Sloane Mona, Moss Emanuel, Chowdhury Rumman
New York University, New York, NY, USA.
Cornell Tech and Data & Society Research Institute, New York, NY, USA.
Patterns (N Y). 2022 Feb 11;3(2):100425. doi: 10.1016/j.patter.2021.100425.
In this perspective, we develop a matrix for auditing algorithmic decision-making systems (ADSs) used in the hiring domain. The tool is a socio-technical assessment of hiring ADSs that is aimed at surfacing the underlying assumptions that justify the use of an algorithmic tool and the forms of knowledge or insight they purport to produce. These underlying assumptions, it is argued, are crucial for assessing not only whether an ADS works "as intended," but also whether the intentions with which the tool was designed are well founded. Throughout, we contextualize the use of the matrix within current and proposed regulatory regimes and within emerging hiring practices that incorporate algorithmic technologies. We suggest using the matrix to expose underlying assumptions rooted in pseudo-scientific essentialized understandings of human nature and capability and to critically investigate emerging auditing standards and practices that fail to address these assumptions.
从这个角度出发，我们开发了一个用于审核招聘领域算法决策系统（ADS）的矩阵。该工具是对招聘ADS的社会技术评估，旨在揭示那些证明算法工具使用合理性的潜在假设，以及它们声称能够产生的知识或见解形式。有人认为，这些潜在假设不仅对于评估ADS是否“按预期运行”至关重要，而且对于评估该工具的设计意图是否有充分依据也至关重要。在整个过程中，我们将该矩阵的使用置于当前和拟议的监管制度以及纳入算法技术的新兴招聘实践背景之中。我们建议使用该矩阵来揭示基于对人性和能力的伪科学本质主义理解的潜在假设，并批判性地研究未能解决这些假设的新兴审核标准和实践。
