Designing Data: Proactive Data Collection and Iteration for Machine Learning
This addresses data diversity issues for ML developers, offering a proactive method to improve dataset quality, though it is incremental as it builds on existing HCI and ML practices.
The paper tackles the problem of lack of diversity in data collection causing failures in machine learning by proposing an iterative approach called 'designing data' that integrates HCI concepts with ML techniques, resulting in models trained on designed datasets generalizing better across intersectional groups and effective debugging using data familiarity.
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.