Interactive machine learning

Untitled work

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Induction of decision trees

Quinlan, J. R. (1986) · Machine Learning · doi:10.1007/bf00116251 · Cited by 21,439 · see citing works

Pattern Classification

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Nearest neighbor pattern classification

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Robust real-time object detection.

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Induction of Decision Trees

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On the Handling of Continuous-Valued Attributes in Decision Tree Generation

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Implicit human computer interaction through context

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The basic ideas in neural networks

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Gaussian mixture model for human skin color and its applications in image and video databases

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The gesture pendant: a self-illuminating, wearable, infrared computer vision system for home automation control and medical monitoring

Starner, T. E., Auxier, J., Ashbrook, D., & Gandy, M. (2002) · International Symposium on Wearable Computers · doi:10.1109/iswc.2000.888469 · Cited by 267 · see citing works

Laser pointer interaction

Olsen, D. R., & Nielsen, T. (2001) · ACM SIGCHI Conference on Human Factors in Computing Systems (CHI) · doi:10.1145/365024.365030 · Cited by 248 · see citing works

VIDEOPLACE—an artificial reality

Krueger, M. W., Gionfriddo, T., & Hinrichsen, K. (1985) · ACM SIGCHI Bulletin · doi:10.1145/1165385.317463 · Cited by 242 · see citing works

VIDEOPLACE---an artificial reality

Krueger, M. W., Gionfriddo, T., & Hinrichsen, K. (1985) · ACM SIGCHI Conference on Human Factors in Computing Systems (CHI) · doi:10.1145/317456.317463 · Cited by 238 · see citing works

Emancipated pixels

Underkoffler, J. S., Ullmer, B., & Ishii, H. (1999) · ACM SIGGRAPH Annual Conference on Computer Graphics and Interactive Techniques (SIGGRAPH) · doi:10.1145/311535.311593 · Cited by 169 · see citing works

Illuminating light

Underkoffler, J. S., & Ishii, H. (1998) · ACM SIGCHI Conference on Human Factors in Computing Systems (CHI) · doi:10.1145/274644.274717 · Cited by 166 · see citing works

Conceptual framework for indexing visual information at multiple levels

Jaimes, A., & Chang, S. (1999) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.373443 · Cited by 121 · see citing works

Model-based sonar localisation for mobile robots

Triggs, B. (1994) · Robotics and Autonomous Systems · doi:10.1016/0921-8890(94)90024-8 · Cited by 39 · see citing works

Light widgets

Fails, J. A., & Olsen, D. R. (2002) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/502716.502729 · Cited by 37 · see citing works

A hierarchical system for efficient image retrieval

Vailaya, A., Zhong, Y., & Jain, A. K. (1996) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.1996.546970 · Cited by 32 · see citing works

INTELLIGENT ROBOTIC SYSTEMS

Meshcheriakov, R. V. (2024) · Изготовление МЭМС-структур чувствительных элементов датчиков концентрации газа с применением органических щелочей · doi:10.25728/datsys.2024.2.2 · Cited by 25 · see citing works

Automatic selection of visual features and classifiers

Jaimes, A., & Chang, S. (1999) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.373566 · Cited by 17 · see citing works

Integrating Multiple Classifiers In Visual Object Detectors Learned From User Input

Jaimes, A., & Chang, S. (2000) · Cited by 9 · see citing works

Artificial Intelligence and Management: The Automation–Augmentation Paradox

Raisch, S., & Krakowski, S. (2021) · Academy of Management Review · doi:10.5465/amr.2018.0072 · Cited by 2,755 · see citing works

Generative Agents: Interactive Simulacra of Human Behavior

Park, J. S., O'Brien, J., Cai, C. J., Morris, M. R., Liang, P., & Bernstein, M. S. (2023) · Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology · doi:10.1145/3586183.3606763 · Cited by 2,187 · see citing works

Power to the People: The Role of Humans in Interactive Machine Learning

Amershi, S., Cakmak, M., Knox, W. B., & Kulesza, T. (2014) · AI Magazine · doi:10.1609/aimag.v35i4.2513 · Cited by 1,084 · see citing works

Principles of Explanatory Debugging to Personalize Interactive Machine Learning

Kulesza, T., Burnett, M., Wong, W.-K., & Stumpf, S. (2015) · Proceedings of the 20th International Conference on Intelligent User Interfaces · doi:10.1145/2678025.2701399 · Cited by 576 · see citing works

Interpreting Interpretability: Understanding Data Scientists' Use of Interpretability Tools for Machine Learning

Kaur, H., Nori, H., Jenkins, S., Caruana, R., Wallach, H., & Wortman Vaughan, J. (2020) · Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376219 · Cited by 564 · see citing works

Human-Centered Tools for Coping with Imperfect Algorithms During Medical Decision-Making

Cai, C. J., Reif, E., Hegde, N., Hipp, J., Kim, B., Smilkov, D., Wattenberg, M., Viegas, F., Corrado, G. S., Stumpe, M. C., et al. (2019) · Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300234 · Cited by 405 · see citing works

The Principles and Limits of Algorithm-in-the-Loop Decision Making

Green, B., & Chen, Y. (2019) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3359152 · Cited by 392 · see citing works

A Review of User Interface Design for Interactive Machine Learning

Dudley, J. J., & Kristensson, P. O. (2018) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/3185517 · Cited by 343 · see citing works

Creative Writing with a Machine in the Loop: Case Studies on Slogans and Stories

Clark, E., Ross, A. S., Tan, C., Ji, Y., & Smith, N. A. (2018) · 23rd International Conference on Intelligent User Interfaces · doi:10.1145/3172944.3172983 · Cited by 257 · see citing works

What is Human-Centered about Human-Centered AI? A Map of the Research Landscape

Capel, T., & Brereton, M. (2023) · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3580959 · Cited by 228 · see citing works

Human model evaluation in interactive supervised learning

Fiebrink, R., Cook, P. R., & Trueman, D. (2011) · Proceedings of the SIGCHI Conference on Human Factors in Computing Systems · doi:10.1145/1978942.1978965 · Cited by 207 · see citing works

“Brilliant AI Doctor” in Rural Clinics: Challenges in AI-Powered Clinical Decision Support System Deployment

Wang, D., Wang, L., Zhang, Z., Wang, D., Zhu, H., Gao, Y., Fan, X., & Tian, F. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445432 · Cited by 209 · see citing works

Interacting meaningfully with machine learning systems: Three experiments

Stumpf, S., Rajaram, V., Li, L., Wong, W.-K., Burnett, M., Dietterich, T., Sullivan, E., & Herlocker, J. (2009) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2009.03.004 · Cited by 184 · see citing works

From explainable to interactive AI: A literature review on current trends in human-AI interaction

Raees, M., Meijerink, I., Lykourentzou, I., Khan, V.-J., & Papangelis, K. (2024) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2024.103301 · Cited by 180 · see citing works

Grounding Interactive Machine Learning Tool Design in How Non-Experts Actually Build Models

Yang, Q., Suh, J., Chen, N.-C., & Ramos, G. (2018) · Proceedings of the 2018 Designing Interactive Systems Conference · doi:10.1145/3196709.3196729 · Cited by 156 · see citing works

Human-AI Collaboration via Conditional Delegation: A Case Study of Content Moderation

Lai, V., Carton, S., Bhatnagar, R., Liao, Q. V., Zhang, Y., & Tan, C. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3501999 · Cited by 145 · see citing works

Discovering Characteristic Actions from On-Body Sensor Data

Minnen, D., Starner, T., Essa, I., & Isbell, C. (2006) · 2006 10th IEEE International Symposium on Wearable Computers · doi:10.1109/iswc.2006.286337 · Cited by 132 · see citing works

BreastScreening-AI: Evaluating medical intelligent agents for human-AI interactions

Calisto, F. M., Santiago, C., Nunes, N., & Nascimento, J. C. (2022) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2022.102285 · Cited by 125 · see citing works

Understanding and Visualizing Data Iteration in Machine Learning

Hohman, F., Wongsuphasawat, K., Kery, M. B., & Patel, K. (2020) · Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376177 · Cited by 110 · see citing works

Gestalt: integrated support for implementation and analysis in machine learning

Patel, K., Bancroft, N., Drucker, S. M., Fogarty, J., Ko, A. J., & Landay, J. (2010) · Proceedings of the 23nd annual ACM symposium on User interface software and technology · doi:10.1145/1866029.1866038 · Cited by 112 · see citing works

Explainable Active Learning (XAL): Toward AI Explanations as Interfaces for Machine Teachers

Ghai, B., Liao, Q. V., Zhang, Y., Bellamy, R., & Mueller, K. (2021) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3432934 · Cited by 106 · see citing works

Whither AutoML? Understanding the Role of Automation in Machine Learning Workflows

Xin, D., Wu, E. Y., Lee, D. J.-L., Salehi, N., & Parameswaran, A. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445306 · Cited by 110 · see citing works

The Disagreement Deconvolution: Bringing Machine Learning Performance Metrics In Line With Reality

Gordon, M. L., Zhou, K., Patel, K., Hashimoto, T., & Bernstein, M. S. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445423 · Cited by 101 · see citing works

Too Late to be Creative? AI-Empowered Tools in Creative Processes

Hwang, A. H.-C. (2022) · CHI Conference on Human Factors in Computing Systems Extended Abstracts · doi:10.1145/3491101.3503549 · Cited by 103 · see citing works

A multifaceted vision of the human-AI collaboration: a comprehensive review

Puerta-Beldarrain, M., Gómez-Carmona, O., Sánchez-Corcuera, R., Casado-Mansilla, D., López-de-Ipiña, D., & Chen, L. (2025) · IEEE Access · doi:10.1109/access.2025.3536095 · Cited by 73 · see citing works

Explaining sentiment analysis results on social media texts through visualization

Jain, R., Kumar, A., Nayyar, A., Dewan, K., Garg, R., Raman, S., & Ganguly, S. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-14432-y · Cited by 68 · see citing works

The Next Generation of Medical Decision Support: A Roadmap Toward Transparent Expert Companions

Bruckert, S., Finzel, B., & Schmid, U. (2020) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2020.507973 · Cited by 66 · see citing works

Co-Design and Evaluation of an Intelligent Decision Support System for Stroke Rehabilitation Assessment

Lee, M. H., Siewiorek, D. P., Smailagic, A., Bernardino, A., & Bermúdez i Badia, S. (2020) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3415227 · Cited by 57 · see citing works

Using Interactive Machine Learning to Support Interface Development Through Workshops with Disabled People

Katan, S., Grierson, M., & Fiebrink, R. (2015) · Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems · doi:10.1145/2702123.2702474 · Cited by 50 · see citing works

Local Decision Pitfalls in Interactive Machine Learning: An Investigation into Feature Selection in Sentiment Analysis

Wu, T., Weld, D. S., & Heer, J. (2019) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3319616 · Cited by 39 · see citing works

SmartEye: Assisting Instant Photo Taking via Integrating User Preference with Deep View Proposal Network

Ma, S., Wei, Z., Tian, F., Fan, X., Zhang, J., Shen, X., Lin, Z., Huang, J., Měch, R., Samaras, D., et al. (2019) · Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300701 · Cited by 24 · see citing works

Why Tutored Problem Solving May be Better Than Example Study: Theoretical Implications from a Simulated-Student Study

Matsuda, N., Cohen, W. W., Sewall, J., Lacerda, G., & Koedinger, K. R. (2008) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-69132-7_16 · Cited by 17 · see citing works

General intelligence requires rethinking exploration

Jiang, M., Rocktäschel, T., & Grefenstette, E. (2022) · arXiv · doi:10.48550/arxiv.2211.07819 · Cited by 17

Computing Education Research Today

Fincher, S. A., Tenenberg, J., Dorn, B., Hundhausen, C., McCartney, R., & Murphy, L. (2019) · The Cambridge Handbook of Computing Education Research · doi:10.1017/9781108654555.003 · Cited by 7 · see citing works

Criticmate: Stagewise Human–AI Co-Critique in Single-Screen UI Evaluation

Ko, J., Choi, J., Morales, C., Kim, D., & Ko, M. (2026) · Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790929 · Cited by 1 · see citing works

Physician-Friendly Machine Learning: A Case Study with Cardiovascular Disease Risk Prediction

Padmanabhan, M., Yuan, P., Chada, G., & Nguyen, H. V. (2019) · Journal of Clinical Medicine · doi:10.3390/jcm8071050 · Cited by 75 · see citing works

Breakdown of Machine Learning Algorithms

Meena, M., & Sehrawat, R. (2022) · Recent Trends in Artificial Intelligence & it s Applications · doi:10.46610/rtaia.2022.v01i03.005 · Cited by 1 · see citing works

Application of Machine Learning in Animal Disease Analysis and Prediction

Zhang, S., Su, Q., & Chen, Q. (2020) · Current Bioinformatics · doi:10.2174/1574893615999200728195613 · Cited by 29 · see citing works

Sentiment Analysis by Using Supervised Machine Learning and Deep Learning Approaches

Naeem, S., Logofătu, D., & Muharemi, F. (2020) · Communications in computer and information science · doi:10.1007/978-3-030-63119-2_39 · Cited by 3 · see citing works

Artificial Intelligence, Machine Learning, and Medicine: A Little Background Goes a Long Way Toward Understanding

Cote, M. P., Lubowitz, J. H., Brand, J. C., & Rossi, M. J. (2021) · Arthroscopy The Journal of Arthroscopic and Related Surgery · doi:10.1016/j.arthro.2021.04.022 · Cited by 31 · see citing works