mlsanityData to 7 Oct 2026

Interactive Machine Learning: A State of the Art Review

arXiv 2207.06196 ↗First posted 13 Jul 2022cs.LGcited by 6

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Abstract (arXiv)

Machine learning has proved useful in many software disciplines, including computer vision, speech and audio processing, natural language processing, robotics and some other fields. However, its applicability has been significantly hampered due its black-box nature and significant resource consumption. Performance is achieved at the expense of enormous computational resource and usually compromising the robustness and trustworthiness of the model. Recent researches have been identifying a lack of interactivity as the prime source of these machine learning problems. Consequently, interactive machine learning (iML) has acquired increased attention of researchers on account of its human-in-the-loop modality and relatively efficient resource utilization. Thereby, a state-of-the-art review of interactive machine learning plays a vital role in easing the effort toward building human-centred models. In this paper, we provide a comprehensive analysis of the state-of-the-art of iML. We analyze salient research works using merit-oriented and application/task oriented mixed taxonomy. We use a bottom-up clustering approach to generate a taxonomy of iML research works. Research works on adversarial black-box attacks and corresponding iML based defense system, exploratory machine learning, resource constrained learning, and iML performance evaluation are analyzed under their corresponding theme in our merit-oriented taxonomy. We have further classified these research works into technical and sectoral categories. Finally, research opportunities that we believe are inspiring for future work in iML are discussed thoroughly.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 49 of its 131 references that are in the map. First posted on arXiv 13 Jul 2022.

Builds on

49 of 131 references.

References · 49

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37 more
  1. 2019Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methodscited by 440
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 2; 2026: 1.

6 papers.

  1. 2024Do Large Language Models Perform the Way People Expect? Measuring the Human Generalization Functioncited by 24
  2. 2024Human-In-The-Loop Machine Learning for Safe and Ethical Autonomous Vehicles: Principles, Challenges, and Opportunitiescited by 6
  3. 2025From Prototypes to Sparse ECG Explanations: SHAP-Driven Counterfactuals for Multivariate Time-Series Multi-class Classificationcited by 3
  4. 2024Integrating Human Expertise in Continuous Spaces: A Novel Interactive Bayesian Optimization Framework with Preference Expected Improvementcited by 0
  5. 2025The Human-Data-Model Interaction Canvas for Visual Analyticscited by 0
  6. 2026Position: We Need Practical AI Alignment Methods to Mirror Human Reasoningcited by 0