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Florian Bemmann, Daniel Buschek, Heinrich Hussmann
Interactive End-User Machine Learning to Boost Explainability and Transparency of Digital Footprint Data
HCXAI Workshop @ CHI 2021, May 08-13, 2021, Yokohama, Japan (bib)

Data collecting applications today only inform users about what data is collected directly, but not about what can be inferred from it. However, awareness of potential inferences is important from a data privacy perspective, especially as inferred information has been shown to be applicable for unethical applications as well. We propose interactive user involvement in model building: Participatory Model Design lets users interactively investigate what happens to their data, to convey which further information could be inferred. To operationalize such interactive explainability in practice, we created a prototype that integrates interactive personalized model training into a behaviour logging app for mobile sensing research. With our prototype we hope to spark discussions and further work towards strong direct user involvement in data collection and inference, to increase data privacy in the age of big data, and to facilitate explainability and transparency of downstream prediction systems.