Doctoral Dissertation Research Improvement Grants: An Anthropological Examination of Algorithmic Recommendation System Design

联合PI Nicholas Seaver将在PI William Maurer的监督下，探索影响算法推荐系统设计的因素。研究人员询问算法分类如何受到社会文化、法律和经济背景的影响，以及工程师的文化理论是如何在代码中形成和操作的。这个项目试图通过音乐推荐的案例研究来回答这些问题，音乐推荐是算法推荐系统中比较活跃的研究和开发领域之一。特别是，它调查了推荐系统的设计者如何在关于算法的形式化的、定量的本质和偏好的主观的、“文化的”本质之间进行调解。随着曾经被认为是人类的领域变得越来越与计算逻辑交织在一起，人类学家处于独特的地位，考虑如何定义、调解和解决这些紧张关系，特别是在专家知识社区中，这些社区将特定的决议构建成广泛影响的技术系统。数据将通过在美国几个学术和行业地点的人种学田野工作来收集，在那里开发和实施表示数据和产生建议的技术。这项实地工作包括参与者观察和对参与这些系统生产的工程师、研究人员、管理人员和实习生的深入、半结构化访谈。这项研究的结果将有助于公众和学术界关于算法过滤的功能和效果的辩论，以及对计算、工作和分类实践的人类学理解。该项目有助于培养一名人类学研究生。这些发现将帮助工程师认识到他们构建在技术基础设施中的文化范式，从而为新算法系统的设计提供信息。

The Co-PI, Nicholas Seaver, under the supervision of the PI, William Maurer, will explore the factors that influence the design of algorithmic recommendation systems. The researcher asks how algorithmic classification is influenced by sociocultural, legal, and economic contexts, as well as how the cultural theories of engineers are formed and operationalized in code. This project seeks to answer these questions through a case study in music recommendation, one of the more active research and development areas in algorithmic recommender systems. In particular, it investigates how the designers of recommender systems mediate between ideas about the formalized, quantitative nature of algorithms and the subjective, "cultural" nature of preference. As domains once thought to be ineffably human become more entwined with logics of computation, anthropologists are uniquely positioned to consider how these tensions are defined, mediated, and resolved, particularly within expert knowledge communities which build specific resolutions into widely influential technical systems.Data will be collected through ethnographic fieldwork at several academic and industry sites in the US, where techniques for representing data and producing recommendations are developed and implemented. This fieldwork includes participant-observation and in-depth, semi-structured interviews with engineers, researchers, managers and interns involved in the production of these systems.Findings from this research will contribute to public and academic debates about the functions and effects of algorithmic filtering, as well as to the anthropological understanding of computation, work, and classificatory practice. The project contributes to the training of a graduate student in anthropology. The findings will inform the design of new algorithmic systems by helping engineers be cognizant of the cultural paradigms they build into technical infrastructures.