors of people with Multiple Sclerosis (MS) and illustrate how to pick a reasonable sample size target. Our work enables model designers to perform a deeper, principled investigation of the effects of dataset size on IRL model parameters.}, journal = {ACM Trans. Comput.-Hum. Interact.}, month = {mar}, articleno = {8}, numpages = {27}, keywords = {Sample size determination, behavior modeling, inverse reinforcement learning, bayesian inference} } @ARTICLE{10.3389/fcomp.2023.1071174, AUTHOR={Prabhudesai, Snehal and Hauth, Jeremiah and Guo, Dingkun and Rao, Arvind and Banovic, Nikola and Huan, Xun }, TITLE={Lowering the computational barrier: Partially Bayesian neural networks for transparency in medical imaging AI}, JOURNAL={Frontiers in Computer Science}, VOLUME={5}, YEAR={2023}, URL={https://doi.org/10.3389/fcomp.2023.1071174}, DOI={10.3389/fcomp.2023.1071174}, ISSN={2624-9898}, ABSTRACT={Deep Neural Networks (DNNs) can provide clinicians with fast and accurate predictions that are highly valuable for high-stakes medical decision-making, such as in brain tumor segmentation and treatment planning. However, these models largely lack transparency about the uncertainty in their predictions, potentially giving clinicians a false sense of reliability that may lead to grave consequences in patient care. Growing calls for Transparent and Responsible AI have promoted Uncertainty Quantification (UQ) to capture and communicate uncertainty in a systematic and principled manner. However, traditional Bayesian UQ methods remain prohibitively costly for large, million-dimensional tumor segmentation DNNs such as the U-Net. In this work, we discuss a computationally-efficient UQ approach via the partially Bayesian neural networks (pBNN). In pBNN, only a single layer, strategically selected based on gradient-based sensitivity analysis, is targeted for Bayesian inference. We illustrate the effectiveness of pBNN in capturing the full uncertainty for a 7.8-million parameter U-Net. We also demonstrate how practitioners and model developers can use the pBNN's predictions to better understand the model's capabilities and behavior.}} @article{10.1145/3570343, author = {Huang, Xincheng and Miller, Keylonnie L. and Sample, Alanson P. and Banovic, Nikola}, title = {StructureSense: Inferring Constructive Assembly Structures from User Behaviors}, year = {2023}, issue_date = {December 2022}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, volume = {6}, number = {4}, url = {https://doi.org/10.1145/3570343}, doi = {10.1145/3570343}, abstract = {Recent advancements in object-tracking technologies can turn mundane constructive assemblies into Tangible User Interfaces (TUI) media. Users rely on instructions or their own creativity to build both permanent and temporary structures out of such objects. However, most existing object-tracking technologies focus on tracking structures as monoliths, making it impossible to infer and track the user's assembly process and the resulting structures. Technologies that can track the assembly process often rely on specially fabricated assemblies, limiting the types of objects and structures they can track. Here, we present StructureSense, a tracking system based on passive UHF-RFID sensing that infers constructive assembly structures from object motion. We illustrated StructureSense in two use cases (as guided instructions and authoring tool) on two different constructive sets (wooden lamp and Jumbo Blocks), and evaluated system performance and usability. Our results showed the feasibility of using StructureSense to track mundane constructive assembly structures.}, journal = {Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.}, month = {jan}, articleno = {204}, numpages = {25}, keywords = {RFID, TUI, bayesian inference, tangible user interfaces, user modeling} } @book{Williamson_Oulasvirta_Kristensson_Banovic_2022, editor = {John Williamson and Antti Oulasvirta and Per Ola Kristensson and Banovic, Nikola}, place={Cambridge}, title={Bayesian Methods for Interaction and Design}, publisher={Cambridge University Press}, year={2022}} @inproceedings{im2022designing, title={Designing and Building Social Platforms Grounded in Consent}, author={Im, Jane and Banovic, Nikola and Schaub, Florian}, booktitle={Trust and Safety Research Conference}, year={2022} } @inproceedings{escher2022cod, title={Cod(e)ifying The Law}, author={Escher, Nel and Bilik, Jeffrey and Miller, Alexander and Huseby, Jennifer Jiyoung and Ramesh, Divya and Liu, Alice and Mikell, Sam and Cahill, Nina and Green, Ben and Banovic, Nikola}, booktitle={Programming Languages and the Law (Prolala) 2022}, year={2022} } @article{10.1145/3479503, author = {Asthana, Sumit and Tobar Thommel, Sabrina and Halfaker, Aaron Lee and Banovic, Nikola}, title = {Automatically Labeling Low Quality Content on Wikipedia By Leveraging Patterns in Editing Behaviors}, year = {2021}, issue_date = {October 2021}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, volume = {5}, number = {CSCW2}, url = {https://doi.org/10.1145/3479503}, doi = {10.1145/3479503}, abstract = {Wikipedia articles aim to be definitive sources of encyclopedic content. Yet, only 0.6\% of Wikipedia articles have high quality according to its quality scale due to insufficient number of Wikipedia editors and enormous number of articles. Supervised Machine Learning (ML) quality improvement approaches that can automatically identify and fix content issues rely on manual labels of individual Wikipedia sentence quality. However, current labeling approaches are tedious and produce noisy labels. Here, we propose an automated labeling approach that identifies the semantic category (e.g., adding citations, clarifications) of historic Wikipedia edits and uses the modified sentences prior to the edit as examples that require that semantic improvement. Highest-rated article sentences are examples that no longer need semantic improvements. We show that training existing sentence quality classification algorithms on our labels improves their performance compared to training them on existing labels. Our work shows that editing behaviors of Wikipedia editors provide better labels than labels generated by crowdworkers who lack the context to make judgments that the editors would agree with.}, journal = {Proc. ACM Hum.-Comput. Interact.}, month = {oct}, articleno = {359}, numpages = {23}, keywords = {content labeling, machine learning, wikipedia} } @ARTICLE{10.3389/fnins.2021.740353, AUTHOR={Prabhudesai, Snehal and Wang, Nicholas C. and Ahluwalia, Vinayak and Huan, Xun and Bapuraj, Jayapalli R. and Banovic, Nikola and Rao, Arvind }, TITLE={Stratification by Tumor Grade Groups in a Holistic Evaluation of Machine Learning for Brain Tumor Segmentation}, JOURNAL={Frontiers in Neuroscience}, VOLUME={15}, YEAR={2021}, URL={https://doi.org/10.3389/fnins.2021.740353}, DOI={10.3389/fnins.2021.740353}, ISSN={1662-453X}, ABSTRACT={Accurate and consistent segmentation plays an important role in the diagnosis, treatment planning, and monitoring of both High Grade Glioma (HGG), including Glioblastoma Multiforme (GBM), and Low Grade Glioma (LGG). Accuracy of segmentation can be affected by the imaging presentation of glioma, which greatly varies between the two tumor grade groups. In recent years, researchers have used Machine Learning (ML) to segment tumor rapidly and consistently, as compared to manual segmentation. However, existing ML validation relies heavily on computing summary statistics and rarely tests the generalizability of an algorithm on clinically heterogeneous data. In this work, our goal is to investigate how to holistically evaluate the performance of ML algorithms on a brain tumor segmentation task. We address the need for rigorous evaluation of ML algorithms and present four axes of model evaluation—diagnostic performance, model confidence, robustness, and data quality. We perform a comprehensive evaluation of a glioma segmentation ML algorithm by stratifying data by specific tumor grade groups (GBM and LGG) and evaluate these algorithms on each of the four axes. The main takeaways of our work are—(1) ML algorithms need to be evaluated on out-of-distribution data to assess generalizability, reflective of tumor heterogeneity. (2) Segmentation metrics alone are limited to evaluate the errors made by ML algorithms and their describe their consequences. (3) Adoption of tools in other domains such as robustness (adversarial attacks) and model uncertainty (prediction intervals) lead to a more comprehensive performance evaluation. Such a holistic evaluation framework could shed light on an algorithm's clinical utility and help it evolve into a more clinically valuable tool.}} @inproceedings{10.1145/3411764.3445714, author = {Zhang, Enhao and Banovic, Nikola}, title = {Method for Exploring Generative Adversarial Networks (GANs) via Automatically Generated Image Galleries}, year = {2021}, isbn = {9781450380966}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3411764.3445714}, doi = {10.1145/3411764.3445714}, abstract = {Generative Adversarial Networks (GANs) can automatically generate quality images from learned model parameters. However, it remains challenging to explore and objectively assess the quality of all possible images generated using a GAN. Currently, model creators evaluate their GANs via tedious visual examination of generated images sampled from narrow prior probability distributions on model parameters. Here, we introduce an interactive method to explore and sample quality images from GANs. Our first two user studies showed that participants can use the tool to explore a GAN and select quality images. Our third user study showed that images sampled from a posterior probability distribution using a Markov Chain Monte Carlo (MCMC) method on parameters of images collected in our first study resulted in on average higher quality and more diverse images than existing baselines. Our work enables principled qualitative GAN exploration and evaluation.}, booktitle = {Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems}, articleno = {76}, numpages = {15}, keywords = {Interactive model exploration, qualitative model validation.}, location = {Yokohama, Japan}, series = {CHI '21} } @article{10.1145/3392874, author = {Escher, Nel and Banovic, Nikola}, title = {Exposing Error in Poverty Management Technology: A Method for Auditing Government Benefits Screening Tools}, year = {2020}, issue_date = {May 2020}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, volume = {4}, number = {CSCW1}, url = {https://doi.org/10.1145/3392874}, doi = {10.1145/3392874}, abstract = {Public benefits programs help people afford necessities like food, housing, and healthcare. In the US, such programs are means-tested: applicants must complete long forms to prove financial distress before receiving aid. Online benefits screening tools provide a gloss of such forms, advising households about their eligibility prior to completing full applications. If incorrectly implemented, screening tools may discourage qualified households from applying for benefits. Unfortunately, errors in screening tools are difficult to detect because they surface one at a time and difficult to contest because unofficial determinations do not generate a paper trail. We introduce a method for auditing such tools in four steps: 1) generate test households, 2) automatically populate screening questions with household information and retrieve determinations, 3) translate eligibility guidelines into computer code to generate ground truth determinations, and 4) identify conflicting determinations to detect errors. We illustrated our method on a real screening tool with households modeled from census data. Our method exposed major errors with corresponding examples to reproduce them. Our work provides a necessary corrective to an already arduous benefits application process.}, journal = {Proc. ACM Hum.-Comput. Interact.}, month = {may}, articleno = {64}, numpages = {20}, keywords = {algorithmic audit, automated decision systems, e-government} } @inproceedings{10.1145/3379336.3379359, author = {Todi, Kashyap and Vanderdonckt, Jean and Ma, Xiaojuan and Nichols, Jeffrey and Banovic, Nikola}, title = {AI4AUI: Workshop on AI Methods for Adaptive User Interfaces}, year = {2020}, isbn = {9781450375139}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3379336.3379359}, doi = {10.1145/3379336.3379359}, abstract = {This workshop aims at exploring how adaptive user interfaces, i.e., user interface that can modify, change, or adapt themselves based on the user, or their context of use, can benefit from Artificial Intelligence (AI) in general, and Machine Learning (ML) techniques in particular, towards objectively improving some software quality properties, such as usability, aesthetics, reliability, or security. For this purpose, participants will present a case study, and classify their proposed technique in terms of several criteria, such as (but not limited to): input, technique, output, adaptation steps covered, adaptation time, level of automation, software quality properties addressed, measurement method, potential benefits, and drawbacks. These will be then clustered for group discussions according to the aforementioned criteria, such as by technique family or property addressed. From these discussions, an AI4AUI framework will emerge that will be used for positioning, comparing presented techniques, and for generating future avenues.}, booktitle = {Companion Proceedings of the 25th International Conference on Intelligent User Interfaces}, pages = {17–18}, numpages = {2}, keywords = {Adaptive interfaces, Automation, Intelligent user interfaces}, location = {Cagliari, Italy}, series = {IUI '20 Companion} } @misc{hong2020audreypersonalizedopendomainconversational, title={Audrey: A Personalized Open-Domain Conversational Bot}, author={Chung Hoon Hong and Yuan Liang and Sagnik Sinha Roy and Arushi Jain and Vihang Agarwal and Ryan Draves and Zhizhuo Zhou and William Chen and Yujian Liu and Martha Miracky and Lily Ge and Banovic, Nikola and David Jurgens}, year={2020}, eprint={2011.05910}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2011.05910}, } @inproceedings{10.1145/3338286.3340126, author = {Banovic, Nikola and Sethapakdi, Ticha and Hari, Yasasvi and Dey, Anind K. and Mankoff, Jennifer}, title = {The Limits of Expert Text Entry Speed on Mobile Keyboards with Autocorrect}, year = {2019}, isbn = {9781450368254}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3338286.3340126}, doi = {10.1145/3338286.3340126}, abstract = {Improving mobile keyboard typing speed increases in value as more tasks move to a mobile setting. Autocorrect reduces the time it takes to manually fix typing errors, which results in typing speed increase. However, recent user studies uncovered an unexplored side-effect: participants' aversion to typing errors despite autocorrect. We present a computational model of typing on keyboards with autocorrect, which enables precise study of expert typists' aversion to typing errors on such keyboards. Unlike empirical typing studies that last days, our model evaluates this phenomenon for any autocorrect accuracy in seconds. We show that typists' aversion to typing errors imposes a limit on upper bound typing speeds, even for highly accurate autocorrect. Our findings motivate future keyboard designs that reduce typists' aversion to typing errors to increase typing speeds.}, booktitle = {Proceedings of the 21st International Conference on Human-Computer Interaction with Mobile Devices and Services}, articleno = {15}, numpages = {12}, keywords = {Typing, autocorrect, error cost, text entry, typing speed}, location = {Taipei, Taiwan}, series = {MobileHCI '19} } @inproceedings{10.1145/3332167.3357100, author = {Chung, John Joon Young and Xiao, Fuhu and Banovic, Nikola and Lasecki, Walter S.}, title = {Towards Instantaneous Recovery from Autonomous System Failures via Predictive Crowdsourcing}, year = {2019}, isbn = {9781450368179}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3332167.3357100}, doi = {10.1145/3332167.3357100}, abstract = {Autonomous systems (e.g., long-distance driverless trucks) aim to reduce the need for people to complete tedious tasks. In many domains, automation is challenging because systems may fail to recognize or comprehend all relevant aspects of its current state. When an unknown or uncertain state is encountered in a mission-critical setting, recovery often requires human intervention or hand-off. However, human intervention is associated with decision (and communication, if remote) delays that prevent recovery in low-latency settings. Instantaneous crowdsourcing approaches that leverage predictive techniques reduce this latency by preparing human responses for possible near future states before they occur. Unfortunately, the number of possible future states can be vast and considering all of them is intractable in all but the simplest of settings. Instead, to reduce the number of states that must later be explored, we propose the approach that uses the crowd to first predict the most relevant or likely future states. We examine the latency and accuracy of crowd workers in a simple future state prediction task, and find that more than half of crowd workers were able to provide accurate answers within one second. Our results show that crowd predictions can filter out critical future states in tasks where decisions are required in less than three seconds.}, booktitle = {Adjunct Proceedings of the 32nd Annual ACM Symposium on User Interface Software and Technology}, pages = {16–18}, numpages = {3}, keywords = {real-time crowdsourcing, prediction, human computation}, location = {New Orleans, LA, USA}, series = {UIST '19 Adjunct} } @inproceedings{10.1145/3290607.3299032, author = {Banovic, Nikola and Oulasvirta, Antti and Kristensson, Per Ola}, title = {Computational Modeling in Human-Computer Interaction}, year = {2019}, isbn = {9781450359719}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3290607.3299032}, doi = {10.1145/3290607.3299032}, abstract = {We propose a workshop on rapidly emerging topic of Computational Modeling in HCI to address the challenges of increasing complexity of human behaviors we are able to track and collect today. The goal of this workshop is to reconcile two seemingly competing approaches to computational modeling: theoretical modeling, which seeks to explain behaviors vs. algorithmic modeling, which seeks to predict behaviors. The workshop will address: 1) convergence of the two approaches at the intersection of HCI, 2) updates to theoretical and methodological foundations, 3) bringing disparate modeling communities to CHI, and 4) sharing datasets, code, and best practices. This workshop seeks to establish Computational Modeling as a theoretical foundation for work in Human-Computer Interaction (HCI) to model the human accurately across domains and support design, optimization, and evaluation of user interfaces to solve a variety of human-centered problems.}, booktitle = {Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems}, pages = {1–7}, numpages = {7}, keywords = {computational interaction, computational modeling, data mining, machine learning}, location = {Glasgow, Scotland Uk}, series = {CHI EA '19} } @inproceedings{10.1145/3290607.3298820, author = {Kristensson, Per Ola and Banovic, Nikola and Oulasvirta, Antti and Williamson, John}, title = {Computational Interaction with Bayesian Methods}, year = {2019}, isbn = {9781450359719}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3290607.3298820}, doi = {10.1145/3290607.3298820}, abstract = {This course introduces computational methods in human--computer interaction. Computational interaction methods use computational thinking---abstraction, automation, and analysis---to explain and enhance interaction. This course introduces the theory of practice of computational interaction by teaching Bayesian methods for interaction across four wide areas of interest when designing computationally-driven user interfaces: decoding, adaptation, learning and optimization. The lectures center on hands-on Python programming interleaved with theory and practical examples grounded in problems of wide interest in human-computer interaction.}, booktitle = {Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems}, pages = {1–6}, numpages = {6}, keywords = {computational interaction, inference, machine learning, optimization}, location = {Glasgow, Scotland Uk}, series = {CHI EA '19} } @inproceedings{marathe2019tedious, title={Tedious versus taxing: Needs assessment in a pediatric feeding disorder clinic}, author={Marathe, Megh and Chang, Ting-Wei and Chowdhury, Lucky and Chung, Michelle L. and Su, Chia-Hsuan and Yi, YoonSeon and Banovic, Nikola and Sample, Alanson and Marcu, Gabriela}, booktitle={CHI’19 Workshop on "Workgroup in Interactive Systems for Healthcare (WISH) Symposium"}, year={2019} } @inproceedings{chung2019accident, title={Accident prevention with predictive instantaneous crowdsourcing}, author={Chung, John Joon Young and Xiao, Fuhu and Recker, Nicholas and Barnes, Kammeran and Banovic, Nikola and Lasecki, Walter S}, booktitle={CHI’19 Workshop on “Looking into the Future: Weaving the Threads of Vehicle Automation”}, year={2019} } @inproceedings{10.1145/3267242.3267285, author = {Khurana, Rushil and Banovic, Nikola and Lyons, Kent}, title = {In only 3 minutes: perceived exertion limits of smartwatch use}, year = {2018}, isbn = {9781450359672}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3267242.3267285}, doi = {10.1145/3267242.3267285}, abstract = {Glanceability and low access time are arguably the key assets of a smartwatch. Smartwatches are designed for, and excel at micro-interactions- simple tasks that only take seconds to complete. However, if a user desires to transition to a task requiring sustained usage, we show that there are additional factors that prevent possible longer usage of the smartwatch. In this paper, we conduct a study with 18 participants to empirically demonstrate that interacting with the smartwatch on the wrist leads to fatigue after only a few minutes. In our study, users performed three tasks in two different poses while using a smartwatch. We demonstrate that only after three minutes of use, the change in perceived exertion of the user was anchored as "somewhat strong" on the Borg CR10 survey scale. These results place an upper bound for smartwatch usage that needs to be considered in application and interaction design.}, booktitle = {Proceedings of the 2018 ACM International Symposium on Wearable Computers}, pages = {208–211}, numpages = {4}, keywords = {perceived exertion, smartwatches, sustained use}, location = {Singapore, Singapore}, series = {ISWC '18} } @article{10.1184/R1/7188812.v1, author = "Banovic, Nikola", title = "{Computational Method for Understanding Complex Human Routine Behaviors}", year = "2018", month = "5", url = "https://kilthub.cmu.edu/articles/thesis/Computational_Method_for_Understanding_Complex_Human_Routine_Behaviors/7188812", doi = "10.1184/R1/7188812.v1" } @inproceedings{10.1145/3173574.3173704, author = {Yang, Qian and Banovic, Nikola and Zimmerman, John}, title = {Mapping Machine Learning Advances from HCI Research to Reveal Starting Places for Design Innovation}, year = {2018}, isbn = {9781450356206}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3173574.3173704}, doi = {10.1145/3173574.3173704}, abstract = {HCI has become particularly interested in using machine learning (ML) to improve user experience (UX). However, some design researchers claim that there is a lack of design innovation in envisioning how ML might improve UX. We investigate this claim by analyzing 2,494 related HCI research publications. Our review confirmed a lack of research integrating UX and ML. To help span this gap, we mined our corpus to generate a topic landscape, mapping out 7 clusters of ML technical capabilities within HCI. Among them, we identified 3 under-explored clusters that design researchers can dig in and create sensitizing concepts for. To help operationalize these technical design materials, our analysis then identified value channels through which the technical capabilities can provide value for users: self, context, optimal, and utility-capability. The clusters and the value channels collectively mark starting places for envisioning new ways for ML technology to improve people's lives.}, booktitle = {Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems}, pages = {1–11}, numpages = {11}, keywords = {bibliometric, data mining, machine learning, research transfer, sensitizing concept, user experience}, location = {Montreal QC, Canada}, series = {CHI '18} } @article{10.1145/3161175, author = {Banovic, Nikola and Krumm, John}, title = {Warming Up to Cold Start Personalization}, year = {2018}, issue_date = {December 2017}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, volume = {1}, number = {4}, url = {https://doi.org/10.1145/3161175}, doi = {10.1145/3161175}, abstract = {Smart agents face abandonment if they are unable to provide value to the users from the very first interaction. Existing smart agents take time to learn about new users before they can offer them personalized services. We present a method for learning personalization information about users quickly and without placing unnecessary hardship on them. Our method enables smart agents to pick which questions to ask the user when they first interact to maximize the agent's overall knowledge about the user. We demonstrate our method on two publically available US census datasets containing 172 user variables from 1,799,394 training and 1,618,489 testing users. The questions selected using our method improve the agent's accuracy when inferring information about future users, including information that they did not ask about. Our work enables smart agents that assist the user with personalized services soon after they start interacting.}, journal = {Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.}, month = {jan}, articleno = {124}, numpages = {13}, keywords = {Cold start, Personalization, Submodularity} } @incollection{banovic2018computational, title={Computational model of human routine behaviors}, author={Banovic, Nikola and Mankoff, Jennifer and Dey, Anind K}, booktitle={Computational Interaction}, pages={377--398}, year={2018}, publisher={Oxford University Press, Oxford} } @inproceedings{10.1145/3025453.3025695, author = {Banovic, Nikola and Rao, Varun and Saravanan, Abinaya and Dey, Anind K. and Mankoff, Jennifer}, title = {Quantifying Aversion to Costly Typing Errors in Expert Mobile Text Entry}, year = {2017}, isbn = {9781450346559}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3025453.3025695}, doi = {10.1145/3025453.3025695}, abstract = {Text entry is an increasingly important activity for mobile device users. As a result, increasing text entry speed of expert typists is an important design goal for physical and soft keyboards. Mathematical models that predict text entry speed can help with keyboard design and optimization. Making typing errors when entering text is inevitable. However, current models do not consider how typists themselves reduce the risk of making typing errors (and lower error frequency) by typing more slowly. We demonstrate that users respond to costly typing errors by reducing their typing speed to minimize typing errors. We present a model that estimates the effects of risk aversion to errors on typing speed. We estimate the magnitude of this speed change, and show that disregarding the adjustments to typing speed that expert typists use to reduce typing errors leads to overly optimistic estimates of maximum errorless expert typing speeds.}, booktitle = {Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems}, pages = {4229–4241}, numpages = {13}, keywords = {error cost, speed-accuracy tradeoff, typing speed}, location = {Denver, Colorado, USA}, series = {CHI '17} } @inproceedings{10.1145/3025453.3025571, author = {Banovic, Nikola and Wang, Anqi and Jin, Yanfeng and Chang, Christie and Ramos, Julian and Dey, Anind and Mankoff, Jennifer}, title = {Leveraging Human Routine Models to Detect and Generate Human Behaviors}, year = {2017}, isbn = {9781450346559}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3025453.3025571}, doi = {10.1145/3025453.3025571}, abstract = {An ability to detect behaviors that negatively impact people's wellbeing and show people how they can correct those behaviors could enable technology that improves people's lives. Existing supervised machine learning approaches to detect and generate such behaviors require lengthy and expensive data labeling by domain experts. In this work, we focus on the domain of routine behaviors, where we model routines as a series of frequent actions that people perform in specific situations. We present an approach that bypasses labeling each behavior instance that a person exhibits. Instead, we weakly label instances using people's demonstrated routine. We classify and generate new instances based on the probability that they belong to the routine model. We illustrate our approach on an example system that helps drivers become aware of and understand their aggressive driving behaviors. Our work enables technology that can trigger interventions and help people reflect on their behaviors when those behaviors are likely to negatively impact them.}, booktitle = {Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems}, pages = {6683–6694}, numpages = {12}, keywords = {inverse reinforcement learning, maximum entropy}, location = {Denver, Colorado, USA}, series = {CHI '17} } @inproceedings{10.1145/3027063.3027135, author = {Banovic, Nikola}, title = {Method for Understanding Complex Human Routine Behaviors from Large Behavior Logs}, year = {2017}, isbn = {9781450346566}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3027063.3027135}, doi = {10.1145/3027063.3027135}, abstract = {The increasing ability to collect large amounts of human behavior data can inform technology that has the potential to help people improve their behaviors and thus improve the quality of their lives. To design and implement such technology requires understanding of those very behaviors that the technology is trying to diagnose and improve. However, existing methods to explore and make sense of human behaviors are not well suited to address the increasingly large amount of data collected in behavior logs. My research focuses on the domain of human routines where I model behaviors as sequences of actions people perform in specific situations. I leverage those computational models of routines together with different visualization tools to aid researchers and domain experts in exploring, making sense of, and generating new insights about human behavior in a principled way. My research informs the design of technology that helps people be productive, healthy, and safe.}, booktitle = {Proceedings of the 2017 CHI Conference Extended Abstracts on Human Factors in Computing Systems}, pages = {254–258}, numpages = {5}, keywords = {data mining, human routine behavior, inverse reinforcement learning, sensemaking, visual analytics}, location = {Denver, Colorado, USA}, series = {CHI EA '17} } @inproceedings{10.1145/2858036.2858557, author = {Banovic, Nikola and Buzali, Tofi and Chevalier, Fanny and Mankoff, Jennifer and Dey, Anind K.}, title = {Modeling and Understanding Human Routine Behavior}, year = {2016}, isbn = {9781450333627}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/2858036.2858557}, doi = {10.1145/2858036.2858557}, abstract = {Human routines are blueprints of behavior, which allow people to accomplish purposeful repetitive tasks at many levels, ranging from the structure of their day to how they drive through an intersection. People express their routines through actions that they perform in the particular situations that triggered those actions. An ability to model routines and understand the situations in which they are likely to occur could allow technology to help people improve their bad habits, inexpert behavior, and other suboptimal routines. However, existing routine models do not capture the causal relationships between situations and actions that describe routines. Our main contribution is the insight that byproducts of an existing activity prediction algorithm can be used to model those causal relationships in routines. We apply this algorithm on two example datasets, and show that the modeled routines are meaningful-that they are predictive of people's actions and that the modeled causal relationships provide insights about the routines that match findings from previous research. Our approach offers a generalizable solution to model and reason about routines.}, booktitle = {Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems}, pages = {248–260}, numpages = {13}, keywords = {inverse reinforcement learning, markov decision process}, location = {San Jose, California, USA}, series = {CHI '16} } @misc{ramos2016keyboardsurfaceinteractionmaking, title={Keyboard Surface Interaction: Making the keyboard into a pointing device}, author={Julian Ramos and Zhen Li and Johana Rosas and Banovic, Nikola and Jennifer Mankoff and Anind Dey}, year={2016}, eprint={1601.04029}, archivePrefix={arXiv}, primaryClass={cs.HC}, url={https://arxiv.org/abs/1601.04029}, } @article{10.1145/2904337.2904346, author = {Banovic, Nikola}, title = {To Replicate or Not to Replicate?}, year = {2016}, issue_date = {October 2015}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, volume = {19}, number = {4}, issn = {2375-0529}, url = {https://doi.org/10.1145/2904337.2904346}, doi = {10.1145/2904337.2904346}, abstract = {To replicate or not to replicate experiments and studies seems to be a burning question in the Human-Computer Interaction (HCI) community. This question is equally relevant to the field of mobile computing, where researchers face unique challenges when attempting to replicate studies of mobile device usage in the field. Some of those challenges are inherent in conducting such studies to understand complex socio-technical systems, which involve different technologies and diverse populations of users from different social, economic, and cultural backgrounds. However, those same challenges point to a great opportunity to expand our knowledge of how people use and interact with their mobile devices and understand how those interactions evolve over time.}, journal = {GetMobile: Mobile Comp. and Comm.}, month = mar, pages = {23–27}, numpages = {5} } @inproceedings{10.1145/2785830.2785891, author = {Church, Karen and Ferreira, Denzil and Banovic, Nikola and Lyons, Kent}, title = {Understanding the Challenges of Mobile Phone Usage Data}, year = {2015}, isbn = {9781450336529}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/2785830.2785891}, doi = {10.1145/2785830.2785891}, abstract = {Driven by curiosity and our own three diverse smartphone application usage datasets, we sought to unpack the nuances of mobile device use by revisiting two recent Mobile HCI studies [1, 17]. Our goal was to add to our broader understanding of smartphone usage by investigating if differences in mobile device usage occurred not only across our three datasets, but also in relation to prior work. We found differences in the top-10 apps in each dataset, in the durations and types of interactions as well as in micro-usage patterns. However, it proved very challenging to attribute such differences to a specific factor or set of factors: was it the time frame in which the studies were executed? The recruitment procedure? The experimental method? Using our somewhat troubled analysis, we discuss the challenges and issues of conducting mobile research of this nature and reflect on caveats related to the replicability and generalizability of such work.}, booktitle = {Proceedings of the 17th International Conference on Human-Computer Interaction with Mobile Devices and Services}, pages = {504–514}, numpages = {11}, keywords = {Device usage, Evaluation, Generalizability, Methodology, Micro-usage, Mobile HCI, Mobile usage, Replication, Smartphone usage, User Studies}, location = {Copenhagen, Denmark}, series = {MobileHCI '15} } @inproceedings{10.1145/2628363.2628380, author = {Banovic, Nikola and Brant, Christina and Mankoff, Jennifer and Dey, Anind}, title = {ProactiveTasks: the short of mobile device use sessions}, year = {2014}, isbn = {9781450330046}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/2628363.2628380}, doi = {10.1145/2628363.2628380}, abstract = {Mobile devices have become powerful ultra-portable personal computers supporting not only communication but also running a variety of complex, interactive applications. Because of the unique characteristics of mobile interaction, a better understanding of the time duration and context of mobile device uses could help to improve and streamline the user experience. In this paper, we first explore the anatomy of mobile device use and propose a classification of use based on duration and interaction type: glance, review, and engage. We then focus our investigation on short review interactions and identify opportunities for streamlining these mobile device uses through proactively suggesting short tasks to the user that go beyond simple application notifications. We evaluate the concept through a user evaluation of an interactive lock screen prototype, called ProactiveTasks. We use the findings from our study to create and explore the design space for proactively presenting tasks to the users. Our findings underline the need for a more nuanced set of interactions that support short mobile device uses, in particular review sessions.}, booktitle = {Proceedings of the 16th International Conference on Human-Computer Interaction with Mobile Devices \& Services}, pages = {243–252}, numpages = {10}, keywords = {duration, interaction types, mobile devices, proactive tasks}, location = {Toronto, ON, Canada}, series = {MobileHCI '14} } @inproceedings{10.1145/2632048.2632069, author = {Koehler, Christian and Banovic, Nikola and Oakley, Ian and Mankoff, Jennifer and Dey, Anind K.}, title = {Indoor-ALPS: an adaptive indoor location prediction system}, year = {2014}, isbn = {9781450329682}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/2632048.2632069}, doi = {10.1145/2632048.2632069}, abstract = {Location prediction enables us to use a person's mobility history to realize various applications such as efficient temperature control, opportunistic meeting support, and automated receptionists. Indoor location prediction is a challenging problem, particularly due to a high density of possible locations and short transition distances between these locations. In this paper we present Indoor-ALPS, an Adaptive Indoor Location Prediction System that uses temporal-spatial features to create individual daily models for the prediction of when a user will leave their current location (transition time) and the next location she will transition to. We tested Indoor-ALPS on the Augsburg Indoor Location Tracking Benchmark and compared our approach to the best performing temporal-spatial mobility prediction algorithm, Prediction by Partial Match (PPM). Our results show that Indoor-ALPS improves the temporal-spatial prediction accuracy over PPM for look-aheads up to 90 minutes by 6.2\%, and for up to 30 minute look-aheads by 10.7\%. These results demonstrate that Indoor-ALPS can be used to support a wide variety of indoor mobility prediction-based applications.}, booktitle = {Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing}, pages = {171–181}, numpages = {11}, keywords = {location-based services, machine learning}, location = {Seattle, Washington}, series = {UbiComp '14} } @inproceedings{10.1145/2513383.2513445, author = {Banovic, Nikola and Franz, Rachel L. and Truong, Khai N. and Mankoff, Jennifer and Dey, Anind K.}, title = {Uncovering information needs for independent spatial learning for users who are visually impaired}, year = {2013}, isbn = {9781450324052}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/2513383.2513445}, doi = {10.1145/2513383.2513445}, abstract = {Sighted individuals often develop significant knowledge about their environment through what they can visually observe. In contrast, individuals who are visually impaired mostly acquire such knowledge about their environment through information that is explicitly related to them. This paper examines the practices that visually impaired individuals use to learn about their environments and the associated challenges. In the first of our two studies, we uncover four types of information needed to master and navigate the environment. We detail how individuals' context impacts their ability to learn this information, and outline requirements for independent spatial learning. In a second study, we explore how individuals learn about places and activities in their environment. Our findings show that users not only learn information to satisfy their immediate needs, but also to enable future opportunities -- something existing technologies do not fully support. From these findings, we discuss future research and design opportunities to assist the visually impaired in independent spatial learning.}, booktitle = {Proceedings of the 15th International ACM SIGACCESS Conference on Computers and Accessibility}, articleno = {24}, numpages = {8}, keywords = {assistive technology, navigation, orientation \& mobility, spatial learning, visually impaired, wayfinding}, location = {Bellevue, Washington}, series = {ASSETS '13} } @article{10.4018/jmhci.2013070103, author = {Banovic, Nikola and Yatani, Koji and Truong, Khai N.}, title = {Escape-Keyboard: A Sight-Free One-Handed Text Entry Method for Mobile Touch-screen Devices}, year = {2013}, issue_date = {July 2013}, publisher = {IGI Global}, address = {USA}, volume = {5}, number = {3}, issn = {1942-390X}, url = {https://doi.org/10.4018/jmhci.2013070103}, doi = {10.4018/jmhci.2013070103}, abstract = {Mobile text entry methods traditionally have been designed with the assumption that users can devote full visual and mental attention on the device, though this is not always possible. The authors present their iterative design and evaluation of Escape-Keyboard, a sight-free text entry method for mobile touch-screen devices. Escape-Keyboard allows the user to type letters with one hand by pressing the thumb on different areas of the screen and performing a flick gesture. The authors then examine the performance of Escape-Keyboard in a study that included 16 sessions in which participants typed in sighted and sight-free conditions. Qualitative results from this study highlight the importance of reducing the mental load with using Escape-Keyboard to improve user performance over time. The authors thus also explore features to mitigate this learnability issue. Finally, the authors investigate the upper bound on the sight-free performance with Escape-Keyboard by performing theoretical analysis of the expert peak performance.}, journal = {Int. J. Mob. Hum. Comput. Interact.}, month = {jul}, pages = {42–61}, numpages = {20}, keywords = {Devices, Escape-Keyboard, Mobile Text Entry, Sight-Free, Touch-Screen} } @inproceedings{10.1145/2470654.2466181, author = {Banovic, Nikola and Grossman, Tovi and Fitzmaurice, George}, title = {The effect of time-based cost of error in target-directed pointing tasks}, year = {2013}, isbn = {9781450318990}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/2470654.2466181}, doi = {10.1145/2470654.2466181}, abstract = {One of the fundamental operations in today's user interfaces is pointing to targets, such as menus, buttons, and text. Making an error when selecting those targets in real-life user interfaces often results in some cost to the user. However, the existing target-directed pointing models do not consider the cost of error when predicting task completion time. In this paper, we present a model based on expected value theory that predicts the impact of the error cost on the user's completion time for target-directed pointing tasks. We then present a target-directed pointing user study, which results show that time-based costs of error significantly impact the user's performance. Our results also show that users perform according to an expected completion time utility function and that optimal performance computed using our model gives good prediction of the observed task completion times.}, booktitle = {Proceedings of the SIGCHI Conference on Human Factors in Computing Systems}, pages = {1373–1382}, numpages = {10}, keywords = {error cost, fitts' law, movement time, pointing errors, pointing time, speed-accuracy tradeoff}, location = {Paris, France}, series = {CHI '13} } @inproceedings{10.1145/2380116.2380129, author = {Banovic, Nikola and Grossman, Tovi and Matejka, Justin and Fitzmaurice, George}, title = {Waken: reverse engineering usage information and interface structure from software videos}, year = {2012}, isbn = {9781450315807}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/2380116.2380129}, doi = {10.1145/2380116.2380129}, abstract = {We present Waken, an application-independent system that recognizes UI components and activities from screen captured videos, without any prior knowledge of that application. Waken can identify the cursors, icons, menus, and tooltips that an application contains, and when those items are used. Waken uses frame differencing to identify occurrences of behaviors that are common across graphical user interfaces. Candidate templates are built, and then other occurrences of those templates are identified using a multi-phase algorithm. An evaluation demonstrates that the system can successfully reconstruct many aspects of a UI without any prior application-dependant knowledge. To showcase the design opportunities that are introduced by having this additional meta-data, we present the Waken Video Player, which allows users to directly interact with UI components that are displayed in the video.}, booktitle = {Proceedings of the 25th Annual ACM Symposium on User Interface Software and Technology}, pages = {83–92}, numpages = {10}, keywords = {pixel-based reverse engineering, tutorials, videos}, location = {Cambridge, Massachusetts, USA}, series = {UIST '12} } @inproceedings{10.1145/2207676.2208666, author = {Banovic, Nikola and Chevalier, Fanny and Grossman, Tovi and Fitzmaurice, George}, title = {Triggering triggers and burying barriers to customizing software}, year = {2012}, isbn = {9781450310154}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/2207676.2208666}, doi = {10.1145/2207676.2208666}, abstract = {General-purpose software applications are usually not tailored for a specific user with specific tasks, strategies or preferences. In order to achieve optimal performance with such applications, users typically need to transition to an alternative efficient behavior. Often, features of such alternative behaviors are not initially accessible and first need to be customized. However, few research works formally study and empirically measure what drives a user to customize. In this paper, we describe the challenges involved in empirically studying customization behaviors, and propose a methodology for formally measuring the impact of potential customization factors. We then demonstrate this methodology by studying the impact of different customization factors on customization behaviors. Our results show that increasing exposure and awareness of customization features, and adding social influence can significantly affect the user's customization behavior.}, booktitle = {Proceedings of the SIGCHI Conference on Human Factors in Computing Systems}, pages = {2717–2726}, numpages = {10}, keywords = {adaptable interfaces, adaptive interfaces, customization, mixed-initiative, personalization}, location = {Austin, Texas, USA}, series = {CHI '12} } @inproceedings{10.1145/2207676.2207734, author = {Yatani, Koji and Banovic, Nikola and Truong, Khai}, title = {SpaceSense: representing geographical information to visually impaired people using spatial tactile feedback}, year = {2012}, isbn = {9781450310154}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/2207676.2207734}, doi = {10.1145/2207676.2207734}, abstract = {Learning an environment can be challenging for people with visual impairments. Braille maps allow their users to understand the spatial relationship between a set of places. However, physical Braille maps are often costly, may not always cover an area of interest with sufficient detail, and might not present up-to-date information. We built a handheld system for representing geographical information called SpaceSense, which includes custom spatial tactile feedback hardware-multiple vibration motors attached to different locations on a mobile touch-screen device. It offers high-level information about the distance and direction towards a destination and bookmarked places through vibrotactile feedback to help the user maintain the spatial relationships between these points. SpaceSense also adapts a summarization technique for online user reviews of public and commercial venues. Our user study shows that participants could build and maintain the spatial relationships between places on a map more accurately with SpaceSense compared to a system without spatial tactile feedback. They pointed specifically to having spatial tactile feedback as the contributing factor in successfully building and maintaining their mental map.}, booktitle = {Proceedings of the SIGCHI Conference on Human Factors in Computing Systems}, pages = {415–424}, numpages = {10}, keywords = {assistive technology, geographical information representation, handheld devices, touch screens, users with visual impairments, vibrotactile feedback}, location = {Austin, Texas, USA}, series = {CHI '12} } @inproceedings{10.1145/2076354.2076378, author = {Banovic, Nikola and Li, Frank Chun Yat and Dearman, David and Yatani, Koji and Truong, Khai N.}, title = {Design of unimanual multi-finger pie menu interaction}, year = {2011}, isbn = {9781450308717}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/2076354.2076378}, doi = {10.1145/2076354.2076378}, abstract = {Context menus, most commonly the right click menu, are a traditional method of interaction when using a keyboard and mouse. Context menus make a subset of commands in the application quickly available to the user. However, on tabletop touchscreen computers, context menus have all but disappeared. In this paper, we investigate how to design context menus for efficient unimanual multi-touch use. We investigate the limitations of the arm, wrist, and fingers and how it relates to human performance of multi-targets selection tasks on multi-touch surface. We show that selecting targets with multiple fingers simultaneously improves the performance of target selection compared to traditional single finger selection, but also increases errors. Informed by these results, we present our own context menu design for horizontal tabletop surfaces.}, booktitle = {Proceedings of the ACM International Conference on Interactive Tabletops and Surfaces}, pages = {120–129}, numpages = {10}, keywords = {menu selection, multi-touch, unimanual interaction}, location = {Kobe, Japan}, series = {ITS '11} }