Managing Algorithmic Accountability: Balancing Reputational Concerns, Engagement Strategies, and the Potential of Rational Discourse
Managing Algorithmic Accountability: Balancing Reputational Concerns, Engagement Strategies, and the Potential of Rational Discourse
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DOI：
10.1007/s10551-019-04226-4
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发表时间：
2020-05-01
影响因子：
6.1
通讯作者：
Fieseler, Christian
中科院分区：
文献类型：
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作者：
Buhmann, Alexander;Passmann, Johannes;Fieseler, Christian
虽然今天的组织广泛使用复杂的算法，但由于算法的不透明性和流动性，算法问责制的概念仍然是一个难以捉摸的理想。在本文中，我们开发了一个管理算法问责制的框架，突出了三个相互关联的维度：声誉问题、参与策略和话语原则。该框架澄清了(a)算法的问责过程是由对算法的认知设置、不透明性和结果的声誉担忧驱动的；(b)组织实际参与对算法的突发期望的方式可能是操纵性的、自适应的或道德的；(c)当问责关系受到复杂算法系统的不透明性和流动性的沉重负担时，参与的重点应转向合理的沟通过程，通过这种过程，可以随着时间的推移，对算法的开发、工作和后果进行持续和初步的评估。这种参与的程度实际上是理性的，可以根据参与、理解、多声音性和反应性这四个话语伦理原则来评估。我们得出结论，该框架可以帮助组织及其环境共同努力，为复杂算法提供更大的问责制。它可以进一步帮助组织在围绕问责问题的声誉定位。本文中介绍的话语伦理原则旨在提升这些定位竞赛，使其超越单纯的适应或遵从，并帮助指导组织找到道德和前瞻性的解决方案来解决责任问题。
While organizations today make extensive use of complex algorithms, the notion of algorithmic accountability remains an elusive ideal due to the opacity and fluidity of algorithms. In this article, we develop a framework for managing algorithmic accountability that highlights three interrelated dimensions: reputational concerns, engagement strategies, and discourse principles. The framework clarifies (a) that accountability processes for algorithms are driven by reputational concerns about the epistemic setup, opacity, and outcomes of algorithms; (b) that the way in which organizations practically engage with emergent expectations about algorithms may be manipulative, adaptive, or moral; and (c) that when accountability relationships are heavily burdened by the opacity and fluidity of complex algorithmic systems, the emphasis of engagement should shift to a rational communication process through which a continuous and tentative assessment of the development, workings, and consequences of algorithms can be achieved over time. The degree to which such engagement is, in fact, rational can be assessed based on four discourse-ethical principles of participation, comprehension, multivocality, and responsiveness. We conclude that the framework may help organizations and their environments to jointly work toward greater accountability for complex algorithms. It may further help organizations in reputational positioning surrounding accountability issues. The discourse-ethical principles introduced in this article are meant to elevate these positioning contests to extend beyond mere adaption or compliance and help guide organizations to find moral and forward-looking solutions to accountability issues.