Designing Data: Proactive Data Collection and Iteration for Machine Learning

TL;DR

This paper introduces an iterative data collection approach that combines HCI and ML techniques to improve dataset diversity and model generalization across real-world variability.

Contribution

It presents a novel designing data process with planning, monitoring, and familiarity steps to enhance data collection and debugging in ML workflows.

Findings

Models trained on designed datasets generalize better across groups.

Data familiarity helps identify and debug dataset issues.

The approach improves model robustness and fairness.

Abstract

Lack of diversity in data collection has caused significant failures in machine learning (ML) applications. While ML developers perform post-collection interventions, these are time intensive and rarely comprehensive. Thus, new methods to track & manage data collection, iteration, and model training are necessary for evaluating whether datasets reflect real world variability. We present designing data, an iterative approach to data collection connecting HCI concepts with ML techniques. Our process includes (1) Pre-Collection Planning, to reflexively prompt and document expected data distributions; (2) Collection Monitoring, to systematically encourage sampling diversity; and (3) Data Familiarity, to identify samples that are unfamiliar to a model using density estimation. We apply designing data to a data collection and modeling task. We find models trained on ''designed'' datasets generalize better across intersectional groups than those trained on similarly sized but less targeted datasets, and that data familiarity is effective for debugging datasets.