02/19/2020 – The Work of Sustaining Order in Wikipedia: The Banning of a Vandal – Sushmethaa Muhundan

February 18, 2020 Sushmethaa Muhundan Leave a comment

The paper takes about the counter-vandalism process in Wikipedia focussing on both the human efforts as well as the silent non-human efforts put in. Fully-automated anti-vandalism bots are a key part of this process and play a critical role in managing the content on Wikipedia. The actors involved range from being fully autonomous software to semi-automated programs to user interfaces used by humans. A case study is presented which is an account of detecting and banning a vandal. This aims to highlight the importance and impact of bots and assisted editing programs. Vandalism-reverting software use queuing algorithms teamed with a ranking mechanism based on vandalism-identification algorithms. The queuing algorithm takes into account multiple factors like the kind of user who made the edit, revert history of the user as well as the type of edit made. The software proves to be extremely effective in presenting prospective vandals to the reviewers. User talk pages are forums utilized to take action after an offense has been reverted. This largely invisible infrastructure has been extremely critical in insulating Wikipedia from vandals, spammers, and other malevolent editors.

I feel that the case study presented helps understand the internal working of vandalism-reverting software and it is a great example of handling a problem by leveraging the complementary strengths of AI and humans via technology. It is interesting to note that the cognitive work of identifying a vandal is distributed across a heterogeneous network and is unified using technology! This lends speed and efficiency and makes the entire system robust. I found it particularly interesting that ClueBot, after identifying a vandal, immediately reverted the edit within seconds. This edit did not have a wait in a queue for a human or a non-human bot to review but was resolved immediately using a bot.

A pivotal feature of this ecosystem that I found very fascinating was the fact that domain expertise or skill is not required to handle such vandal cases. The only expertise required of vandal fighters is in the use of the assisted editing tools themselves, and the kinds of commonsensical judgment those tools enable. This widens the eligibility criteria for prospective workers since specialized domain experts are not required.

The queuing algorithm takes into account multiple factors like the kind of user who made the edit, revert history of the user as well as the type of edit made. Apart from the factors mentioned in the paper, what other factors can be incorporated into the queuing algorithm to improve its efficiency?
What are some innovative ideas that can be used to further minimize the turnaround reaction time to a vandal in this ecosystem?
What other tools can be used to leverage the complementary strengths of humans and AI using technology to detect and handle vandals in an efficient manner?

02/18/20 – Akshita Jha – The Work of Sustaining Order in Wikipedia: The Banning of a Vandal

February 18, 2020February 18, 2020 Akshita Jha Leave a comment

Summary:
“The Work of Sustaining Order in Wikipedia: The Banning of a Vandal” by Geiger and Ribes examines the role of software tools in the English Wikipedia, specifically involving autonomous and assisted editing. Wikipedia is a “free online encyclopedia, created and edited by volunteers around the world and hosted by the Wikimedia Foundation.” Bots are “fully-automated software agents that perform algorithmically-defined tasks involved with editing, maintenance, and administration in Wikipedia.” Different bots have different functions which can range from simple tasks like correcting grammatical errors to more complicated tasks like detecting personal insults. The authors present a detailed case study: “The Banning of a Vandal”. The authors talk about “Huggle”, that is the most widely used editing tool across Wikipedia that queues all the edits. The user then has the option to perform a variety of actions like ‘revert’, ‘warn’, etc. on each of the edits that is displayed. The user does not have the option to select which edit he wants to make changes to. An anonymous user had been vandalizing multiple Wikipedia pages and was not discouraged by the warning and comments given by the moderators. Eventually, this rogue user was blocked by making use of the network of moderators or vandal fighters and the bots but it was more cumbersome than expected. In addition to the quantitative and the qualitative studies, the research also demonstrated the importance of trace ethnography for studying such sociotechnical systems.

Reflections:
This is an interesting work. It was particularly insightful as I was unaware of the role of multiple bots in Wikipedia editing. Bots and humans working cohesively have helped make Wikipedia the widely used resource it currently is. Making Wikipedia a free resource that allows editing by volunteers comes with a cost. This paper helped highlight the limitations of the Wikipedia bots and how a significant amount of effort is needed from multiple moderators to ban a vandal from Wikipedia. Each moderator makes a local judgement but the Wikipedia talk pages help keep a record of all the warnings against a particular user. Certain kinds of vandalism, like inserting obscenities and profanities, are easy to detect. However, if a vandal deletes an important section from the Wikipedia page, that might involve significant cognitive effort from moderators to identify and rectify. An interesting question is how would Wikipedia be effected, if it made use of a completely automated bot instead of the hybrid system it currently uses. Would the bots be able to determine the significance of an edit or a change? How would that change the moderators behaviors and actions? Since, automated tools help determine the kind of social activities that are possible on Wikipedia, will having a completely automated bot significantly alter Wikipedia and the user involvement? It would also be interesting to see if we can use trace ethnography to study Reddit, which is another big sociotechnical system.

Questions:
1. How did such a network come into place?
2. Do you think certain kinds of Wikipedia pages are more susceptible than others to vandalism?
3. Will completely automated bots help?
4. Can we conduct such a case study for Reddit? Why? Why not?

02/19/2020 – Nurendra Choudhary – Updates in Human-AI Teams

February 18, 2020February 18, 2020 Nurendra Choudhary Leave a comment

Summary

In this paper, the authors study the role of studying human-AI team performance in contrast to their individual performance and explain its necessity. They explain the importance of human inference of AI tools. Humans develop mental models of AI’s performance. Advances made in AI’s algorithm only evaluate the improvement in the prediction. However, the improvements cause behavioral changes in AI that do not fit the human’s mental models and reduce the overall performance of their team. To alleviate this, the authors propose a new logarithmic loss that considers the compatibility between human mental models and AI models for making updates to the AI model.

The authors construct user studies to show the development of human mental models across different conditions. Additionally, they illustrate the degradation in overall team performance with improvement in AI’s prediction. Furthermore, they show the addition of the additional loss increases the overall team performance of the AI model while increasing AI’s prediction efficiency.

Reflection

Humans and AI form formidable teams in multiple environments and I think such a study as a necessity for further development of AI. Most state-of-the-art AI systems are not independently useful in real-world and rely on human intervention from time-to-time (as discussed in previous classes). Till a point of time where this situation exists, we cannot improve AI independently and have to consider the humans involved in the task. I believe the evaluation metrics currently used in AI research are completely focussed on the AI’s prediction. However, this needs to change and the paper is a great primary step in the direction. I believe we should construct more such evaluation metrics for various other AI tasks. But, if we develop our evaluation metrics around human-AI teams, we take the risk of potentially making AI systems reliant on human input. Hence, there is a possibility that AI systems never independently solve our problems. I believe the solution lies in interpretability.

Current AI techniques rely on statistical spaces that are not human-interpretable. Focusing on making these spaces interpretable allows human comprehensibility. Interpretable AI is a rising research topic in several subareas of AI and I believe it can solve the current dilemma. We can develop AI systems independently and all the updates will be comprehensible by humans and they can accordingly update their mental models. But, we interpretability is not a trivial subject. Recent work has only shown incremental progress and the work still compromises on prediction ability for interpretability. The effectiveness of AI is observed because of their ability to recognize patterns in dimensions incomprehensible to human beings. The current paper and interpretability both require human understanding of the model and I am not sure if this is possible.

Questions

Can we have evaluation metrics for other tasks based on this? Will it involve human evaluation? If so, how do we maintain comparative fairness across such metrics?
If we continue evaluating Human-AI teams together, will we ever be able to develop completely independent AI systems?
Should we focus on making the AI systems interpretable or their performance?
Is interpretable AI the future for real-world systems? Think about, for every search query made, the user is able to see all their features that aids the system’s decision making process.

Word Count: 545

02/18/20 – Akshita Jha – Human-Machine Collaboration for Content Regulation: The Case of Reddit Automoderator

February 18, 2020February 18, 2020 Akshita Jha 1 Comment

Summary:
“Human-Machine Collaboration for Content Regulation: The Case of Reddit Automoderator” by Jhaver et al. talks about the popular social media website Reddit and the unusual unpaid human moderators and automated moderator collaboration. Reddit moderators make use of the heavily configurable automated program called, ‘Automoderator’ to help make decisions about the content that should be removed from the website. The authors interview 16 Reddit moderators to understand how they benefit from the moderating tool, ‘Automod’ and how they adapt and configure it to reflect the subreddit’s policies to help them moderate the subreddit effectively. The authors also offer valuable insights that may benefit the creators of the platforms, designers of automated regulation systems, scholars of platform governance, and content moderators. The authors conclude by pointing out that the moderation system in reddit is a collaborative effort between humans as well as the automated systems. This hybrid system works but there is definitely a scope for improvement in the development and deployment of these tools.

Reflections:
Online platforms can be a boon or a bane depending on how people choose to engage with it. Regulation might seem necessary to ensure that low quality posts (these posts can be treated as noise) do not drown out informative and worthy posts on the site. However, this is a challenging task. Deciding whether a post is appropriate for the subreddit puts a lot of responsibility on the moderator. In some cases the moderator might be a bot, ‘Automod’ and in other cases the platform relies on paid or unpaid volunteers. Reddit moderators are unpaid. The authors in this work analysed 5 different subreddits: ‘r/photoshopbattles’, ‘r/space’, ‘r/oddlysatisfying’, ‘r/explainlikeiamfive’ and ‘r/politics’. It’s interesting that some reddit moderators prefer to implement moderation bots from scratch while others make use of tools made by others. It’s intriguing how making use of tools made by others forms a sense of community of moderators within the bigger community of reddit. Most redditors use ‘Automod’ which was initially created by ‘Chad Birch’ using the Reddit API in January 2012. However, a major drawback of this study is that all the moderators that the authors interviewed were males. It would be helpful to get the perspective of female moderators, if there are any, since the user base for Reddit is disproportionately male. I feel the authors should have selected ‘r/AskHistorians’ as one of the subreddits for analysis since it’s widely known to be highly moderated and content driven. It would have also been interesting to deep dive into the comments that ‘Automod’ marked as offensive but were not. This would help improve the performance of the moderator while informing us of its limitations. One might also need to wonder about the consequences if the subreddit community grows larger. There might be a need to reflect on the existing tools and their scale.

Questions:
1. Do you agree that social media content should be moderated?
2. What about the mental health of the moderators?
3. What kind of resources should be make available to the moderators since they are dealing with sensitive content all the time?

02/19/2020 – Nurendra Choudhary – The Work of Sustaining Order in Wikipedia

February 18, 2020February 18, 2020 Nurendra Choudhary Leave a comment

Summary

In this paper, the authors discuss the problem of maintaining order in open-edit information corpora, specifically Wikipedia here. They start with explaining the near-immunity of Wikipedia to vandalism that is achieved through a synergy between humans and AI. Wikipedia is open to all editors and the team behind the system is highly technical. However, the authors study on its immunity dependence on the community’s social behavior. They show that vandal fighters are networks of people that identify the vandals based on a network of behavior. They are supported by AI tools but banning a vandal is yet not a completely automated process. The process of banning a user is a requires individual editor judgements at a local level and a collective decision at a global level. This creates a heterogeneous network and emphasizes on decision corroboration by different actors.

As given in the conclusion, “this research has shown the salience of trace ethnography for the study of distributed sociotechnical systems”. Here, trace ethnography combines the ability of editors with data across their actions to analyze vandalism in Wikipedia.

Reflection

It is interesting to see that Wikipedia’s vandal fighters include such a seamless cooperation between humans and AI. I think this is another case where AI can leverage human networks for support. The more significant part is that the tasks are not trivial and require human specialization and not just plain effort. Also, collaboration is a significant part of AI’s capability. Human editors analyze the articles in the local context. AI can efficiently combine the results and target the source of these errors by building a heterogeneous network of such decisions. Further, human beings analyze these networks to ban vandals. This methodology applies the most important abilities of both humans and bots. The collaboration involves the best attributes of humans, i.e; judgement and of AI, i.e; pattern recognition. Also, it effectively utilizes this collaboration against vandals who are independent or small networks of mal-practitioners who do not have access to the bigger picture.

The methodology utilizes distributed work patterns for accomplishing different tasks of editing and moral agency. Distributing the work enables involvement of human beings on trivial tasks. However, combining the results to attain logical inferences is not humanly possible. This is because the vast amount of data is incomprehensible to humans. But, humans have the ability to develop algorithms that the machine can apply at a larger-scale to get such inferences. However, the inferences do not have a fixed structure and require human intelligence to retrieve desired actions against vandalism. Given that, most of the cases of such vandalism are by independent humans, a collaborative effort by AI can greatly turn the odds for vandal fighters. This is because AI aids humans by utilizing the bigger picture incomprehensible to just humans.

Questions

If vandals have access to the network, will they be able to destroy the synergy?
If there’s more motivation like political or monetary gain, will it give rise to a kind-of mafia network of such mal-practitioners? Will the current methodology still be valid in such a case?
Do we need a trust-worthiness metric for each Wikipedia page? Can the page be utilized as reference for absolute information?
Wikipedia is a great example of crowd-sourcing and this is a great article for crowd-control on these networks. Can this be extended to other crowd-sourcing softwares like Amazon MT or information blogs?