DADD: Discovering and Attesting Digital Discrimination

DADD：发现并证明数字歧视

• 批准号: EP/R033188/1
• 负责人: Jose Such
• 金额: $ 83.3万
• 依托单位: King's College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FR033188%2F1
• 关键词: DADD; Discovering; Attesting; Digital; Discrimination

In digital discrimination, users are treated unfairly, unethically or just differently based on their personal data. Examples include low-income neighborhoods targeted with high-interest loans; women being undervalued by 21% in online marketing; and online ads suggestive of arrest records appearing more often with searches of black-sounding names than white-sounding names. Digital discrimination very often reproduces existing instances of discrimination in the offline world by either inheriting the biases of prior decision makers, or simply reflecting widespread prejudices in society. Digital discrimination may also have an even more perverse result, it may exacerbate existing inequalities by causing less favourable treatment for historically disadvantaged groups, suggesting they actually deserve that treatment. As more and more tasks are delegated to computers, mobile devices, and autonomous systems, digital discrimination is becoming a huge problem. Digital discrimination can be the result of algorithmic biases, i.e., the way in which a particular algorithm has been designed creates discriminatory outcomes, but it also occurs using non-biased algorithms when they are fed or trained with biased data. Research has been conducted on so-called fair algorithms, tackling biased input data, demonstrating learned biases, and measuring relative influence of data attributes, which can quantify and limit the extent of bias introduced by an algorithm or dataset. But, how much bias is too much? That is, what is legal, ethical and/or socially-acceptable? And even more importantly, how do we translate those legal, ethical, or social expectations into automated methods that attest digital discrimination in datasets and algorithms? DADD (Discovering and Attesting Digital Discrimination) is a *novel cross-disciplinary collaboration* to address these open research questions following a continuously-running co-creation process with academic (Computer Science, Digital Humanities, Law and Ethics) and non-academic partners (Google, AI Club), and the general public, including technical and non-technical users. DADD will design ground-breaking methods to certify whether or not datasets and algorithms discriminate by automatically verifying computational non-discrimination norms, which will in turn be formalised based on socio-economic, cultural, legal, and ethical dimensions, creating the new *transdisciplinary field of digital discrimination certification*.

在数字歧视中，用户受到不公平、不道德或基于个人数据的不同对待。例如，低收入社区成为高息贷款的目标;女性在网络营销中被低估了21%;网络广告暗示逮捕记录的出现频率更高，搜索听起来像黑人的名字而不是白人的名字。数字歧视经常通过继承先前决策者的偏见或仅仅反映社会中普遍存在的偏见，再现离线世界中现有的歧视实例。数字歧视还可能产生更有害的结果，它可能加剧现有的不平等，使历史上处于不利地位的群体受到更不利的待遇，这表明他们实际上应该得到这种待遇。随着越来越多的任务被委托给计算机、移动的设备和自主系统，数字歧视正成为一个巨大的问题。数字歧视可能是算法偏差的结果，即，设计特定算法的方式会产生歧视性结果，但在使用非偏置算法时，当它们被输入或训练有偏置数据时，也会出现歧视性结果。人们对所谓的公平算法进行了研究，处理有偏见的输入数据，展示学习的偏见，并测量数据属性的相对影响，这可以量化和限制算法或数据集引入的偏见程度。但是，多大的偏见是太多了？也就是说，什么是法律的、道德的和/或社会可接受的？更重要的是，我们如何将这些法律的、道德的或社会的期望转化为自动化的方法来证明数据集和算法中的数字歧视？DADD（发现和证明数字歧视）是一个新颖的跨学科合作项目，旨在解决这些开放的研究问题，该项目与学术（计算机科学，数字人文，法律和伦理）和非学术合作伙伴（谷歌，AI俱乐部）以及公众（包括技术和非技术用户）持续共同创建。DADD将设计突破性的方法，通过自动验证计算非歧视规范来验证数据集和算法是否具有歧视性，这些规范将基于社会经济，文化，法律的和道德维度进行正式化，从而创建新的跨学科领域数字歧视认证。

项目成果

Discovering and Categorising Language Biases in Reddit

• DOI: 10.1609/icwsm.v15i1.18048
• 发表时间: 2020-08
• 作者: Xavier Ferrer Aran;T. Nuenen;J. Such;N. Criado

Bias and Discrimination in AI: A Cross-Disciplinary Perspective

• DOI: 10.1109/mts.2021.3056293
• 发表时间: 2021-06-01
• 期刊: IEEE TECHNOLOGY AND SOCIETY MAGAZINE
• 影响因子: 2.2
• 作者: Ferrer, Xavier;van Nuenen, Tom;Criado, Natalia
• 通讯作者: Criado, Natalia

Attesting Biases and Discrimination using Language Semantics

使用语言语义证明偏见和歧视

• 发表时间: 2019
• 作者: Ferrer X

A Normative approach to Attest Digital Discrimination

证明数字歧视的规范方法

• 发表时间: 2020
• 作者: Criado N

Algorithmic Regulation

算法调节

• DOI: 10.1093/oso/9780198838494.003.0007
• 发表时间: 2019
• 作者: Griffiths A