Responsible AI for Inclusive, Democratic Societies: A cross-disciplinary approach to detecting and countering abusive language online

有毒和辱骂性的语言威胁着公共对话和民主的完整性。辱骂性语言，如辱骂、诽谤、种族主义、极端主义、粗鲁、挑衅和伪装，通常被认为是冒犯性和侮辱性的，与政治两极分化和公民冷漠、恐怖主义和激进化的兴起以及网络欺凌有关。作为回应，世界各国政府颁布了强有力的法律，禁止导致针对特定群体的仇恨、暴力和刑事犯罪的辱骂性语言。这包括节制的法律的义务（即，这些措施包括及时检测、评估和可能删除或删除含有仇恨或非法语言的在线材料;社交媒体公司在其使用条款中采取了更严格的规定。然而，在过去几年中，这种滥用网络行为大幅增加，使政府、社交媒体平台和个人难以应对后果。 负责任地（即有效、公平和公正地）节制辱骂性语言带来了重大的实际、文化和法律的挑战。虽然目前的立法和公众的愤怒需要迅速作出反应，但我们还没有有效的人力或技术程序来满足这一需求。人工内容审核员的广泛部署在许多层面上都是昂贵和不足的：工作的性质在心理上具有挑战性，并且重要的努力落后于每秒发布的大量数据。与此同时，为解决辱骂性语言而实施的人工智能（AI）解决方案引发了人们对影响基本人权（例如表达自由、隐私和缺乏企业透明度）的自动化流程的担忧。很能说明问题的是，审查互联网内容的第一步行动集中在LGBTQ社区和艾滋病活动所使用的术语上。因此，内容审核被行业和媒体称为“十亿美元的问题”也就不足为奇了。“因此，该项目解决了一个首要问题：如何通过将表达自由、对人权的承诺和多元文化参与纳入防止虐待的保护中，更好地部署人工智能以促进民主？我们的项目通过一种新的方法来检测和打击滥用语言的困难和紧迫的问题，这种方法将计算机科学与社会科学和人文科学的专业知识和方法相结合。我们关注两个因毒性而臭名昭著的选区：政治家和游戏玩家。政治家，由于他们的公众角色，经常受到辱骂。在线游戏和游戏空间被认定为极端政治观点的私人“招募网站”，并与离线暴力袭击有关。具体来说，我们的团队将量化当前内容审核系统中嵌入的偏见，这些系统使用对辱骂性语言的严格定义或确定，可能会矛盾地产生基于身份的新形式的歧视或偏见，包括性别，性别，种族，文化，宗教，政治派别或其他。我们将通过生产更多的上下文感知，动态检测系统来抵消这些影响。此外，我们将通过将这些开源工具嵌入到民主反言论和基于社区的护理和响应战略中来增强用户的能力。项目成果将通过开放获取白色文件、出版物和其他在线材料与政策、学术、行业、社区和公共利益攸关方广泛分享。该项目将吸引和培训下一代跨学科的专家，这对负责任的人工智能的发展至关重要。 由于其重点是强大的人工智能方法，以有效和合法的方式解决在线滥用问题，以促进民主社会的活力，这项研究对加拿大和英国具有广泛的影响和相关性。

Toxic and abusive language threaten the integrity of public dialogue and democracy. Abusive language, such as taunts, slurs, racism, extremism, crudeness, provocation and disguise are generally considered offensive and insulting, has been linked to political polarisation and citizen apathy; the rise of terrorism and radicalisation; and cyberbullying. In response, governments worldwide have enacted strong laws against abusive language that leads to hatred, violence and criminal offences against a particular group. This includes legal obligations to moderate (i.e., detection, evaluation, and potential removal or deletion) online material containing hateful or illegal language in a timely manner; and social media companies have adopted even more stringent regulations in their terms of use. The last few years, however, have seen a significant surge in such abusive online behaviour, leaving governments, social media platforms, and individuals struggling to deal with the consequences. The responsible (i.e. effective, fair and unbiased) moderation of abusive language carries significant practical, cultural, and legal challenges. While current legislation and public outrage demand a swift response, we do not yet have effective human or technical processes that can address this need. The widespread deployment of human content moderators is costly and inadequate on many levels: the nature of the work is psychologically challenging, and significant efforts lag behind the deluge of data posted every second. At the same time, Artificial Intelligence (AI) solutions implemented to address abusive language have raised concerns about automated processes that affect fundamental human rights, such as freedom of expression, privacy and lack of corporate transparency. Tellingly, the first moves to censor Internet content focused on terms used by the LGBTQ community and AIDS activism. It is no surprise then that content moderation has been dubbed by industry and media as a "billion dollar problem." Thus, this project addresses the overarching question: how can AI be better deployed to foster democracy by integrating freedom of expression, commitments to human rights and multicultural participation in the protection against abuse? Our project takes on the difficult and urgent issue of detecting and countering abusive language through a novel approach to AI-enhanced moderation that combines computer science with social science and humanities expertise and methods. We focus on two constituencies infamous for toxicity: politicians and gamers. Politicians, because of their public role, are regularly subjected to abusive language. Online gaming and gaming spaces have been identified as private "recruitment sites"' for extreme political views and linked to off-line violent attacks. Specifically, our team will quantify the bias embedded within current content moderation systems that use rigid definitions or determinations of abusive language that may paradoxically create new forms of discrimination or bias based on identity, including sex, gender, ethnicity, culture, religion, political affiliation or other. We will offset these effects by producing more context-aware, dynamic systems of detection. Further, we will empower users by embedding these open source tools within strategies of democratic counter-speech and community-based care and response. Project results will be shared broadly through open access white papers, publications and other online materials with policy, academic, industry, community and public stakeholders. This project will engage and train the next generation of interdisciplinary scholars-crucial to the development of responsible AI. With its focus on robust AI methods for tackling online abuse in an effective and legally-compliant manner to the vigour of democratic societies, this research has wide-ranging implications and relevance for Canada and the UK.