Machine Learning that Matters

Abstract

Much of current machine learning (ML) research has lost its connection to problems of import to the larger world of science and society. From this perspective, there exist glaring limitations in the data sets we investigate, the metrics we employ for evaluation, and the degree to which results are communicated back to their originating domains. What changes are needed to how we conduct research to increase the impact that ML has? We present six Impact Challenges to explicitly focus the field?s energy and attention, and we discuss existing obstacles that must be addressed. We aim to inspire ongoing discussion and focus on ML that matters.

ICML2012

Cited by in corpus (12)

• How I failed machine learning in medical imaging -- shortcomings and recommendations
• A Plea for Neutral Comparison Studies in Computational Sciences
• A Hierarchy of Limitations in Machine Learning
• REAL ML: Recognizing, Exploring, and Articulating Limitations of Machine Learning Research
• Can Fairness be Automated? Guidelines and Opportunities for Fairness-aware AutoML
• Better, Not Just More: Data-Centric Machine Learning for Earth Observation
• Concise comparative summaries (CCS) of large text corpora with a human experiment
• Generating 3D Bio-Printable Patches Using Wound Segmentation and Reconstruction to Treat Diabetic Foot Ulcers
• Enabling Collaborative Data Science Development with the Ballet Framework
• Likelihood Ratios for Out-of-Distribution Detection
• Detecting Environmental Violations with Satellite Imagery in Near Real Time: Land Application under the Clean Water Act
• TEASPN: Framework and Protocol for Integrated Writing Assistance Environments