CAREER: Towards Methodologies and Tools for Conducting Algorithm Audits

该项目将开发进行算法审计的方法和工具。算法审计使用受控实验来检查算法系统，如在线服务或大数据信息档案，并确定(1)它是如何工作的，(2)它是否可能造成损害。记录在案的算法危害的例子包括歧视、种族主义和不公平的贸易做法。尽管人们越来越意识到算法系统可能造成危害，但在实践中实际检测这种危害仍然是一个关键挑战。鉴于大多数受关注的算法都是专有的和不透明的，显然需要对这些系统进行黑盒分析的方法。许多监管机构和政府都表达了对算法的担忧，并希望在这一领域提高透明度和问责性。这项研究将开发方法来审计影响许多人的三个领域的算法：在线市场、招聘网站和金融服务。这三个领域的审计算法将需要解决基本的方法论挑战，例如如何分析具有大量未知特征集的系统，以及如何在没有地面真实数据的情况下估计特征值。为了应对这些广泛的挑战，这项研究将借鉴以往审计个性化算法的经验。此外，每个领域还带来了将单独解决的独特挑战。例如，将构建利用大量在线和离线历史的新型审计工具。这些新工具将允许检查以前研究人员无法访问的系统，包括金融服务公司。方法论、开放源码和数据集将提供给其他学术研究人员和监管机构。该项目包括两个综合教育目标：(1)开设一门新的关于大数据伦理的计算机科学课程，教授如何识别和减轻大数据技术的有害副作用，以及(2)制作基于网络的审计工具版本，其设计目的是向公众提供信息和信息，以提高特定的、重要的算法系统的透明度，并促进关于算法系统扩散和影响的一般教育。

This project will develop methodologies and tools for conducting algorithm audits. An algorithm audit uses controlled experiments to examine an algorithmic system, such as an online service or big data information archive, and ascertain (1) how it functions, and (2) whether it may cause harm. Examples of documented harms by algorithms include discrimination, racism, and unfair trade practices. Although there is rising awareness of the potential for algorithmic systems to cause harm, actually detecting this harm in practice remains a key challenge. Given that most algorithms of concern are proprietary and non-transparent, there is a clear need for methods to conduct black-box analyses of these systems. Numerous regulators and governments have expressed concerns about algorithms, as well as a desire to increase transparency and accountability in this area.This research will develop methodologies to audit algorithms in three domains that impact many people: online markets, hiring websites, and financial services. Auditing algorithms in these three domains will require solving fundamental methodological challenges, such as how to analyze systems with large, unknown feature sets, and how to estimate feature values without ground-truth data. To address these broad challenges, the research will draw on insights from prior experience auditing personalization algorithms. Additionally, each domain also brings unique challenges that will be addressed individually. For example, novel auditing tools will be constructed that leverage extensive online and offline histories. These new tools will allow examination of systems that were previously inaccessible to researchers, including financial services companies. Methodologies, open-source code, and datasets will be made available to other academic researchers and regulators. This project includes two integrated educational objectives: (1) to create a new computer science course on big data ethics, teaching how to identify and mitigate harmful side-effects of big data technologies, and (2) production of web-based versions of the auditing tools that are designed to be accessible and informative to the general public, that will increase transparency around specific, prominent algorithmic systems, as well as promote general education about the proliferation and impact of algorithmic systems.