In this paper, we argue that the characteristics of digital platforms challenge the fundamental assumptions of value sensitive design (VSD). Traditionally, VSD methods assume that we can identify relevant values during the design phase of new technologies. The underlying assumption is that there is only epistemic uncertainty about which values will be impacted by a technology. VSD methods suggest that one can predict which values will be affected by new technologies by increasing knowledge about how values are interpreted or understood in context. In contrast, digital platforms exhibit a novel form of uncertainty, namely, ontological uncertainty: even with full information and overview, it cannot be foreseen what users or developers will do with digital platforms. Hence, predictions about which values are affected might not hold. In this paper, we suggest expanding VSD methods to account for value dynamism resulting from ontological uncertainty. Our expansions involve (1) extending VSD to the entire lifecycle of a platform, (2) broadening VSD through the addition of reflexivity, i.e. second-order learning about what values to aim at, and (3) adding specific tools of moral sandboxing and moral prototyping to enhance such reflexivity. While we illustrate our approach with a short case study about ride-sharing platforms such as Uber, our approach is relevant for other technologies exhibiting ontological uncertainty as well, such as machine learning, robotics and artificial intelligence.

中文翻译：

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数字平台和负责任的创新：扩展价值敏感型设计以克服本体论的不确定性

在本文中，我们认为数字平台的特征挑战了价值敏感设计（VSD）的基本假设。传统上，VSD方法假定我们可以在新技术的设计阶段识别相关值。基本假设是，对于哪些值将受技术影响只有认识论上的不确定性。VSD方法建议人们通过增加有关如何在上下文中解释或理解值的知识来预测新技术将影响哪些值。相反，数字平台表现出一种新型的不确定性，即本体不确定性：即使有完整的信息和概述，也无法预见用户或开发人员将如何使用数字平台。因此，关于哪个值受影响的预测可能无法成立。在本文中，我们建议扩展VSD方法，以解决由本体论不确定性引起的价值动态。我们的扩展包括（1）将VSD扩展到平台的整个生命周期，（2）通过增加自反性来扩大VSD，即对目标价值的二阶学习，以及（3）添加道德沙盒和道德原型，以增强这种反身性。虽然我们以关于Uber之类的乘车共享平台的案例研究为例来说明我们的方法，但我们的方法也适用于其他表现出本体不确定性的技术，例如机器学习，机器人技术和人工智能。