Week Ending 04.14.19

 

RESEARCH WATCH: 04.14.19

 
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Over the past week, 293 new papers were published in "Computer Science".

  • The paper discussed most in the news over the past week was "Discrimination through optimization: How Facebooks ad delivery can lead to skewed outcomes" by Muhammad Ali et al (Apr 2019), which was referenced 47 times, including in the article Facebook’s Ad System Might Be Hard-Coded for Discrimination in Wired News. The paper author, Alan Mislove (Computer science professor at Northeastern University), was quoted saying "All advertising is based on auctions all over the web, and I don’t know how you fix that without just saying we don’t have those kinds of ads". The paper also got the most social media traction with 1292 shares. The investigators demonstrate that such skewed delivery occurs on Facebook, due to market and financial optimization effects as well as the platforms own predictions about the relevance of ads to different groups of users. A user, @TimKarr, tweeted "April 4, 2019: Academic paper published analyzing FB's algorithms to find they're built using historically discriminatory data. The algorithms deliver results biased against people based on race & gender, & perpetuate discrimination in advertising".

  • Leading researcher Yoshua Bengio (Université de Montréal) published "Speech Model Pre-training for End-to-End Spoken Language Understanding" @_josh_meyer_ tweeted "Good thread ---> inspirations of a dataset and the models trained on it".

  • The paper shared the most on social media this week is by a team at Google: "Depth from Videos in the Wild: Unsupervised Monocular Depth Learning from Unknown Cameras" by Ariel Gordon et al (Apr 2019) with 361 shares. @gersart (Gerry Straathof) tweeted "This was what I was trying to do with a video shot out a window while driving. I wanted to find some way of understanding which elements could be removed, though, so I could make a dependable slitscan of the highway view. In mine, fenceposts and power poles were the issue".

Over the past week, 71 new papers were published in "Computer Science - Artificial Intelligence".

This week was very active for "Computer Science - Computer Vision and Pattern Recognition", with 310 new papers.

Over the past week, 21 new papers were published in "Computer Science - Computers and Society".

  • The paper discussed most in the news over the past week was "Discrimination through optimization: How Facebooks ad delivery can lead to skewed outcomes" by Muhammad Ali et al (Apr 2019), which was referenced 47 times, including in the article Facebook’s Ad System Might Be Hard-Coded for Discrimination in Wired News. The paper author, Alan Mislove (Computer science professor at Northeastern University), was quoted saying "All advertising is based on auctions all over the web, and I don’t know how you fix that without just saying we don’t have those kinds of ads". The paper also got the most social media traction with 1292 shares. The researchers demonstrate that such skewed delivery occurs on Facebook, due to market and financial optimization effects as well as the platforms own predictions about the relevance of ads to different groups of users. A Twitter user, @TimKarr, said "April 4, 2019: Academic paper published analyzing FB's algorithms to find they're built using historically discriminatory data. The algorithms deliver results biased against people based on race & gender, & perpetuate discrimination in advertising".

This week was active for "Computer Science - Human-Computer Interaction", with 32 new papers.

This week was active for "Computer Science - Learning", with 217 new papers.

  • The paper discussed most in the news over the past week was "Predictive Inequity in Object Detection" by Benjamin Wilson et al (Feb 2019), which was referenced 154 times, including in the article Apple’s Siri is as important as iPhone or the Mac in TechnologyNews.win. The paper author, Jamie Morgenstern (University of Pennsylvania), was quoted saying "The main takeaway from our work is that vision systems that share common structures to the ones we tested should be looked at more closely". The paper got social media traction with 169 shares. The researchers investigate whether state - of - the - art object detection systems have equitable predictive performance on pedestrians with different skin tones. On Twitter, @defcon_5 posted "This is what systemic racism looks like.🚨🚨🚨 Black people may be at a greater risk of getting hit by self-driving cars because today's object-detection models exhibit higher precision on lighter skin tones #whitesupremacy".

  • Leading researcher Yoshua Bengio (Université de Montréal) published "Speech Model Pre-training for End-to-End Spoken Language Understanding" @_josh_meyer_ tweeted "Good thread ---> inspirations of a dataset and the models trained on it".

  • The paper shared the most on social media this week is by a team at Google: "Depth from Videos in the Wild: Unsupervised Monocular Depth Learning from Unknown Cameras" by Ariel Gordon et al (Apr 2019) with 361 shares. @gersart (Gerry Straathof) tweeted "This was what I was trying to do with a video shot out a window while driving. I wanted to find some way of understanding which elements could be removed, though, so I could make a dependable slitscan of the highway view. In mine, fenceposts and power poles were the issue".

Over the past week, 13 new papers were published in "Computer Science - Multiagent Systems".

Over the past week, 31 new papers were published in "Computer Science - Neural and Evolutionary Computing".

This week was active for "Computer Science - Robotics", with 48 new papers.


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