The DOI matches its Crossref record; 53 of the 55 references it cites were found in a registry or an index.
Power to the People: The Role of Humans in Interactive Machine Learning
Open accessAbstract
Systems that can learn interactively from their end‐users are quickly becoming widespread. Until recently, this progress has been fueled mostly by advances in machine learning; however, more and more researchers are realizing the importance of studying users of these systems. In this article we promote this approach and demonstrate how it can result in better user experiences and more effective learning systems. We present a number of case studies that demonstrate how interactivity results in a tight coupling between the system and the user, exemplify ways in which some existing systems fail to account for the user, and explore new ways for learning systems to interact with their users. After giving a glimpse of the progress that has been made thus far, we discuss some of the challenges we face in moving the field forward.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“The resulting tight coupling between humans and machines, with the two influencing one another, makes it increasingly difficult, or even impossible, to decouple their influence on the resulting model (Amershi et al., 2014).”
From Artificial Intelligence and Management: The Automation–Augmentation Paradox (2021) · cites this paper as Amershi et al., 2014
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Owing to their inherent complexity and uncertainty, augmentation efforts often fail (Amershi et al., 2014).”
From Artificial Intelligence and Management: The Automation–Augmentation Paradox (2021) · cites this paper as Amershi et al., 2014
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Contrary to automation, augmentation demands continued human involvement and experimentation (Amershi et al., 2014).”
From Artificial Intelligence and Management: The Automation–Augmentation Paradox (2021) · cites this paper as Amershi et al., 2014
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Through augmentation, humans and machines become so closely intertwined that they collectively exhibit entirely new, emergent behaviors, which neither show individually (Amershi et al., 2014).”
From Artificial Intelligence and Management: The Automation–Augmentation Paradox (2021) · cites this paper as Amershi et al., 2014
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Augmentation is therefore a coevolutionary process during which humans learn from machines and machines learn from humans (Amershi, Cakmak, Knox, & Kulesza, 2014;Rahwan et al., 2019).”
From Artificial Intelligence and Management: The Automation–Augmentation Paradox (2021) · cites this paper as Amershi, Cakmak, Knox, & Kulesza, 2014
In the citing paper
“-Data Design: Design issues on data collection, pre-processing, cleaning, labeling and augmentation, including big data challenges [17,23,25-27, 31,33,41,44,74-76,105,114-116].”
From Software engineering challenges for machine learning applications: A literature review (2020) · cites this paper as [25]
In the citing paper
“-Visualization: Technical challenges on visualization techniques for the design of ML applications [25,56,63,122,123].”
From Software engineering challenges for machine learning applications: A literature review (2020) · cites this paper as [25]
In the citing paper
“-Tools: Needs of designing tools for ML applications, such as tools for non-expert ML designers, visualization tools for understanding the relationships between data and the behavior of algorithms, tools for ensuring interoperability with other tools, and domain-specific language (DSL) support [16,25,42,66].”
From Software engineering challenges for machine learning applications: A literature review (2020) · cites this paper as [25]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
A decision-theoretic generalization of on-line learning and an application to boosting
Freund, Y., & Schapire, R. (1997) · European Conference on Computational Learning Theory · doi:10.1007/3-540-59119-2_166 · Cited by 23,988
A decision-theoretic generalization of on-line learning and an application to boosting
Freund, Y., & Schapire, R. E. (1997) · Journal of Computer and System Sciences · doi:10.1006/jcss.1997.1504 · Cited by 20,466
Reinforcement Learning: A Survey
Kaelbling, L. P., Littman, M. L., & Moore, A. W. (1996) · Journal of Artificial Intelligence Research · doi:10.1613/jair.301 · Cited by 8,972
Norman, D. A. (2016) · Vahlen eBooks · doi:10.15358/9783800648108 · Cited by 6,415
Designing the user interface: Strategies for effective human-computer interaction
(1993) · Applied Ergonomics · doi:10.1016/0003-6870(93)90471-k · Cited by 5,592
(2009) · Minds at UW (University of Wisconsin) · Cited by 4,791
A Survey of Collaborative Filtering Techniques
Su, X., & Khoshgoftaar, T. M. (2009) · Advances in Artificial Intelligence · doi:10.1155/2009/421425 · Cited by 3,641
Proceedings of the 29th International Conference on Machine Learning (ICML-12)Preprint
Langford, J. C., & Pineau, J. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1207.4676 · Cited by 1,687
Principles of mixed-initiative user interfaces
Horvitz, E. (1999) · Proceedings of the SIGCHI conference on Human factors in computing systems the CHI is the limit - CHI '99 · doi:10.1145/302979.303030 · Cited by 1,490
(2003) · CRC Press eBooks · doi:10.1201/9780367804787 · Cited by 1,227
Flagged first, then by citations
2 of the 55 references couldn’t be matched to a known paper.
Research works printed but not linked: 2
- Amershi S., 2009, Proceedings of the ACM Symposium on User Interface Software and Technology, 2009 (UIST 2009), 47
- Kamar E., 2012, Proceedings of the International Conference on Autonomous Agents and Multi‐Agent Systems (AAMAS 2012)
Cited by
Sorted by citations
Items 701–800 of 1,004
Human Factors Engineering-as-a-Service in Cybersecurity
Nobles, C., Rangarajan, A., & Burrell, D. N. (2025) · IGI Global eBooks · doi:10.4018/979-8-3693-6437-6.ch014 · Cited by 2
Alkan, B. B., Kumartas, M., Kuzucuk, S., & Alkan, N. (2025) · BMC Psychology · doi:10.1186/s40359-025-02768-z · Cited by 3
Beyond the Enforcer: Exploring Metaphors for Alternative AI Moderation in Online Games
Galwey, R., Sparrow, L. A., & Butt, M.-A. R. (2025) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3706599.3720148 · Cited by 2
Toward trustworthy AI with integrative explainable AI frameworks
Finzel, B. (2025) · it - Information Technology · doi:10.1515/itit-2025-0007 · Cited by 4
Introduction: Designing Adaptable and Adaptive Robots
Andriella, A., Carme Torras, A., & Alenyà, G. (2025) · Springer tracts in advanced robotics · doi:10.1007/978-3-031-71732-1_1 · Cited by 2
A systematic review of shared personal informatics
Xue, M., An, P., Guo, Z., Liang, R.-H., Hu, J., Hansen, P., & Feijs, L. M. G. (2025) · Information and Software Technology · doi:10.1016/j.infsof.2025.107759 · Cited by 2
eyeNotate: Interactive Annotation of Mobile Eye Tracking Data Based on Few-Shot Image Classification
Barz, M., Bhatti, O. S., Alam, H., Nguyen, D. M. H., Altmeyer, K., Malone, S., & Sonntag, D. (2025) · Journal of Eye Movement Research · doi:10.3390/jemr18040027 · Cited by 2
Implementation of an AI‐Based Expert System for Functional Safety of Machinery
Iyenghar, P. (2025) · Risk Analysis · doi:10.1111/risa.70151 · Cited by 2
Human at the Center: A Framework for Human‐Driven AI Development
Hu, D., Acosta-Navas, D., Gaube, S., Mozannar, H., Taylor, M. E., Dvijotham, K., & Bondi, E. (2025) · AI Magazine · doi:10.1002/aaai.70043 · Cited by 2
Leveraging Large Language Models and Human-In-The-Loop for Interactive Learning Pipelines
Du, H., Kannan, D., Tarun, B., & Gehringer, E. F. (2025) · IEEE Frontiers in Education Conference (FIE) · doi:10.1109/fie63693.2025.11328405 · Cited by 2
Fading Memories: The Role of Machine Learning in Organizational Knowledge Depreciation
Gerlach, J. P., & Lange, D. A. (2026) · Academy of Management Review · doi:10.5465/amr.2024.0408 · Cited by 3
Guarding truth in an AI world: ethics for the next generation of scholarship
Mubarak, F. (2026) · Journal of Information Communication and Ethics in Society · doi:10.1108/jices-09-2025-0241 · Cited by 2
Ge, Z.-G., & Li, Q. (2026) · Interactive Learning Environments · doi:10.1080/10494820.2026.2667464 · Cited by 3
The Explanatory Gap in Algorithmic News Curation
Heuer, H. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87031-7_1 · Cited by 2
Teachable Conversational Agents for Crowdwork: Effects on Performance and Trust
Chhibber, N., Goh, J., & Law, E. (2022) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3555223 · Cited by 6
Interactive Machine Learning on Edge Devices With User-in-the-Loop Sample Recommendation
Liu, T., & Sugano, Y. (2022) · IEEE Access · doi:10.1109/access.2022.3212077 · Cited by 3
Osmanoglu, B. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-22131-6_29 · Cited by 3
Learning Custom Experience Ontologies via Embedding-based Feedback Loops
Zaidi, A. M., Turbeville, K., Ivančić, K., Moss, J., Villalobos, J. G., Sagar, A., Li, H., Mehra, C., Li, S., Hutchins, S., et al. (2023) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/3586183.3606715 · Cited by 2
Artificial Intelligence in Project Management: Insights from Croatia
Vegar, B., & Mijač, T. (2024) · International Convention on Information and Communication Technology, Electronics and Microelectronics · doi:10.1109/mipro60963.2024.10569357 · Cited by 2
Towards Interactive Guidance for Writing Training Utterances for Conversational Agents
Piorkowski, D., Ostrand, R., Brimijoin, K., He, J., Albert, E., & Houde, S. (2024) · ACM Conference on Conversational User Interfaces (CUI) · doi:10.1145/3640794.3665553 · Cited by 2
Aspects of Decision-Making in Human–Machine Teaming
Balthasar, M. (2024) · Springer proceedings in complexity · doi:10.1007/978-3-031-57785-7_43 · Cited by 2
Wang, Y., Zhao, J. L., Hong, J., Askin, R. G., & Maciejewski, R. (2024) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2024.3468352 · Cited by 2
A Survey on Anomaly Detection in IoT and Cloud Computing Security
Gagan, P., Varghese, J. E., & Aarella, S. G. (2024) · International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) · doi:10.1109/i-smac61858.2024.10714750 · Cited by 3
Towards Co-designing a Human-Agent Interface: A Case Study in Online Grooming Detection.
Daish, P., Micallef, N., Lorenzo-Dus, N., Paiement, A., & Sahoo, D. R. (2024) · International Conference on Human-Agent Interaction (HAI) · doi:10.1145/3687272.3690893 · Cited by 2
“What are they not telling me?” Learning machine learning: Understanding the challenges for novices
Cinca, R., Costanza, E., Musolesi, M., & Alebri, M. (2024) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2024.103438 · Cited by 4
Governing Technology: A MacIntyrean Approach to the Ethics of Artificial Intelligence
Ruiz, P. G. (2024) · Ethical economy · doi:10.1007/978-3-031-78888-8_9 · Cited by 2
A User-Driven Hybrid Neuro-Symbolic Approach for Knowledge Graph Creation from Relational Data
Stütz, J.-D., Karras, O., Oelen, A., & Auer, S. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-81375-7_10 · Cited by 2
Shi, Z., Chung, D., Du, Y., Zhang, J., Raina, S., & Matarić, M. J. (2025) · ACM International Conference on Interaction Design and Children (IDC) · doi:10.1145/3713043.3728841 · Cited by 5
Human-Centered Machine Learning
Schillizzi, C. (2025) · Springer eBooks · doi:10.1007/978-981-95-3658-0_25 · Cited by 2
Adaptive Machine Learning Frameworks Integrating LLMs and Knowledge Graphs for Tailored Education
Wasnik, H. H., & Shrivastava, T. (2025) · International Conference on Circuit, Power and Computing Technologies · doi:10.1109/iccpct65132.2025.11176710 · Cited by 2
Knoll: Creating a Knowledge Ecosystem for Large Language Models
Zhao, D., Yang, D., & Bernstein, M. S. (2025) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/3746059.3747711 · Cited by 7
Efficient Construction of Interpretable Oblique Decision Trees
Estivill‐Castro, V., & Nabuuso, N. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-15638-9_7 · Cited by 2
Crowd-Powered Systems to Diminish the Effects of Semantic Drift
Pedro, S. D. D. S., & Junior, E. R. H. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-29859-3_59 · Cited by 2
Silva, L. S., Nakamura, R., & Nunes, F. L. S. (2020) · International Conference on Systems, Signals and Image Processing · doi:10.1109/iwssip48289.2020.9145098 · Cited by 2
Cruz, C. A., & Igarashi, T. (2020) · Annual Symposium on Computer-Human Interaction in Play · doi:10.1145/3383668.3419938 · Cited by 2
Nunzio, G. M. D. (2016) · ACM/IEEE Joint Conference on Digital Libraries · doi:10.1145/2910896.2925437 · Cited by 2
Billewicz, A. (2018) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3170427.3180300 · Cited by 2
Kickmeier-Rust, M., & Holzinger, A. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-11548-7_33 · Cited by 2
A Case for Guided Machine Learning
Westphal, F., Lavesson, N., & Grahn, H. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-29726-8_22 · Cited by 2
Embodied working memory during ongoing input streams
Berberian, N., Ross, M., & Chartier, S. (2021) · PLoS ONE · doi:10.1371/journal.pone.0244822 · Cited by 2
Applying Usability Heuristics in the Context of Data Labeling Systems
Viana, L., Passos, L., de Oliveira, E. C. C., & Conte, T. U. (2021) · Brazilian Symposium on Human Factors in Computing Systems (IHC) · doi:10.1145/3472301.3484374 · Cited by 2
Computer-supported Interactive Assignment of Keywords for Literature Collections
Agarwal, S., Bernard, J., & Beck, F. (2018) · doi:10.1109/mlui52768.2018.10075564 · Cited by 2
Ethics Education of Human Factors Engineers for Responsible AI Development
Rantanen, E. M., Lee, J. D., Darveau, K., Miller, D., Intriligator, J. M., & Sawyer, B. D. (2021) · Proceedings of the Human Factors and Ergonomics Society Annual Meeting · doi:10.1177/1071181321651038 · Cited by 3
Proposing an Interactive Audit Pipeline for Visual Privacy Research
DeHart, J., Xu, C., Grant, C., & Egede, L. (2021) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata52589.2021.9671478 · Cited by 3
LabelUX! Guidelines to support software engineers to design data labeling systems
Passos, L., Viana, L., de Oliveira, E. C. C., & Conte, T. U. (2021) · Brazilian Symposium on Software Quality (SBQS) · doi:10.1145/3493244.3493252 · Cited by 2
Demelo, J., & Sedig, K. (2021) · Information · doi:10.3390/info13010008 · Cited by 2
Dynamic Data Mining for Multidimensional Data Based On Machine Learning Algorithms
Jena, S. K., Sahu, P., & Mishra, S. (2021) · International Conference on Information Systems and Computer Networks (ISCON) · doi:10.1109/iscon52037.2021.9702355 · Cited by 2
Israel, M. J., Graves, M., & Amer, A. (2021) · EAI Endorsed Transactions on Creative Technologies · doi:10.4108/eai.1-12-2021.172359 · Cited by 2
Interactive Visualization of Deep Learning for 3D Brain Data Analysis
Li, H., Fang, S., Goñi, J., Saykin, A. J., & Shen, L. (2021) · IEEE International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) · doi:10.1109/iccicc53683.2021.9811312 · Cited by 2
Shaping Haru’s Affective Behavior with Valence and Arousal Based Implicit Facial Feedback
Wang, H., Chen, G., Gómez, R., Nakamura, K., He, B., & Li, G. (2022) · IEEE RO-MAN · doi:10.1109/ro-man53752.2022.9900540 · Cited by 2
Designing for Human Behaviour in a Systemic World
Maier, A. M., & Cash, P. J. (2023) · Springer eBooks · doi:10.1007/978-3-030-46054-9_16-2 · Cited by 3
Persson, J., Bugeja, J., Davidsson, P., Holmberg, J., Kebande, V. R., Mihailescu, R., Sarkheyli-Hägele, A., & Tegen, A. (2023) · Applied Sciences · doi:10.3390/app13116516 · Cited by 2
Managing Collaborative Development of Artificial Intelligence: Lessons from the Field
Mayer, A., van den Broek, E., Kim, B., Karačić, T., Hidalgo, M. S., & Huysman, M. (2023) · Hawaii International Conference on System Sciences (HICSS) · doi:10.24251/hicss.2023.744 · Cited by 2
Position: We Can Measure XAI Explanations Better with Templates.
Dodge, J. E., & Burnett, M. (2020) · Cited by 2
Dornig, J., & Li, C. (2020) · IEEE International Conference on Multimedia Information Processing and Retrieval (MIPR) · doi:10.1109/mipr49039.2020.00087 · Cited by 2
Gaggle: Visual Analytics for Model Space Navigation
Das, S., Cashman, D., Chang, R., & Endert, A. (2020) · Proceedings · doi:10.20380/gi2020.15 · Cited by 2
DSWorkFlow: A Framework for Capturing Data Scientists’ Workflows
Mash, M., Rosenthal, S. L., & Simmons, R. G. (2021) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3411763.3451683 · Cited by 2
Active Correction for Incremental Speaker Diarization of a Collection with Human in the Loop
Prokopalo, Y., Shamsi, M., Barrault, L., Meignier, S., & Larcher, A. (2022) · Applied Sciences · doi:10.3390/app12041782 · Cited by 2
Discovering the Sweet Spot of Human-Computer Configurations: A Case Study in Information ExtractionPreprint
Mackeprang, M., Müller-Birn, C., & Stauss, M. T. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.07065 · Cited by 2
Specifying and Interpreting Reinforcement Learning Policies through Simulatable Machine Learning.Preprint
Tambwekar, P., Silva, A., Gopalan, N., & Gombolay, M. (2021) · arXiv (Cornell University) · Cited by 2
Opportunities and Challenges in Machine Learning With IoT
Tanwar, S., Garg, J., Gupta, M., & Rana, A. (2022) · doi:10.1002/9781119763499.ch11 · Cited by 2
Interactive Online Machine Learning
Tegen, A. (2022) · doi:10.24834/isbn.9789178772810 · Cited by 3
Delacroix, S. (2024) · Social Science Research Network · doi:10.2139/ssrn.4751774 · Cited by 2
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
Koch, M. R., Ziegler, J., Reuter, C., Schlegel, T., & Prilla, M. (2020) · Informatik-Spektrum · doi:10.1007/s00287-020-01299-8 · Cited by 1
2nd Workshop on Human-Interactive Robot Learning (HIRL)
Mirsky, R., Baraka, K., Faulkner, T. K., Hart, J., Xiao, X., Yedidsion, H., Idrees, I., & Gordon, E. K. (2023) · ACM/IEEE International Conference on Human-Robot Interaction (HRI) · doi:10.1145/3568294.3579962 · Cited by 1
Cao, S., Xu, Y., Zhou, T., & Wu, A. (2024) · Frontiers in Psychology · doi:10.3389/fpsyg.2024.1322482 · Cited by 1
An Interactive Interface for Feature Space Navigation
Cappuccio, E., Beretta, I., Manerba, M. M., & Rinzivillo, S. (2024) · Frontiers in artificial intelligence and applications · doi:10.3233/faia240184 · Cited by 1
Balthasar, M. (2024) · i-com · doi:10.1515/icom-2024-0016 · Cited by 1
Kang, R., & Rau, P. P. (2024) · Interacting with Computers · doi:10.1093/iwc/iwae034 · Cited by 1
Example-Based Conditioning for Text-to-Image Generative Models
Takada, A., Kawabe, W., & Sugano, Y. (2024) · IEEE Access · doi:10.1109/access.2024.3486055 · Cited by 1
Learning from Demonstrations via Deformable Residual Multi-Attention Domain-Adaptive Meta-Learning
Yan, Z., Gan, Z., Lu, G., Liu, J., & Li, W. (2025) · Biomimetics · doi:10.3390/biomimetics10020103 · Cited by 2
Using similarity network analysis to improve text similarity calculations
Witschard, D., Kucher, K., Jusufi, I., & Kerren, A. (2025) · Applied Network Science · doi:10.1007/s41109-025-00699-7 · Cited by 2
Human-Computer Interaction, User Experience, and AI Systems
Fischer, M., & Rößler, S. (2025) · Human-computer interaction series · doi:10.1007/978-3-031-61375-3_2 · Cited by 1
Cheng, Y., Wang, Y., & Pei, Y. (2025) · IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec65147.2025.11043128 · Cited by 1
TEMPO: Timestep Explanations for Modeling Preferences in Online Preference-Based RL
Karlaus, J., & Schwenker, F. (2025) · IEEE Access · doi:10.1109/access.2025.3588936 · Cited by 1
Shubhanshi (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-5951-9.ch009 · Cited by 1
Eliciting Multimodal Approaches for Machine Learning–Assisted Photobook Creation
Bittner, S.-J., Barz, M., & Sonntag, D. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-05002-1_31 · Cited by 1
On-Device Interaction Mining MHCI024
Arsan, D., Guo, C., Wellyanto, M. R., Ji, E. R., Talton, J. O., & Kumar, R. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3743726 · Cited by 1
Model-informed oracle training for enhancing active learning without external knowledge
Cha, Y. (2025) · Machine Learning with Applications · doi:10.1016/j.mlwa.2025.100775 · Cited by 1
Kumbhar, V. R., Sireesha, G., Jadhav, V. D., Barve, A., Sharma, R., & Soudagar, M. E. M. (2025) · International Conference on Intelligent Communication Networks and Computational Techniques (ICICNCT) · doi:10.1109/icicnct66124.2025.11232580 · Cited by 1
Human-Centered Artificial Intelligence in Cybersecurity
Chen, J., Garcia, K. R., Zhang, Y. “., Dudley, L. E., & Warren, A. D. (2025) · Springer eBooks · doi:10.1007/978-981-97-8440-0_99-1 · Cited by 1
Carrasco-Aguilar, Á., Shu, Z., Camacho-Ruiz, M., & Arias-Oliva, M. (2026) · Springer eBooks · doi:10.1007/978-3-032-08213-8_11 · Cited by 1
Bridging Big Data Machine Learning and Human Insight with Visual Interaction
Abraham, A. (2025) · International Conference on Image Information Processing (ICIIP) · doi:10.1109/iciip68302.2025.11346395 · Cited by 2
Kawabe, W., Fukuda, H., Shitara, A., Nakao, Y., & Sugano, Y. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2026.2619622 · Cited by 1
Nakrani, J., Nair, D., Gadekar, P., Dalvi, T., & Tiwari, M. (2025) · DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry (IDICAIHEI) · doi:10.1109/idicaihei65991.2025.11379045 · Cited by 1
Investigating the Impact of Robot Degree of Redundancy on Learning from Demonstration
Bilal, M., Chacon, D. A., Lipovetzky, N., Oetomo, D., & Johal, W. (2026) · ACM/IEEE International Conference on Human-Robot Interaction (HRI) · doi:10.1145/3757279.3785606 · Cited by 1
AI-Driven Human Capital Management for Academic and Research Advancement
Bhaskaru, O., & Lalitha, K. (2026) · IGI Global eBooks · doi:10.4018/979-8-3373-3867-5.ch002 · Cited by 1
Mishra, S. (2026) · IEEE International Conference on Omni-layer Intelligent Systems (COINS) · doi:10.1109/coins69995.2026.11715619 · Cited by 1
Fan, G. F., Liu, D., & Pan, L. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791358 · Cited by 1
Selim, A. M., Bhatti, O. S., Gomaa, A., Barz, M., & Sonntag, D. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790594 · Cited by 1
How Well do LLMs Assist Parents in Assessing Child Appropriateness of Videos?
Nawshin, S., Ishoel, A. P., Buduru, A. B., & Kapadia, A. C. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791610 · Cited by 1
Four Aspirations for Evaluating AI-Infused Technologies: A Narrative Review
Morey, D. A., Jalaeian, M., Reynolds, M. E., McGeorge, N., Oldham, H. K., & Rayo, M. F. (2026) · Journal of Cognitive Engineering and Decision Making · doi:10.1177/15553434261443032 · Cited by 1
Thaariq, Z. Z. A. (2026) · Review of Education · doi:10.1002/rev3.70163 · Cited by 1
Embedding governance in AI security culture: from trust calibration to accountable decisions
Żywiołek, J., Wolniak, R., & Grebski, W. (2026) · International Journal of Information Security · doi:10.1007/s10207-026-01265-7 · Cited by 1
PenTest2.0: Advancing Ethical Hacking with GenAI-Driven Privilege Escalation
Al-Sinani, H. S., Mitchell, C. J., & Al-Hosni, A. S. (2026) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-3-032-23335-6_2 · Cited by 1
Automated interictal epileptic spike detection from simple and noisy annotations in MEG data
Mouchès, P., Jung, J., Demasson, A., Guinard, A., Bouet, R., Marchal, R., & Quentin, R. (2026) · Brain Structure and Function · doi:10.1007/s00429-026-03118-4 · Cited by 1
From Co-Pilots to Co-Workers: A Formal Typology of Human–Agent Collaboration in Organizations
Rodrigo-Ginés, F.-J., Chamorro-Padial, J., Pretel-Villanueva, J., Aguilera-Aguilera, J., & Lugli, V. (2026) · Conference on Algebraic Informatics · doi:10.1109/cai68641.2026.11536465
Human-in-the-Loop for Artificial Intelligence
Stephens, M., Khurshid, S., & Esposito, M. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-7036-1.ch013 · Cited by 1
Chen, C., Lv, L., & Xu, C. (2026) · Information & Management · doi:10.1016/j.im.2026.104400 · Cited by 1
Metadata
- Authors
- Amershi, S., Cakmak, M., Knox, W. B., & Kulesza, T.
- Affiliations
- Microsoft (United States) (US)
- Massachusetts Institute of Technology (US)
- Oregon State University (US)
- Venue
- AI Magazine
- Publisher
- Wiley
- Published
- 2014-12
- Volume
- 35
- Issue
- 4
- Pages
- 105-120
- ISSN
- 0738-4602 (ISSN-L)
- 2371-9621
- OpenAlex
- W1501005121
- Cited by
- 1,084
- References
- 55
- Citations by year
- 2026: 139 · 2025: 127 · 2024: 106 · 2023: 101 · 2022: 111 · 2021: 172 · 2020: 111 · 2019: 83 · 2018: 63 · 2017: 33 · 2016: 22 · 2015: 14 · 2014: 2
- FWCI
- 23.60
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- AI Magazine (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Keywords
- interactive machine learning, human-in-the-loop learning, user modeling, user experience, human-computer interaction, learning systems
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
