mlsanityData to 14 Oct 2026

An Explanatory Model Steering System for Collaboration between Domain Experts and AI

arXiv 2405.13038 ↗First posted 17 May 2024cs.HC cs.AIcited by 4

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Abstract (arXiv)

With the increasing adoption of Artificial Intelligence (AI) systems in high-stake domains, such as healthcare, effective collaboration between domain experts and AI is imperative. To facilitate effective collaboration between domain experts and AI systems, we introduce an Explanatory Model Steering system that allows domain experts to steer prediction models using their domain knowledge. The system includes an explanation dashboard that combines different types of data-centric and model-centric explanations and allows prediction models to be steered through manual and automated data configuration approaches. It allows domain experts to apply their prior knowledge for configuring the underlying training data and refining prediction models. Additionally, our model steering system has been evaluated for a healthcare-focused scenario with 174 healthcare experts through three extensive user studies. Our findings highlight the importance of involving domain experts during model steering, ultimately leading to improved human-AI collaboration.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 13 of its 32 references that are in the map. First posted on arXiv 17 May 2024.

Builds on

13 of 32 references.

References · 13

  1. 2017A Unified Approach to Interpreting Model Predictionscited by 6,555
  2. 2013Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Mapscited by 2,371
  3. 2018A Survey Of Methods For Explaining Black Box Modelscited by 973
  4. 2023Data-centric Artificial Intelligence: A Surveycited by 203
  5. 2020Making deep neural networks right for the right scientific reasons by interacting with their explanationscited by 142
  6. 2022Leveraging Explanations in Interactive Machine Learning: An Overviewcited by 53
  7. 2022Interpretability, Then What? Editing Machine Learning Models to Reflect Human Knowledge and Valuescited by 25
  8. 2023Directive Explanations for Monitoring the Risk of Diabetes Onset: Introducing Directive Data-Centric Explanations and Combinations to Support What-If Explorationscited by 18
  9. 2024EXMOS: Explanatory Model Steering Through Multifaceted Explanations and Data Configurationscited by 14
  10. 2023DeepSeer: Interactive RNN Explanation and Debugging via State Abstractioncited by 6
  11. 2023Towards Directive Explanations: Crafting Explainable AI Systems for Actionable Human-AI Interactionscited by 6
  12. 2023Lessons Learned from EXMOS User Studies: A Technical Report Summarizing Key Takeaways from User Studies Conducted to Evaluate The EXMOS Platformcited by 4
1 more
  1. 2021AHMoSe: A Knowledge-Based Visual Support System for Selecting Regression Machine Learning Modelscited by 2

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Cited by year (2026 to 14 Oct)

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

4 papers.

  1. 2024Explanatory Debiasing: Involving Domain Experts in the Data Generation Process to Mitigate Representation Bias in AI Systemscited by 5
  2. 2025Show Me How: Benefits and Challenges of Agent-Augmented Counterfactual Explanations for Non-Expert Userscited by 2
  3. 2025Exploring the Impact of Explainable AI and Cognitive Capabilities on Users' Decisionscited by 1
  4. 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