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Deep learning and principal-agent problems of algorithmic governance: The new materialism perspective

机译：算法治理的深度学习和委托 - 代理问题：新的唯物主义视角

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With the advent of artificial intelligence, stakeholders and experts cede their policy decisions for human affairs to computer algorithms in algorithmic governance. However, they face a new material principal-agent problem, which occurs between computer scientists as principals and computer algorithms as agents. Drawing upon new materialism, this study investigates informational asymmetry, malfeasance, agency relationships, and solutions related to the principal-agent problem. The inscrutability of computer algorithms is central to the notion of informational asymmetry and their relational agency is related to the notion of malfeasance. The principal-agent relationship is viewed as the output of socio-material assemblages in which computer scientists strive to build trust with computer algorithms. The inscrutability of computer algorithms coupled with their performativity would make it challenging for human principals to ascertain the malfeasance of computer algorithms as agents, thereby forming the material principal-agent problem. Finally, this study recommends an incremental, precautionary, and technologically pluralist approach to cope with this problem.
机译：随着人工智能，利益相关者和专家的出现，使他们在算法治理中对计算机算法进行人事的政策决策。然而，他们面临着新的材料委托 - 代理问题，这在计算机科学家之间发生了作为代理的主体和计算机算法。借鉴新的唯物主义，研究了与委托 - 代理问题有关的信息不对称，渎职，代理关系和解决方案。计算机算法的阻碍性是信息不对称的概念，他们的关系机构与渎职概念有关。委托代理关系被视为社会材料组合的产出，其中计算机科学家努力建立与计算机算法的信任。计算机算法耦合的计算机算法及其表现性将使人类原则具有挑战，以确定计算机算法作为药剂的误报，从而形成材料主体代理问题。最后，本研究建议应对这个问题的增量，预防和技术上的多元化方法。

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