DeepAI AI Chat
Log In Sign Up

"Garbage In, Garbage Out" Revisited: What Do Machine Learning Application Papers Report About Human-Labeled Training Data?

07/05/2021
by   R. Stuart Geiger, et al.
20

Supervised machine learning, in which models are automatically derived from labeled training data, is only as good as the quality of that data. This study builds on prior work that investigated to what extent 'best practices' around labeling training data were followed in applied ML publications within a single domain (social media platforms). In this paper, we expand by studying publications that apply supervised ML in a far broader spectrum of disciplines, focusing on human-labeled data. We report to what extent a random sample of ML application papers across disciplines give specific details about whether best practices were followed, while acknowledging that a greater range of application fields necessarily produces greater diversity of labeling and annotation methods. Because much of machine learning research and education only focuses on what is done once a "ground truth" or "gold standard" of training data is available, it is especially relevant to discuss issues around the equally-important aspect of whether such data is reliable in the first place. This determination becomes increasingly complex when applied to a variety of specialized fields, as labeling can range from a task requiring little-to-no background knowledge to one that must be performed by someone with career expertise.

READ FULL TEXT

page 1

page 5

page 9

page 13

page 18

page 19

page 20

page 21

04/11/2022

The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink

Machine Learning (ML) workloads have rapidly grown in importance, but ra...
08/12/2016

Rapid Classification of Crisis-Related Data on Social Networks using Convolutional Neural Networks

The role of social media, in particular microblogging platforms such as ...
01/04/2022

Survey on the Convergence of Machine Learning and Blockchain

Machine learning (ML) has been pervasively researched nowadays and it ha...
02/14/2019

Automatic Labeled LiDAR Data Generation based on Precise Human Model

Following improvements in deep neural networks, state-of-the-art network...
11/04/2022

The 'Problem' of Human Label Variation: On Ground Truth in Data, Modeling and Evaluation

Human variation in labeling is often considered noise. Annotation projec...