PLEAD: Provenance-driven and Legally-grounded Explanations for Automated Decisions

如今，算法和人工智能在许多控制或影响我们生活各个方面的技术系统中发挥着关键作用。他们每天根据交通状况优化我们的驾驶路线;他们决定我们的抵押贷款申请是否获得批准;他们甚至为我们推荐潜在的生活伴侣。他们在幕后默默地工作，没有引起我们太多的注意，直到他们不这样做。当我们的信用卡申请在两秒钟内被批准时，我们中的一些人可能会想太多。只有当它被拒绝时，我们才开始质疑这个决定。大多数时候，我们得到的答案并不令人满意，如果我们得到任何。这种不透明的自动化决策在日常生活中的蔓延，推动了公众对算法问责制的需求-解释和证明自动化决策的义务。主要的担忧是，这些算法，实际上是黑匣子，接收我们的数据并以我们不理解的方式做出影响我们的决定是不正确的。因此，《通用数据保护条例》要求我们作为数据主体，获得“有关所涉及逻辑的有意义的信息，以及此类处理的重要性和预期后果。“同样，消费者在接受金融服务时应根据金融服务法规得到公平对待，算法应根据数据保护，平等和人权法不受歧视。然而，由于法律法规没有规定如何满足这些要求，企业不得不自己解释这些要求，采用各种手段，包括报告，互动网站，甚至专门的呼叫中心，向客户提供解释。在这种背景下，出处，特别是其标准PROV，描述了一段信息或数据是如何创建的，以及是什么影响了它的生成。在记录的出处跟踪中，我们可以追溯自动化决策，以提供一些问题的答案，例如使用什么数据来支持决策，谁或哪个组织负责数据，还有谁可能受到影响。虽然来源信息在结构上很简单，但是从自动化系统捕获的来源往往对人类消费来说是压倒性的。此外，简单地向一个人提供出处并不一定构成解释。需要对其进行总结并提取其精髓，以便能够构建针对特定监管目的的解释。PLEAD汇集了一个由技术专家、法律的专家、商业公司和公共组织组成的跨学科团队，以调查来源如何帮助解释自动化决策背后的逻辑，从而使数据主体受益，并帮助数据控制者证明遵守法律。特别是，我们将识别与其目的相关的算法决策的各种类型的有意义的解释，并根据适用于英国企业的与数据保护，歧视和金融服务有关的法律的要求对其进行分类。在此基础上，我们将构思出一种简化生成算法，它可以处理、总结和抽象由自动决策管道记录的出处。将为数据控制员创建一个解释助手工具，为他们的应用程序提供基于出处的解释功能。在整个项目过程中，我们将通过访谈和用户研究与合作伙伴、数据主体、数据控制者和监管机构接触，以确保解释符合目的和有意义。因此，来源驱动和法律依据的解释将使数据主体能够信任自动化决策，并使数据控制者能够确保遵守对其组织的法律的要求。

Algorithms and Artificial Intelligence play a key role nowadays in many technological systems that control or affect various aspects of our lives. They optimise our driving routes every day according to traffic conditions; they decide whether our mortgage applications get approved; they even recommend us potential life partners. They work silently behind the scene without much of our notice, until they do not. Few of us would probably think much about it when our credit card application is approved in two seconds. Only when it is rejected, do we start to question the decision. Most of the time, the answers we get are not satisfactory, if we get any at all. The spread of such opaque automated decision-making in daily life has been driving the public demand for algorithmic accountability - the obligation to explain and justify automated decisions. The main concern is that it is not right for those algorithms, effectively black boxes, to take in our data and to make decisions affecting us in ways we do not understand. For this reason, the General Data Protection Regulation requires that we, as data subjects, be provided with "meaningful information about the logic involved, as well as the significance and the envisaged consequences of such processing." Likewise, consumers should be treated fairly when receiving financial services as per financial services regulations and algorithms should be free of discrimination as per data protection, equality and human rights laws. However, as laws and regulations do not prescribe how to meet such requirements, businesses are left with having to interpret those themselves, employing a variety of means, including reports, interactive websites, or even dedicated call centres, to provide explanations to their customers.Against this background, provenance, and specifically its standard PROV, describes how a piece of information or data was created and what influenced its production. Within recorded provenance trails, we can retrace automated decisions to provide answers to some questions, such as what data were used to support a decision, who or what organisation was responsible for the data, who else might have been impacted. While provenance information is structurally simple, provenance captured from automated systems, however, tends to be overwhelming for human consumption. In addition, simply making provenance available to a person does not necessarily constitute an explanation. It would need to be summarised and its essence extracted to be able to construct an explanation addressing a specific regulatory purpose. How we do this is unknown today.PLEAD brings together an interdisciplinary team of technologists, legal experts, commercial companies and public organisations to investigate how provenance can help explain the logic that underlies automated decision-making to the benefit of data subjects as well as help data controllers to demonstrate compliance with the law. In particular, we will identify various types of meaningful explanations for algorithmic decisions in relation to their purposes, categorise them against the legal requirements applicable to UK businesses relating to data protection, discrimination and financial services. Building on those, we will conceive explanation-generating algorithms that process, summarise and abstract provenance logged by automated decision-making pipelines. An Explanation Assistant tool will be created for data controllers to provision their applications with provenance-based explanations capabilities. Throughout the project, we will engage with partners, data subjects, data controllers, and regulators via interviews and user studies to ensure the explanations are fit for purpose and meaningful. As a result, explanations that are provenance-driven and legally-grounded will allow data subjects to place their trust in automated decisions, and will allow data controllers to ensure compliance with legal requirements placed on their organisations.