mlsanityData to 7 Oct 2026

EXMOS: Explanatory Model Steering Through Multifaceted Explanations and Data Configurations

arXiv 2402.00491 ↗First posted 1 Feb 2024cs.AI cs.HCcited by 14

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Abstract (arXiv)

Explanations in interactive machine-learning systems facilitate debugging and improving prediction models. However, the effectiveness of various global model-centric and data-centric explanations in aiding domain experts to detect and resolve potential data issues for model improvement remains unexplored. This research investigates the influence of data-centric and model-centric global explanations in systems that support healthcare experts in optimising models through automated and manual data configurations. We conducted quantitative (n=70) and qualitative (n=30) studies with healthcare experts to explore the impact of different explanations on trust, understandability and model improvement. Our results reveal the insufficiency of global model-centric explanations for guiding users during data configuration. Although data-centric explanations enhanced understanding of post-configuration system changes, a hybrid fusion of both explanation types demonstrated the highest effectiveness. Based on our study results, we also present design implications for effective explanation-driven interactive machine-learning systems.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Builds on 30 of its 90 references that are in the map. First posted on arXiv 1 Feb 2024.

Builds on

30 of 90 references.

References · 30

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,690
  3. 2017A Unified Approach to Interpreting Model Predictionscited by 6,488
  4. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,040
  5. 2013Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Mapscited by 2,348
  6. 2017Explanation in Artificial Intelligence: Insights from the Social Sciencescited by 1,201
  7. 2018A Survey Of Methods For Explaining Black Box Modelscited by 970
  8. 2018Metrics for Explainable AI: Challenges and Prospectscited by 257
  9. 2020Questioning the AI: Informing Design Practices for Explainable AI User Experiencescited by 251
  10. 2023Data-centric Artificial Intelligence: A Surveycited by 202
  11. 2020Making deep neural networks right for the right scientific reasons by interacting with their explanationscited by 141
  12. 2019explAIner: A Visual Analytics Framework for Interactive and Explainable Machine Learningcited by 69
18 more
  1. 2022TalkToModel: Explaining Machine Learning Models with Interactive Natural Language Conversationscited by 65
  2. 2022"Help Me Help the AI": Understanding How Explainability Can Support Human-AI Interactioncited by 63
  3. 2023The No Free Lunch Theorem, Kolmogorov Complexity, and the Role of Inductive Biases in Machine Learningcited by 63
  4. 2022Leveraging Explanations in Interactive Machine Learning: An Overviewcited by 52
  5. 2015Interpretable Classification Models for Recidivism Predictioncited by 44
  6. 2022Connecting Algorithmic Research and Usage Contexts: A Perspective of Contextualized Evaluation for Explainable AIcited by 40
  7. 2022Visual Attention Methods in Deep Learning: An In-Depth Surveycited by 37
  8. 2021How to avoid machine learning pitfalls: a guide for academic researcherscited by 34
  9. 2019Explaining Models: An Empirical Study of How Explanations Impact Fairness Judgmentcited by 31
  10. 2022Interpretability, Then What? Editing Machine Learning Models to Reflect Human Knowledge and Valuescited by 25
  11. 2020Soliciting Human-in-the-Loop User Feedback for Interactive Machine Learning Reduces User Trust and Impressions of Model Accuracycited by 19
  12. 2023Directive Explanations for Monitoring the Risk of Diabetes Onset: Introducing Directive Data-Centric Explanations and Combinations to Support What-If Explorationscited by 18
  13. 2020Machine Guides, Human Supervises: Interactive Learning with Global Explanationscited by 16
  14. 2019Fair-by-design explainable models for prediction of recidivismcited by 8
  15. 2020Active Learning for Skewed Data Setscited by 6
  16. 2023DeepSeer: Interactive RNN Explanation and Debugging via State Abstractioncited by 6
  17. 2021Explainers in the Wild: Making Surrogate Explainers Robust to Distortions through Perceptioncited by 4
  18. 2022A Nested Genetic Algorithm for Explaining Classification Data Sets with Decision Rulescited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 4; 2025: 8; 2026: 2.

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  1. 2024Abstraction Alignment: Comparing Model-Learned and Human-Encoded Conceptual Relationshipscited by 7
  2. 2025Tempo: Helping Data Scientists and Domain Experts Collaboratively Specify Predictive Modeling Taskscited by 6
  3. 2024Explanatory Debiasing: Involving Domain Experts in the Data Generation Process to Mitigate Representation Bias in AI Systemscited by 5
  4. 2024An Explanatory Model Steering System for Collaboration between Domain Experts and AIcited by 4
  5. 2024XEQ Scale for Evaluating XAI Experience Qualitycited by 2
  6. 2025Show Me How: Benefits and Challenges of Agent-Augmented Counterfactual Explanations for Non-Expert Userscited by 2
  7. 2025Let's Get You Hired: A Job Seeker's Perspective on Multi-Agent Recruitment Systems for Explaining Hiring Decisionscited by 2
  8. 2025Visual-Conversational Interface for Evidence-Based Explanation of Diabetes Risk Predictioncited by 2
  9. 2025Interactivity x Explainability: Toward Understanding How Interactivity Can Improve Computer Vision Explanationscited by 1
  10. 2025Exploring the Impact of Explainable AI and Cognitive Capabilities on Users' Decisionscited by 1
  11. 2025Measurement as Bricolage: Examining How Data Scientists Construct Target Variables for Predictive Modeling Taskscited by 1
  12. 2025Explanation User Interfaces: A Systematic Literature Reviewcited by 0
2 more
  1. 2026When Life Gives You AI, Will You Turn It Into A Market for Lemons? Understanding How Information Asymmetries About AI System Capabilities Affect Market Outcomes and Adoptioncited by 0
  2. 2026Empowering Affected Individuals to Shape AI Fairness Assessments: Processes, Criteria, and Toolscited by 0