Interactive sequential analysis of a model improves the performance of human decision-making
Interactive sequential analysis of a model improves the performance of human decision-making by Hubert Baniecki (University of Warsaw)
Evaluation of explainable machine learning, especially with human subjects, became mandatory for the trustworthy adoption of predictive models in various applications. This contribution focuses on reporting the results from a user study considering evaluating model explanations in a real-world medical use case; as described in Section 5 of the paper: Baniecki, D. Parzych, P. Biecek. The Grammar of Interactive Explanatory Model Analysis, 2022 (https://arxiv.org/abs/2005.00497v4). The contribution's title is mainly supported by Tables 4 & 5. Moreover, we find both: the user study design and the IEMA grammar's theoretical background worth discussing.
The talk was delivered during ML in PL Conference 2022 as a part of Contributed Talks. The conference was organized by a non-profit NGO called ML in PL Association.
ML in PL Association website: https://mlinpl.org/
ML in PL Conference 2022 website: https://conference2022.mlinpl.org/
ML In PL Conference 2023 website: https://conference2023.mlinpl.org/
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ML in PL Association was founded based on the experiences in organizing of the ML in PL Conference (formerly PL in ML), the ML in PL Association is a non-profit organization devoted to fostering the machine learning community in Poland and Europe and promoting a deep understanding of ML methods. Even though ML in PL is based in Poland, it seeks to provide opportunities for international cooperation.
