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

TL;DR

This paper introduces explanation strategies as a lens to understand how technical explanations influence non-ML experts' interpretations, emphasizing contextual and participatory design perspectives in ML interpretability research.

Contribution

It proposes explanation strategies based on philosophy of technology to analyze interpretability in socio-technical contexts, extending beyond traditional technical approaches.

Findings

Explanation strategies help analyze stakeholder sense-making.

Participatory design reveals contextual interpretability needs.

Methodological implications for co-design workshops.

Abstract

During a research project in which we developed a machine learning (ML) driven visualization system for non-ML experts, we reflected on interpretability research in ML, computer-supported collaborative work and human-computer interaction. We found that while there are manifold technical approaches, these often focus on ML experts and are evaluated in decontextualized empirical studies. We hypothesized that participatory design research may support the understanding of stakeholders' situated sense-making in our project, yet, found guidance regarding ML interpretability inexhaustive. Building on philosophy of technology, we formulated explanation strategies as an empirical-analytical lens explicating how technical explanations mediate the contextual preferences concerning people's interpretations. In this paper, we contribute a report of our proof-of-concept use of explanation strategies to analyze a co-design workshop with non-ML experts, methodological implications for participatory design research, design implications for explanations for non-ML experts and suggest further investigation of technological mediation theories in the ML interpretability space.