{"title":"Understanding content moderation systems: new methods to understand internet governance at scale, over time, and across platforms","authors":"Nicolas Suzor","doi":"10.4337/9781788977456.00013","DOIUrl":"https://doi.org/10.4337/9781788977456.00013","url":null,"abstract":"There is increasing global concern about how the decisions of internet and telecommunications companies impact on human rights. As a key priority, if we care about how intermediaries govern their networks, we need to be able to measure their impact on human rights and work out how we can use this information to help protect them from external pressures that would limit our freedom and how we can hold them accountable for decisions they make on their own initiatives. Understanding the effects that technology companies have on our lives and identifying potential biases and other problems requires careful attention to the inputs and outputs of these systems and how they actually work in different social contexts. Analysis of this type will require large-scale access to data on individual decisions as well as deep qualitative analyses of the automated and human processes that platforms deploy internally. This chapter presents the ‘Platform Governance Observatory’: new research infrastructure that was designed to enable the systematic study of content moderation practices on major social media platforms. The core research question that made this infrastructure necessary was to understand, at a large scale, what social media content is moderated and by whom, how this compares between platforms, and how this changes over time. So far, this infrastructure enables the analysis of content removals of public posts on YouTube, Twitter, and Instagram. The infrastructure I created to support this exploration proceeds on a general design principle of building a random sample of public social media content, and then tests the availability of that sample at a later date.","PeriodicalId":145445,"journal":{"name":"Computational Legal Studies","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130691505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Is legal cognition computational? (When will DeepVehicle replace Judge Hercules?)","authors":"Paul Gowder","doi":"10.31228/osf.io/gk2ms","DOIUrl":"https://doi.org/10.31228/osf.io/gk2ms","url":null,"abstract":"Could we insert machine learning into the adjudicative process? This chapter considers the extent of the isomorphism between common-law reasoning from prior cases and machine learning reasoning from prior observations, as well as the normative considerations governing any such use. It ultimately concludes that we could use machine learning models to assist judges in reasoning about some questions of law, but only in the context of an ordinary legal process regulating both the use and the forms of such models. Ultimately, machine learning would be less likely to replace judicial reasoning and legal argument than to move it around.","PeriodicalId":145445,"journal":{"name":"Computational Legal Studies","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125517424","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}