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In the realm of interactive machine-learning systems, the provision ofexplanations serves as a vital aid in the processes of debugging and enhancingprediction models. However, the extent to which various global model-centricand data-centric explanations can effectively assist domain experts indetecting and resolving potential data-related issues for the purpose of modelimprovement has remained largely unexplored. In this technical report, wesummarise the key findings of our two user studies. Our research involved acomprehensive examination of the impact of global explanations rooted in bothdata-centric and model-centric perspectives within systems designed to supporthealthcare experts in optimising machine learning models through both automatedand manual data configurations. To empirically investigate these dynamics, weconducted two user studies, comprising quantitative analysis involving a samplesize of 70 healthcare experts and qualitative assessments involving 30healthcare experts. These studies were aimed at illuminating the influence ofdifferent explanation types on three key dimensions: trust, understandability,and model improvement. Results show that global model-centric explanationsalone are insufficient for effectively guiding users during the intricateprocess of data configuration. In contrast, data-centric explanations exhibitedtheir potential by enhancing the understanding of system changes that occurpost-configuration. However, a combination of both showed the highest level ofefficacy for fostering trust, improving understandability, and facilitatingmodel enhancement among healthcare experts. We also present essentialimplications for developing interactive machine-learning systems driven byexplanations. These insights can guide the creation of more effective systemsthat empower domain experts to harness the full potential of machine learning

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