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

负责任的人工智能促进包容性民主社会:检测和反击在线辱骂性语言的跨学科方法

基本信息

  • 批准号:
    ES/T012714/1
  • 负责人:
  • 金额:
    $ 64.75万
  • 依托单位:
  • 依托单位国家:
    英国
  • 项目类别:
    Research Grant
  • 财政年份:
    2020
  • 资助国家:
    英国
  • 起止时间:
    2020 至 无数据
  • 项目状态:
    已结题

项目摘要

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.
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,如嘲弄、诽谤、种族主义、极端主义、粗鲁、挑衅和伪装,通常被认为是冒犯和侮辱,与政治两极分化和公民冷漠有关;恐怖主义和激进主义抬头;和网络欺凌。作为回应,世界各国政府制定了强有力的法律,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。这包括及时调节(即检测、评估和可能删除或删除)含有仇恨或非法语言的在线材料的法律义务;社交媒体公司在使用条款上采取了更为严格的规定。然而,在过去的几年里,这种滥用网络的行为急剧增加,政府、社交媒体平台和个人都在努力应对其后果。对辱骂性语言负责任(即有效、公平和公正)的节制带来了重大的实践、文化和法律挑战。虽然目前的立法和公众的愤怒要求迅速作出反应,但我们还没有有效的人力或技术流程来满足这一需求。广泛部署人工内容审核员成本高昂,而且在很多层面上都不充分:这项工作的性质在心理上具有挑战性,而且每秒发布的海量数据背后存在巨大的努力滞后。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的自动化过程的担忧,例如言论自由、隐私和缺乏公司透明度。值得注意的是,审查互联网内容的第一步集中在LGBTQ社区和艾滋病活动人士使用的术语上。因此,内容审核被业界和媒体称为“十亿美元问题”也就不足为奇了。因此,该项目解决了一个首要问题:如何通过整合言论自由、对人权的承诺和多元文化参与来防止滥用,更好地利用人工智能来促进民主?我们的项目通过一种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的人工智能增强审核的新方法来检测和打击辱骂性语言,这是一个困难而紧迫的问题。我们关注的是两个臭名昭著的群体:政治家和游戏玩家。由于政治家的公共角色,他们经常受到辱骂。网络游戏和游戏空间被认定为极端政治观点的私人“招募网站”,并与线下暴力袭击有关。具体而言,我们的团队将量化当前内容审核系统中嵌入的偏见,这些系统使用严格的定义或确定辱骂性语言,这些语言可能会矛盾地基于身份(包括性别、性别、种族、文化、宗教、政治派别或其他)创造新的歧视或偏见形式。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关怀和应对策略中来增强用户的能力。项目成果将通过开放获取白皮书、出版物和其他在线材料与政策、学术、行业、社区和公众利益相关者广泛分享。该项目将吸引和培养下一代跨学科学者,这对负责任的人工智能的发展至关重要。这项研究的重点是强大的人工智能方法,以有效和合法的方式解决网络滥用问题,以促进民主社会的活力,因此对加拿大和英国具有广泛的影响和相关性。

项目成果

期刊论文数量(10)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Abuse in the time of COVID-19: the effects of Brexit, gender and partisanship
  • DOI:
    10.1108/oir-07-2022-0392
  • 发表时间:
    2024-02-27
  • 期刊:
  • 影响因子:
    3.1
  • 作者:
    Bakir,Mehmet Emin;Farrell,Tracie;Bontcheva,Kalina
  • 通讯作者:
    Bontcheva,Kalina
MP Twitter Engagement and Abuse Post-first COVID-19 Lockdown in the UK: White Paper
英国首次 COVID-19 封锁后 Twitter 参与度和滥用情况:白皮书
  • DOI:
    10.48550/arxiv.2103.02917
  • 发表时间:
    2021
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Farrell T
  • 通讯作者:
    Farrell T
Which politicians receive abuse? Four factors illuminated in the UK general election 2019
哪些政客受到虐待?
  • DOI:
    10.1140/epjds/s13688-020-00236-9
  • 发表时间:
    2020
  • 期刊:
  • 影响因子:
    3.6
  • 作者:
    Gorrell G
  • 通讯作者:
    Gorrell G
Vindication, virtue, and vitriol: A study of online engagement and abuse toward British MPs during the COVID-19 pandemic.
Vindication, Virtue and Vitriol: A study of online engagement and abuse toward British MPs during the COVID-19 Pandemic
辩护、美德和尖刻:关于 COVID-19 大流行期间英国议员在线参与和虐待的研究
  • DOI:
    10.48550/arxiv.2008.05261
  • 发表时间:
    2020
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Farrell T
  • 通讯作者:
    Farrell T
{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ monograph.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ sciAawards.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ conferencePapers.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ patent.updateTime }}

Kalina Bontcheva其他文献

SheffieldVeraAI at SemEval-2024 Task 4: Prompting and fine-tuning a Large Vision-Language Model for Binary Classification of Persuasion Techniques in Memes
SheffieldVeraAI 在 SemEval-2024 任务 4:提示和微调用于模因说服技术二元分类的大型视觉语言模型
Integrated Testbed of Case Study 1
案例1综合测试平台
  • DOI:
  • 发表时间:
    2008
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Danica Damljanovic;Kalina Bontcheva;Milan Agatonovic;Ian Roberts;T. Heitz
  • 通讯作者:
    T. Heitz
Online Abuse toward Candidates during the UK General Election 2019: Working Paper
2019 年英国大选期间针对候选人的网络虐待行为:工作文件
  • DOI:
  • 发表时间:
    2020
  • 期刊:
  • 影响因子:
    0
  • 作者:
    G. Gorrell;M. Bakir;Ian Roberts;M. Greenwood;Kalina Bontcheva
  • 通讯作者:
    Kalina Bontcheva
Reputation Profiling with GATE
使用 GATE 进行声誉分析
Experiments and evaluation of testbed 1
试验台1的实验与评估
  • DOI:
  • 发表时间:
    2009
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Danica Damljanovic;Kalina Bontcheva
  • 通讯作者:
    Kalina Bontcheva

Kalina Bontcheva的其他文献

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

{{ truncateString('Kalina Bontcheva', 18)}}的其他基金

XAIvsDisinfo: eXplainable AI Methods for Categorisation and Analysis of COVID-19 Vaccine Disinformation and Online Debates
XAIvsDisinfo:用于分类和分析 COVID-19 疫苗虚假信息和在线辩论的 eXplainable AI 方法
  • 批准号:
    EP/W011212/1
  • 财政年份:
    2021
  • 资助金额:
    $ 64.75万
  • 项目类别:
    Research Grant
Machine Learning Methods for Personalised, Abstractive Summarisation of Consumer-Generated Media
用于消费者生成媒体的个性化、抽象总结的机器学习方法
  • 批准号:
    EP/I004327/1
  • 财政年份:
    2010
  • 资助金额:
    $ 64.75万
  • 项目类别:
    Fellowship

相似国自然基金

患者安全视角下医疗AI技术对医务人员风险感知的双刃剑机制研究
  • 批准号:
  • 批准年份:
    2025
  • 资助金额:
    0.0 万元
  • 项目类别:
    省市级项目
基于AI 技术的高校网络舆情监测与治理路径研究
  • 批准号:
  • 批准年份:
    2025
  • 资助金额:
    0.0 万元
  • 项目类别:
    省市级项目
基于可穿戴设备与AI动态优化的阿尔茨海默病早期生活方式干预系统研发及效应研究
  • 批准号:
  • 批准年份:
    2025
  • 资助金额:
    0.0 万元
  • 项目类别:
    省市级项目
成渝交通一体化背景下的高速公路智慧管控系统:大数据驱动、AI预警与数智决策
  • 批准号:
  • 批准年份:
    2025
  • 资助金额:
    0.0 万元
  • 项目类别:
    省市级项目
AI驱动药物研发的技术发展趋势及重庆技术创新路径选择战略研究
  • 批准号:
  • 批准年份:
    2025
  • 资助金额:
    0.0 万元
  • 项目类别:
    省市级项目
AI赋能职业教育:“智慧职教”平台教学视频核心知识抽取研究
  • 批准号:
  • 批准年份:
    2025
  • 资助金额:
    0.0 万元
  • 项目类别:
    省市级项目
基于AI的光谱-色度耦合动态调控系统技术研究及其在城乡建筑光环境优化中的应用
  • 批准号:
  • 批准年份:
    2025
  • 资助金额:
    0.0 万元
  • 项目类别:
    省市级项目
多模态下AI技术融合在教育创新中的应用与关键技术研究
  • 批准号:
  • 批准年份:
    2025
  • 资助金额:
    0.0 万元
  • 项目类别:
    省市级项目
基于职业教育和产学研协同的低成本专用大模型AI系统研究
  • 批准号:
  • 批准年份:
    2025
  • 资助金额:
    0.0 万元
  • 项目类别:
    省市级项目
联邦学习驱动下成渝地区职业教育AI产教协同的跨区域数据共享机制与培养方案优化要素机理研究
  • 批准号:
  • 批准年份:
    2025
  • 资助金额:
    0.0 万元
  • 项目类别:
    省市级项目

相似海外基金

iREAL: Inclusive Requirements Elicitation for AI in Libraries to Support Respectful Management of Indigenous Knowledges
iREAL:图书馆人工智能的包容性需求获取,支持对本土知识的尊重管理
  • 批准号:
    AH/Z505638/1
  • 财政年份:
    2024
  • 资助金额:
    $ 64.75万
  • 项目类别:
    Research Grant
I-Corps: Translation potential of using artificial intelligence (AI) for an interactive and inclusive language-learning process designed for young children
I-Corps:使用人工智能 (AI) 为幼儿设计的交互式和包容性语言学习过程的翻译潜力
  • 批准号:
    2418277
  • 财政年份:
    2024
  • 资助金额:
    $ 64.75万
  • 项目类别:
    Standard Grant
RAPID: DRL AI: A Community-Inclusive AI Chatbot to Support Teachers in Developing Culturally Focused and Universally Designed STEM Activities
RAPID:DRL AI:社区包容性 AI 聊天机器人,支持教师开展以文化为中心且通用设计的 STEM 活动
  • 批准号:
    2334631
  • 财政年份:
    2023
  • 资助金额:
    $ 64.75万
  • 项目类别:
    Standard Grant
Inclusive AI for Healthy Change, Retaining Identity Preservation
包容性人工智能促进健康变革,保留身份保护
  • 批准号:
    10059947
  • 财政年份:
    2023
  • 资助金额:
    $ 64.75万
  • 项目类别:
    Grant for R&D
AI Institute for Inclusive Intelligent Technologies for Education (INVITE)
AI 普惠智能教育技术研究所 (INVITE)
  • 批准号:
    2229612
  • 财政年份:
    2023
  • 资助金额:
    $ 64.75万
  • 项目类别:
    Cooperative Agreement
Equitable and Inclusive AI for public health and healthcare: Advancing EDI Principles in the life cycle of AI
面向公共卫生和医疗保健的公平和包容性人工智能:在人工智能生命周期中推进 EDI 原则
  • 批准号:
    480918
  • 财政年份:
    2023
  • 资助金额:
    $ 64.75万
  • 项目类别:
    Miscellaneous Programs
NSF Convergence Accelerator Track H: An Inclusive, Human-Centered, and Convergent Framework for Transforming Voice AI Accessibility for People Who Stutter
NSF 融合加速器轨道 H:一个包容性、以人为本的融合框架,用于改变口吃者的语音 AI 可访问性
  • 批准号:
    2345086
  • 财政年份:
    2023
  • 资助金额:
    $ 64.75万
  • 项目类别:
    Cooperative Agreement
NSF Convergence Accelerator Track H: Addressing the Fragmented Information Access Problem - A Community-Driven, AI-Powered Platform for Inclusive, Multimodal Content Creation
NSF 融合加速器轨道 H:解决碎片化信息访问问题 - 社区驱动、人工智能驱动的包容性多模式内容创建平台
  • 批准号:
    2345159
  • 财政年份:
    2023
  • 资助金额:
    $ 64.75万
  • 项目类别:
    Cooperative Agreement
A voice-user-interface (VUI) artificial intelligence (AI) model exploration for electric vehicle (EV) charging that is inclusive to those living with disabilities
针对电动汽车 (EV) 充电的语音用户界面 (VUI) 人工智能 (AI) 模型探索,为残疾人士提供包容性
  • 批准号:
    10087169
  • 财政年份:
    2023
  • 资助金额:
    $ 64.75万
  • 项目类别:
    Collaborative R&D
Canada-UK AI 2019 : Responsible Automation for Inclusive Mobility (RAIM) : Using AI to Develop Future Transport Systems that Meet the Needs of Ageing Populations
加拿大-英国 AI 2019:包容性出行的负责任自动化 (RAIM):利用人工智能开发满足老龄化人口需求的未来交通系统
  • 批准号:
    548594-2019
  • 财政年份:
    2022
  • 资助金额:
    $ 64.75万
  • 项目类别:
    Alliance Grants
{{ showInfoDetail.title }}

作者:{{ showInfoDetail.author }}

知道了