Four short links: 4 March 2016

Snapchat's Business, Tracking Voters, Testing for Discriminatory Associations, and Assessing Impact

  1. How Snapchat Built a Business by Confusing Olds (Bloomberg) — Advertisers don’t have a lot of good options to reach under-30s. The audiences of CBS, NBC, and ABC are, on average, in their 50s. Cable networks such as CNN and Fox News have it worse, with median viewerships near or past Social Security age. MTV’s median viewers are in their early 20s, but ratings have dropped in recent years. Marketers are understandably anxious, and Spiegel and his deputies have capitalized on those anxieties brilliantly by charging hundreds of thousands of dollars when Snapchat introduces an ad product.
  2. Tracking VotersOn the night of the Iowa caucus, Dstillery flagged all the [ad network-mediated ad] auctions that took place on phones in latitudes and longitudes near caucus locations. It wound up spotting 16,000 devices on caucus night, as those people had granted location privileges to the apps or devices that served them ads. It captured those mobile ID’s and then looked up the characteristics associated with those IDs in order to make observations about the kind of people that went to Republican caucus locations (young parents) versus Democrat caucus locations. It drilled down further (e.g., ‘people who like NASCAR voted for Trump and Clinton’) by looking at which candidate won at a particular caucus location.
  3. Discovering Unwarranted Associations in Data-Driven Applications with the FairTest Testing Toolkit (arXiv) — We describe FairTest, a testing toolkit that detects unwarranted associations between an algorithm’s outputs (e.g., prices or labels) and user subpopulations, including sensitive groups (e.g., defined by race or gender). FairTest reports statistically significant associations to programmers as association bugs, ranked by their strength and likelihood of being unintentional, rather than necessary effects. See also slides from PrivacyCon. Source code not yet released.
  4. Inferring Causal Impact Using Bayesian Structural Time-Series Models (Adrian Colyer) — understanding the impact of an intervention by building a predictive model of what would have happened without the intervention, then diffing reality to that model.
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