5,346 research outputs found
Uncovering the wider structure of extreme right communities spanning popular online networks
AbstractRecent years have seen increased interest in the online presence of extreme right groups. Although originally composed of dedicated websites, the online extreme right milieu now spans multiple networks, including popular social media platforms such as Twitter, Facebook and YouTube. Ideally therefore, any contemporary analysis of online extreme right activity requires the consideration of multiple data sources, rather than being restricted to a single platform.We investigate the potential for Twitter to act as one possible gateway to communities within the wider online network of the extreme right, given its facility for the dissemination of content. A strategy for representing heterogeneous network data with a single homogeneous network for the purpose of community detection is presented, where these inherently dynamic communities are tracked over time. We use this strategy to discover and analyze persistent English and German language extreme right communities.Authored by Derek O’Callaghan, Derek Greene, Maura Conway, Joe Carthy and Padraig Cunningham
Distill-and-Compare: Auditing Black-Box Models Using Transparent Model Distillation
Black-box risk scoring models permeate our lives, yet are typically
proprietary or opaque. We propose Distill-and-Compare, a model distillation and
comparison approach to audit such models. To gain insight into black-box
models, we treat them as teachers, training transparent student models to mimic
the risk scores assigned by black-box models. We compare the student model
trained with distillation to a second un-distilled transparent model trained on
ground-truth outcomes, and use differences between the two models to gain
insight into the black-box model. Our approach can be applied in a realistic
setting, without probing the black-box model API. We demonstrate the approach
on four public data sets: COMPAS, Stop-and-Frisk, Chicago Police, and Lending
Club. We also propose a statistical test to determine if a data set is missing
key features used to train the black-box model. Our test finds that the
ProPublica data is likely missing key feature(s) used in COMPAS.Comment: Camera-ready version for AAAI/ACM AIES 2018. Data and pseudocode at
https://github.com/shftan/auditblackbox. Previously titled "Detecting Bias in
Black-Box Models Using Transparent Model Distillation". A short version was
presented at NIPS 2017 Symposium on Interpretable Machine Learnin
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