Tackling Socioeconomic Bias in Machine Learning

Abstract: 

Despite the meteoric rise of ML, most commercially available datasets only represent a small fraction of humanity, with a skew towards high-income populations. To combat algorithmic bias, businesses need to train their ML algorithms on datasets that are representative of all the populations that will be affected by AI deployment. Modern companies and HR teams have learned that inclusive, diverse workforces perform better, and it’s time for the ML community to apply the same wisdom to its training data, especially as their products and services start to impact billions of people across emerging economies and developing countries. This presentation will discuss how socioeconomically diverse datasets can help address algorithmic bias, drawing from Cody’s experience co-creating the open-access Dollar Street Dataset (alongside Gapminder, Harvard University, and MLCommons) and his deep academic knowledge in the space.

Session Outline:

The presentation's goal is to inspire and enable ML developers and leaders to deliver more socioeconomically inclusive algorithms and a fairer future for all communities.

Bio: 

Cody Coleman is a co-founder of Coactive AI, an analytics platform for visual content, and serves as the CEO. Coactive leverages AI to make it easy for enterprises to search, filter, and analyze large amounts of image and video data by bringing structure to unstructured data. He is also a founding member of MLCommons, and his work spans from high-performance deep learning to data-centric AI. He holds a PhD in CS from Stanford and MEng and a BS degree in EECS from MIT.

Open Data Science

 

 

 

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