Finding (and Studying) Wikipedia Trolls

Finding (and Studying) Wikipedia Trolls

You may be shocked to hear this, but sometimes, people on the internet can be mean. For some of us this is just a minor annoyance, but if you're a maintainer or contributor of a large project like Wikipedia, abusive users can be a huge problem. Fighting the problem starts with understanding it, and understanding it starts with measuring it; the thing is, for a huge website like Wikipedia, there can be millions of edits and comments where abuse might happen, so measurement isn't a simple task. That's where machine learning comes in: by building an "abuse classifier," and pointing it at the Wikipedia edit corpus, researchers at Jigsaw and the Wikimedia foundation are for the first time able to estimate abuse rates and curate a dataset of abusive incidents. Then those researchers, and others, can use that dataset to study the pathologies and effects of Wikipedia trolls.

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Episoder(317)

Distillation, or, How to Steal a Model

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27 Jul 0s

Invisible LLM Failures and AI Fluency with Chris Potts (Stanford)

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20 Jul 41min

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Summer break: back soon by Katie Malone

6 Jul 36s

Interviewing the Linear Digressions Agents (The Agents Season, Episode 11)

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28 Jun 37min

Agent Economics (The Agents Season, Episode 10)

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22 Jun 24min

Agent Trust, Oversight and Control (The Agents Season, Episode 9)

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Capabilities get all the attention when it comes to AI agents — but what happens when a highly capable agent makes a bad decision in the real world? Trust, oversight, and control are the unglamorous b...

15 Jun 25min

Many Agents, Many Problems (The Agents Season, Episode 8)

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8 Jun 28min

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