Do natural language processing tools from Amazon and Google contain racial and gender bias? Charles Earl investigates.
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Christo Wilson Discusses the Ethics of Online Behavioral Experiments
Charles Earl shares the insights he gleaned after hearing a talk from Dr. Christo Wilson about ethics in online behavioral experiements.
… Continue readingUsing ML for Campaign Optimization: Our Journey to Marketing Science at Automattic
Demet on using pipe to segment and study how users respond to marketing email campaigns.
… Continue readingData Science Insights from Cameron Davidson-Pilon
Cameron Davidson-Pilon talks to the Automattic data scientists about his work post-Shopify, data practices, and how data scientists can best serve their organizations.
… Continue readingThe 2019 Fairness, Accountability, and Transparency Conference
Charles Earl shares what he learned from this year's conference.
… Continue readingHow to Increase Retention and Revenue in 1,000 Nontrivial Steps
Yanir reflects on how data scientists at Automattic work to improve customer retention.
… Continue readingBuilding Thousands of Reproducible ML Models with pipe, the Automattic Machine Learning Pipeline
Demet takes you deep into pipe, a tool that allows anyone at Automattic to build solid machine learning models.
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