2/19 – Dylan Finch – The Work of Sustaining Order in Wikipedia: The Banning of a Vandal

Word count: 565

Summary of the Reading

This paper analyzes the use of autonomous technologies that are used on Wikipedia. These technologies help to keep the peace on the large platform, helping to flag malicious users and revert inaccurate and spammy changes so that Wikipedia stays accurate and up to date. Many people may think that humans play the major role in policing the platform, but machines and algorithms also play a very large part, aiding the humans to deal with the large amount of edits.

Some tools are completely automated and can prevent vandalism with no human input. Other tools give human contributors tips to help them spot and fight vandalism. Humans work together with the automated systems and each other to edit the site and keep the pages vandal free. The way in which all of the editors edit together, even though they are not physically together or connected as a team, is an impressive feat of human and AI interaction.

Reflections and Connections

To start, I think that Wikipedia is such and interesting thing to examine for a paper like this. While many organizations have a similar structure, I think that WIkipedia is unique and interesting to study because it is so large, so distributed, and so widely used. It can be hard enough to get a small team of people to work together on documentation. At Wikipedia’s size the complexities of making it all work must be unimaginable. It is so interesting to find out how machines and humans work together at that scale to keep the site running smoothly. The ideas and analysis seen here can easily be applied to smaller systems that are trying to accomplish the same thing.

I also think that this article serves as a great reminder of the power of AI. The fact that AI is able to do some much to help editors keep the site running smoothly even with all of the complexities of the site is amazing and it shows just how much power AI can have when applied to the right situation. A lot of the work done on Wikipedia is not hard work. The article mentions some of the things that bots do, like importing data and fixing grammatical mistakes. These things are incredibly tedious for humans to do and yet they are perfect work for machines. They can do this work almost instantly while it may take a human an hour. This not only serves as a great reminder of the power of AI’s and humans complimenting each other’s abilities, but it also shows what the power of what the internet can do. Something like this never would have been possible before in the history of human civilization. The mere fact that we can do something like this now speaks to the amazing power of the current age. 

Questions

  1. Does this research have applications elsewhere? What would be the best place to apply this analysis?
  2. Could this process ever be done with no human input whatsoever? Could Wikipedia one day be completely self sufficient?
  3. This article talks a lot about how the bots of Wikipedia are becoming more and more important, compared to the policies and social interactions between editors. Is this happening elsewhere? Are there bots other places that we might not see and might not notice, even though they are doing a larger and larger share of the work?

2 thoughts on “2/19 – Dylan Finch – The Work of Sustaining Order in Wikipedia: The Banning of a Vandal

  1. 3. I kind of think that is the main goal. Humans have expertise but the end-goal should be make the AI take most of the load. AI is supposed to learn and if human actions make them learn, I think in the future they will do most of the work. However, I am not sure if this will potentially end human involvement. I believe it will just increase the amount of expertise humans need to identify vandals. Vandals, on the other hand, are adversaries to the system and in due time, they too shall come up with better vandalism tactics. So, I believe it will just increase complexity of the vandal fighting and it is better that AI is able to keep pace.

  2. 1. Yup, trace ethnography could be used for understanding user actions in collaborative environments like online communities or tools like Google Docs, MS Office, etc,. Also, it pushes us to think about evaluating intelligent agents as an actor in a socio-technical setting rather than in isolation, considering the fact they have wider impact on the overall goal and the other actors involved.

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