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Detecting and Quantifying Malicious Activity with Simulation-based Inference
Andrew Gambardella · Naeemullah Khan · Phil Torr · Atilim Gunes Baydin

@
in Workshop on Socially Responsible Machine Learning »

We propose the use of probabilistic programming techniques to tackle the malicious user identification problem in a recommendation algorithm. Probabilistic programming provides numerous advantages over other techniques, including but not limited to providing a disentangled representation of how malicious users acted under a structured model, as well as allowing for the quantification of damage caused by malicious users. We show experiments in malicious user identification using a model of regular and malicious users interacting with a simple recommendation algorithm, and provide a novel simulation-based measure for quantifying the effects of a user or group of users on its dynamics.

Author Information

Andrew Gambardella (University of Oxford)
Naeemullah Khan (Oxford)
Phil Torr (Oxford)
Atilim Gunes Baydin (University of Oxford)

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