Expressiveness, Cost, and Collectivism: How the Design of Preference Languages Shapes Participation in Algorithmic Decision-Making
Expressiveness, Cost, and Collectivism: How the Design of Preference Languages Shapes Participation in Algorithmic Decision-Making
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DOI：
10.1145/3544548.3580996
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发表时间：
2023-04
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影响因子：
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通讯作者：
Samantha Robertson;Tonya Nguyen;Cathy Hu;C. Albiston;Afshin Nikzad;Niloufar Salehi
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作者：
Samantha Robertson;Tonya Nguyen;Cathy Hu;C. Albiston;Afshin Nikzad;Niloufar Salehi
新出现的参与式算法设计方法建议收集和汇总各个利益相关者的偏好，以创建考虑这些利益相关者价值观的算法系统。通过对美国两个公立学区两年的研究，我们研究了家庭和学区如何利用学生对学校的偏好，在算法学生分配系统的背景下实现他们的目标。我们发现，偏好语言的设计，即参与者必须向决策者表达他们的需求和目标的结构，塑造了有意义参与的机会。我们定义了偏好语言的三个属性-表现力、成本和集体主义-并讨论了这些因素如何塑造谁能够参与，以及他们能够在多大程度上有效地将他们的需求传达给决策者。反思这些发现，我们为正在考虑应用基于偏好的模型参与算法决策的研究人员和实践者提供了启示和前进的道路。
Emerging methods for participatory algorithm design have proposed collecting and aggregating individual stakeholders’ preferences to create algorithmic systems that account for those stakeholders’ values. Drawing on two years of research across two public school districts in the United States, we study how families and school districts use students’ preferences for schools to meet their goals in the context of algorithmic student assignment systems. We find that the design of the preference language, i.e. the structure in which participants must express their needs and goals to the decision-maker, shapes the opportunities for meaningful participation. We define three properties of preference languages – expressiveness, cost, and collectivism – and discuss how these factors shape who is able to participate, and the extent to which they are able to effectively communicate their needs to the decision-maker. Reflecting on these findings, we offer implications and paths forward for researchers and practitioners who are considering applying a preference-based model for participation in algorithmic decision making.