Why Sprint is paying a record $330 million settlement in New York


Sprint shares were higher on Wednesday following news that the company is preparing to mortgage its wireless airwaves. ALBANY – Sprint has agreed to pay a $330 million settlement after the company skirted New York tax law for nearly a decade, New York's attorney general announced Friday. The record-breaking settlement came in the wake of a false claims lawsuit filed by Attorney General Barbara Underwood alleging the cellular provider failed to collect and remit over $100 million in state and local taxes on flat-rate calling plans. The $330 million settlement is the largest recovery by a single state in a false claims lawsuit, according to the attorney general's office. "Sprint knew exactly how New York sales tax law applied to its plans – yet for years the company flagrantly broke the law, cheating the state and its localities out of tax dollars that should have been invested in our communities," Underwood said in a statement.

A Generalized Fellegi-Sunter Framework for Multiple Record Linkage With Application to Homicide Record Systems

arXiv.org Machine Learning

We present a probabilistic method for linking multiple datafiles. This task is not trivial in the absence of unique identifiers for the individuals recorded. This is a common scenario when linking census data to coverage measurement surveys for census coverage evaluation, and in general when multiple record-systems need to be integrated for posterior analysis. Our method generalizes the Fellegi-Sunter theory for linking records from two datafiles and its modern implementations. The multiple record linkage goal is to classify the record K-tuples coming from K datafiles according to the different matching patterns. Our method incorporates the transitivity of agreement in the computation of the data used to model matching probabilities. We use a mixture model to fit matching probabilities via maximum likelihood using the EM algorithm. We present a method to decide the record K-tuples membership to the subsets of matching patterns and we prove its optimality. We apply our method to the integration of three Colombian homicide record systems and we perform a simulation study in order to explore the performance of the method under measurement error and different scenarios. The proposed method works well and opens some directions for future research.

Facebook Faces Class Action Lawsuit Challenging Its Use Of Facial Recognition Data


Cardboard cutouts of Facebook founder and CEO Mark Zuckerberg stand outside the U.S. Capitol in Washington as he testified before a Senate panel last week. Cardboard cutouts of Facebook founder and CEO Mark Zuckerberg stand outside the U.S. Capitol in Washington as he testified before a Senate panel last week. A federal judge in California has ruled that Facebook can be sued in a class-action lawsuit brought by users in Illinois who say the social network improperly used facial recognition technology on their uploaded photographs. The plaintiffs are three Illinois Facebook users who sued under a state law that says a private entity such as Facebook can't collect and store a person's biometric facial information without their written consent. The law, known as the Biometric Information Privacy Act, also says that information that uniquely identifies an individual is, in essence, their property.

The Bayesian Echo Chamber: Modeling Social Influence via Linguistic Accommodation

arXiv.org Machine Learning

We present the Bayesian Echo Chamber, a new Bayesian generative model for social interaction data. By modeling the evolution of people's language usage over time, this model discovers latent influence relationships between them. Unlike previous work on inferring influence, which has primarily focused on simple temporal dynamics evidenced via turn-taking behavior, our model captures more nuanced influence relationships, evidenced via linguistic accommodation patterns in interaction content. The model, which is based on a discrete analog of the multivariate Hawkes process, permits a fully Bayesian inference algorithm. We validate our model's ability to discover latent influence patterns using transcripts of arguments heard by the US Supreme Court and the movie "12 Angry Men." We showcase our model's capabilities by using it to infer latent influence patterns from Federal Open Market Committee meeting transcripts, demonstrating state-of-the-art performance at uncovering social dynamics in group discussions.

Facial Recognition Surveillance Now at a Privacy Tipping Point


Much more rapidly than anyone originally thought possible, facial recognition technology has become part of the cultural mainstream. Facebook, for example, now uses AI-powered facial recognition software as part of its core social networking platform to identify people, while law enforcement agencies around the world have experimented with facial recognition surveillance cameras to reduce crime and improve public safety. But now it looks like society is finally starting to wake up to the immense privacy implications of real-time facial recognition surveillance. For example, San Francisco is now considering an outright ban on facial recognition surveillance. If pending legislation known as "Stop Secret Surveillance" passes, this would make San Francisco the first city ever to ban (and not just regulate) facial recognition technology.