一种关系而不是一件事：算法问责制和评估文档的关系方法
A relationship and not a thing: A relational approach to algorithmic accountability and assessment documentation
摘要：
许多关于算法问责制的学术、监管和公众对话的核心问题是，谁应该有权访问揭示算法系统内部工作、预期功能和预期后果的文档，这可能会为受影响的公众建立新的途径来质疑这些系统的运作。目前，开发人员在很大程度上垄断了有关其系统实际工作方式的信息，并被激励保持自己对系统如何影响世界的无知。越来越多的立法者、监管机构和倡导者转向评估文档，以解决公众对算法危害的体验与开发人员记录和证明其设计决策的义务之间的差距。然而，目前的地位和专业知识问题阻止了公众在预防和纠正算法伤害方面的共同利益;正如我们在多个案件中所证明的那样，法院经常发现计算伤害是不可认知的，很少要求开发人员解决实质性的伤害索赔。算法影响评估制度建立在三重问责关系的基础上，可以通过围绕公众获取报告和文件的程序性权利来改变这种情况。发展一个关系的问责方法，我们认为，强大的问责制度必须为公众建立机会，凝聚在共同的经验和利益，并质疑影响他们生活的算法系统的结果。此外，许多司法管辖区目前正在考虑的算法问责政策必须为公众提供足够的地位和机会，以访问和质疑行为者提供的文件和论坛通过的判决。
英文摘要：
Central to a number of scholarly, regulatory, and public conversations about algorithmic accountability is the question of who should have access to documentation that reveals the inner workings, intended function, and anticipated consequences of algorithmic systems, potentially establishing new routes for impacted publics to contest the operations of these systems. Currently, developers largely have a monopoly on information about how their systems actually work and are incentivized to maintain their own ignorance about aspects of how their systems affect the world. Increasingly, legislators, regulators and advocates have turned to assessment documentation in order to address the gap between the public's experience of algorithmic harms and the obligations of developers to document and justify their design decisions. However, issues of standing and expertise currently prevent publics from cohering around shared interests in preventing and redressing algorithmic harms; as we demonstrate with multiple cases, courts often find computational harms non-cognizable and rarely require developers to address material claims of harm. Constructed with a triadic accountability relationship, algorithmic impact assessment regimes could alter this situation by establishing procedural rights around public access to reporting and documentation. Developing a relational approach to accountability, we argue that robust accountability regimes must establish opportunities for publics to cohere around shared experiences and interests, and to contest the outcomes of algorithmic systems that affect their lives. Furthermore, algorithmic accountability policies currently under consideration in many jurisdictions must provide the public with adequate standing and opportunities to access and contest the documentation provided by the actors and the judgments passed by the forum.
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