Four short links: 28 March 2019

Data-Oriented Design, Time Zone Hell, Music Algorithms, and Fairness in ML

By Nat Torkington
March 28, 2019
Four short links
  1. Data Oriented DesignA curated list of data-oriented design resources.
  2. Storing UTC is Not a Silver Bullet — time zones will drive you to drink.
  3. Learn faster. Dig deeper. See farther.

    Join the O'Reilly online learning platform. Get a free trial today and find answers on the fly, or master something new and useful.

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  4. Warner Music Signed an Algorithm to a Record Deal (Verge) — Although Endel signed a deal with Warner, the deal is crucially not for “an algorithm,” and Warner is not in control of Endel’s product. The label approached Endel with a distribution deal and Endel used its algorithm to create 600 short tracks on 20 albums that were then put on streaming services, returning a 50/50 royalty split to Endel. Unlike a typical major label record deal, Endel didn’t get any advance money paid upfront, and it retained ownership of the master recordings.
  5. 50 Years of Unfairness: Lessons for Machine Learning We trace how the notion of fairness has been defined within the testing communities of education and hiring over the past half century, exploring the cultural and social context in which different fairness definitions have emerged. In some cases, earlier definitions of fairness are similar or identical to definitions of fairness in current machine learning research, and foreshadow current formal work. In other cases, insights into what fairness means and how to measure it have largely gone overlooked. We compare past and current notions of fairness along several dimensions, including the fairness criteria, the focus of the criteria (e.g., a test, a model, or its use), the relationship of fairness to individuals, groups, and subgroups, and the mathematical method for measuring fairness (e.g., classification, regression). This work points the way toward future research and measurement of (un)fairness that builds from our modern understanding of fairness while incorporating insights from the past.
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