Exploring value change

Q4 Arts and Humanities
Tristan Emile de Wildt, Vanessa Jine Schweizer
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引用次数: 0

Abstract

This article aims to explore the use of cross-impact balances (CIB) to identify scenarios of value change. The possibility of value change has received little attention in the literature on value-sensitive design (VSD). Examples of value change include the emergence of new values and changes in the relative importance of values. Value change could lead to a mismatch between values embedded in technology and the way they are currently considered in society. Such a mismatch could result in a lack of acceptability of technologies, increasing social tensions and injustices. However, methods to study value change in the VSD literature are rare. CIB is a scenario tool that can study systems characterized by feedback loops that are hard to describe mathematically. This is often the case when aiming to define values and their relationships. We demonstrate the use of CIB to identify scenarios of value change using two cases: digital voice assistants and gene drive organisms. Our findings show that CIB is helpful in building scenarios of value change, even in instances where the operationalization of values is complex. CIB also helps us to understand the mechanisms of value change and evaluate when such mechanisms occur. Finally, we find that CIB is particularly useful for social learning and explanatory modelling. CIB can therefore contribute to the design of value-sensitive technologies.
探索价值变化
本文旨在探索使用交叉影响平衡(CIB)来识别价值变化的场景。在价值敏感设计(VSD)的文献中,价值变化的可能性很少受到关注。价值变化的例子包括新价值的出现和价值相对重要性的变化。价值变化可能导致嵌入技术的价值观与当前社会对它们的看法不匹配。这种不匹配可能导致技术缺乏可接受性,增加社会紧张和不公正。然而,研究VSD值变化的方法文献很少。CIB是一个场景工具,可以研究以反馈循环为特征的系统,这些系统很难用数学方法描述。在定义价值和它们之间的关系时,经常会出现这种情况。我们使用两个案例来演示CIB的使用,以确定价值变化的场景:数字语音助手和基因驱动生物。我们的研究结果表明,CIB有助于构建价值变化的场景,即使在价值的操作化很复杂的情况下也是如此。CIB还帮助我们理解价值变化的机制,并在这种机制发生时进行评估。最后,我们发现CIB对社会学习和解释建模特别有用。因此,CIB可以帮助设计对价值敏感的技术。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Prometheus (Italy)
Prometheus (Italy) Arts and Humanities-Literature and Literary Theory
CiteScore
0.20
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0.00%
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