Poechhacker Nikolaus, Kacianka Severin
Institute for Public Law and Political Science, University of Graz, Graz, Austria.
Department of Computer Science, Chair of Software and Systems Engineering, Technical University of Munich, Munich, Germany.
Front Big Data. 2021 Jan 29;3:519957. doi: 10.3389/fdata.2020.519957. eCollection 2020.
The increasing use of automated decision making (ADM) and machine learning sparked an ongoing discussion about algorithmic accountability. Within computer science, a new form of producing accountability has been discussed recently: causality as an expression of algorithmic accountability, formalized using structural causal models (SCMs). However, causality itself is a concept that needs further exploration. Therefore, in this contribution we confront ideas of SCMs with insights from social theory, more explicitly pragmatism, and argue that formal expressions of causality must always be seen in the context of the social system in which they are applied. This results in the formulation of further research questions and directions.
自动化决策（ADM）和机器学习的使用日益增加，引发了关于算法问责制的持续讨论。在计算机科学领域，最近讨论了一种新的产生问责制的形式：因果关系作为算法问责制的一种表达，使用结构因果模型（SCM）进行形式化。然而，因果关系本身是一个需要进一步探索的概念。因此，在本论文中，我们将SCM的理念与社会理论（更明确地说是实用主义）的见解相结合，并认为因果关系的形式化表达必须始终放在其应用的社会系统背景中去看待。这就产生了进一步的研究问题和方向。
