Designing Data: Proactive Data Collection and Iteration for Machine Learning
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 and manage data collection, iteration, and model training are necessary for evaluating whether datasets reflect real world variability. We present designing data, …
Read more “Designing Data: Proactive Data Collection and Iteration for Machine Learning”