Eliciting fairness via micro-ethics embedded interfaces for machine learning workflows

Abstract

Automated and no-code ML tools make model building accessible but can obscure harms that arise when users include sensitive attributes. We embed micro-ethics nudges at key workflow moments and evaluate downstream fairness outcomes and user experience. In a between-subjects experiment (N=34) participants used a simplified AutoML web tool on a subset of the HMDA mortgage dataset with a 10-minute modeling task. The intervention combined in text notice when selecting sensitive attributes such as race, gender, ethnicity and post-training model explanation visualizations, while the base condition showed only model performance metrics. Participants in the Intervention group included significantly fewer sensitive features and produced models with substantially smaller equal-opportunity gaps, while System Usability Scale scores did not differ significantly across conditions. Moral acceptability did not significantly differ between conditions, though it trended lower under the intervention. We conclude that minimal, well timed fairness feedback can meaningfully reduce bias in rapid prototyping workflows. We also discuss design patterns for embedding fairness and the implications of increased moral sensitivity for tool adoption.

Keywords: Explainable AI, Human-centered AI, Algorithmic Fairness

DOI: 10.54941/ahfe1007537