DADD: Discovering and Attesting Digital Discrimination

在数字歧视中，用户受到不公平、不道德或只是基于他们的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%；搜索黑人名字而不是白人名字的在线广告更容易显示逮捕记录。数字歧视经常通过继承先前决策者的偏见，或者只是反映社会上广泛存在的偏见，来复制线下世界中现有的歧视实例。数字歧视还可能产生更反常的结果，它可能会使历史上处于不利地位的群体受到不太有利的待遇，从而加剧现有的不平等，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，即特定算法的设计方式造成歧视性结果，但当向他们提供或训练有偏见的数据时，也会使用无偏见的算法。已经对所谓的公平算法进行了研究，这些算法处理有偏差的输入数据，展示学习的偏差，并测量数据属性的相对影响，这些属性可以量化和限制算法或数据集引入的偏差程度。但是，有多大的偏见才算过分呢？也就是说，什么是合法的、道德的和/或社会可以接受的？更重要的是，我们如何将这些法律、道德或社会期望转化为自动化方法，以证明数据集和算法中的数字歧视？DADD(发现和证明数字歧视)是一种*新颖的跨学科合作*，旨在解决这些开放的研究问题，此前，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、人工智能俱乐部)以及包括技术和非技术用户在内的普通公众不断运行共同创建过程。DADD将设计突破性的方法，通过自动验证计算的非歧视规范来验证数据集和算法是否存在歧视，这些规范将基于社会经济、文化、法律和伦理维度正式确定，从而创建数字歧视认证的新的*跨学科领域*。

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*.