Do natural language processing tools from Amazon and Google contain racial and gender bias? Charles Earl investigates.
… Continue readingChristo 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 readingThis Week in Data Reading: Project Estimation, Abandoning Significance, and Video Games.
This week, Rob suggests using statistics to help you plan your next project, Carly shares some surprising use cases for artificial intelligence, and Boris imagines a world without significance testing.
… Continue readingLooker NYC Meetup
Like any company, Automattic is constantly on a journey to get better: sometimes we have the good fortune of finding improvement in leaps and bounds, but most of the time, we move slowly, we make small changes, finding iterative wins and moving down the to-do list. I think probably this is how most progress happens: … Continue reading Looker NYC Meetup
This Week in Data Reading: Experimentation, Tech and the Humanities, and Eliminating Bias in Testing
This week, Carly, Demet, and Charles bring you some interesting material on tech and the humanities, experimentation culture, and eliminating bias in testing.
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