Google has announced the Inclusive Images Competition on Kaggle, challenging participants to develop image recognition systems that can perform well on datasets drawn from regions across the world. A machine learning model’s training data has a significant impact on the model’s effectiveness, and many models perform poorly when exposed to real-world data when their training data is only representative of a narrow demographic. For example, a model that labels wedding photos may not properly identify an image as a wedding photo if individuals in it are not wearing Western attire. Competition participants will train their models using Open Images, a large, publicly available image dataset, and Google will use two datasets that were crowd sourced from across the world to evaluate the submissions.
Making Machine Learning Models Inclusive
Michael McLaughlin is a research analyst at the Center for Data Innovation. He researches and writes about a variety of issues related to information technology and Internet policy, including digital platforms, e-government, and artificial intelligence. Michael graduated from Wake Forest University, where he majored in Communication with Minors in Politics and International Affairs and Journalism. He received his Master’s in Communication at Stanford University, specializing in Data Journalism.
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