mlsanityData to 7 Oct 2026

Lessons Learned from EXMOS User Studies: A Technical Report Summarizing Key Takeaways from User Studies Conducted to Evaluate The EXMOS Platform

arXiv 2310.02063 ↗First posted 3 Oct 2023cs.LG cs.HCcited by 4

Not on the map. Why?

Abstract (arXiv)

In the realm of interactive machine-learning systems, the provision of explanations serves as a vital aid in the processes of debugging and enhancing prediction models. However, the extent to which various global model-centric and data-centric explanations can effectively assist domain experts in detecting and resolving potential data-related issues for the purpose of model improvement has remained largely unexplored. In this technical report, we summarise the key findings of our two user studies. Our research involved a comprehensive examination of the impact of global explanations rooted in both data-centric and model-centric perspectives within systems designed to support healthcare experts in optimising machine learning models through both automated and manual data configurations. To empirically investigate these dynamics, we conducted two user studies, comprising quantitative analysis involving a sample size of 70 healthcare experts and qualitative assessments involving 30 healthcare experts. These studies were aimed at illuminating the influence of different explanation types on three key dimensions: trust, understandability, and model improvement. Results show that global model-centric explanations alone are insufficient for effectively guiding users during the intricate process of data configuration. In contrast, data-centric explanations exhibited their potential by enhancing the understanding of system changes that occur post-configuration. However, a combination of both showed the highest level of efficacy for fostering trust, improving understandability, and facilitating model enhancement among healthcare experts. We also present essential implications for developing interactive machine-learning systems driven by explanations. These insights can guide the creation of more effective systems that empower domain experts to harness the full potential of machine learning

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 8 of its 26 references that are in the map. First posted on arXiv 3 Oct 2023.

Builds on

8 of 26 references.

References · 8

  1. 2018A Survey Of Methods For Explaining Black Box Modelscited by 970
  2. 2020Making deep neural networks right for the right scientific reasons by interacting with their explanationscited by 141
  3. 2019explAIner: A Visual Analytics Framework for Interactive and Explainable Machine Learningcited by 69
  4. 2022Rethinking Explainability as a Dialogue: A Practitioner's Perspectivecited by 52
  5. 2022Leveraging Explanations in Interactive Machine Learning: An Overviewcited by 52
  6. 2022Interpretability, Then What? Editing Machine Learning Models to Reflect Human Knowledge and Valuescited by 25
  7. 2020Soliciting Human-in-the-Loop User Feedback for Interactive Machine Learning Reduces User Trust and Impressions of Model Accuracycited by 19
  8. 2023Directive Explanations for Monitoring the Risk of Diabetes Onset: Introducing Directive Data-Centric Explanations and Combinations to Support What-If Explorationscited by 18

Built on by

Cited by year

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

4 papers.

  1. 2024Abstraction Alignment: Comparing Model-Learned and Human-Encoded Conceptual Relationshipscited by 7
  2. 2024Explanatory Debiasing: Involving Domain Experts in the Data Generation Process to Mitigate Representation Bias in AI Systemscited by 5
  3. 2024An Explanatory Model Steering System for Collaboration between Domain Experts and AIcited by 4
  4. 2025Show Me How: Benefits and Challenges of Agent-Augmented Counterfactual Explanations for Non-Expert Userscited by 2