English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

During a research project in which we developed a machine learning (ML)driven visualization system for non-ML experts, we reflected oninterpretability research in ML, computer-supported collaborative work andhuman-computer interaction. We found that while there are manifold technicalapproaches, these often focus on ML experts and are evaluated indecontextualized empirical studies. We hypothesized that participatory designresearch may support the understanding of stakeholders' situated sense-makingin our project, yet, found guidance regarding ML interpretability inexhaustive.Building on philosophy of technology, we formulated explanation strategies asan empirical-analytical lens explicating how technical explanations mediate thecontextual preferences concerning people's interpretations. In this paper, wecontribute a report of our proof-of-concept use of explanation strategies toanalyze a co-design workshop with non-ML experts, methodological implicationsfor participatory design research, design implications for explanations fornon-ML experts and suggest further investigation of technological mediationtheories in the ML interpretability space.

Explanation Strategies as an Empirical-Analytical Lens for Socio-Technical Contextualization of Machine Learning Interpretability