算法受众隐私剥夺感的概念及其构成——基于青年群体的扎根理论研究

Research on the Concept and Composition of Algorithmic Audiences’Privacy Deprivation:Analysis of the Grounded Theory Based on Youth Groups

智能算法带来社会福祉的同时也生成了隐私剥夺风险,研究算法受众隐私剥夺感的概念与构成可为新情境下的隐私社会心态问题分析提供基础概念范畴和理论框架。基于扎根理论方法,采用半结构化深度访谈法获取34位青年的经验资料,借助质性分析工具NVivo12.0进行开放式编码、主轴式编码和选择性编码,反复比较并通过理论饱和度检验后提炼出算法受众隐私剥夺感的概念内涵与构成维度。研究认为,算法受众隐私剥夺感是个体在算法隐私资源交换中持续处于不利地位,感知到自身受到多次剥夺且无力抵抗,产生侵入、不安、愤怒、徒劳等负面情绪,并可能表现出“抵抗式”控制自身信息的行为倾向。从内涵上看,算法受众隐私剥夺感本质上是一种个体无力对抗算法隐私剥夺行为的不公平感;从构成上看,其由侵入感、不安感、愤怒感和徒劳感四个维度构成,并存在“认知评估→情感反应→行为倾向”的内在结构关系。对算法受众来说,算法隐私剥夺行为带来的隐私未得、人格受损及抵抗无力让他们萌生出应得、想得的隐私被剥夺的不公平心理,并陷入一种“抵抗无力或徒劳”的状态。

Summary While intelligent algorithms bring in social welfare,it also generate privacy deprivation risks,which may lead to social structural privacy deprivation from the individual level to the community level.On the basis of reviewing existing research,the study examines the concept and composition of algorithmic audiences’privacy deprivation,which can provide a basic conceptual category and theoretical framework for analyzing privacy social mentality issues in new contexts.Based on the grounded theory method,a semi-structured in-depth interview method was used to obtain experiential data from 34 young people.Qualitative analysis tool NVivo12.0 was used to conduct open coding,spindle coding,and selective coding.After repeated comparison and theoretical saturation testing,the conceptual connotation and constituent dimensions of privacy deprivation among the algorithmic audience were extracted.The study suggests that algorithmic audiences’privacy deprivation refers to the persistent sense of disadvantage individuals experience in the exchange of algorithmic privacy resources.They perceive repeated deprivation and feel powerless to resist,leading to negative emotions such as intrusion,anxiety,anger,and a sense of futility.Additionally,they may display a tendency to engage in‘resistant’behaviors to regain control over their personal information.From the perspective of connotation,algorithmic audiences’privacy deprivation is essentially a sense of unfairness in which individuals are unable to resist algorithmic privacy deprivation behavior.From the perspective of composition,privacy deprivation is composed of four dimensions:invasion,anxiety,anger,and futility.Also,there exists an inherent structural relationship of“cognitive evaluation→emotional response→behavioral tendency”.In short,for algorithmic audiences,the privacy loss,personality damage,and inability to resist caused by algorithmic deprivation behavior result in their development of an unfair mentality,feeling deprived of the privacy they deserve or want,and falling into a state of futility or“inability to resist”.This study proposes a conceptual model to understand the new phenomenon or problem of algorithmic audiences’privacy deprivation,which provides a theoretical framework for further research on privacy deprivation in the era of intelligent algorithms.On the one hand,compared with traditional theories related to privacy psychology,privacy deprivation focuses more on the unfair psychology of individual privacy deprivation in the exchange of privacy resources;on the other hand,privacy deprivation is consistent with relative deprivation theory,but its concept puts more emphasize on the structural nature of relative deprivation,meaning that algorithmic audiences’privacy deprivation is essentially due to continuous,multi-level,and cumulative structural deprivation.At the same time,this study suggests that further understanding of privacy psychological risk governance issues can be considered within the“resource deprivation-personality deprivation-behavior deprivation”relationship structure,expanding the scope of privacy psychological behavior research in intelligent algorithm environments,which can provide a new perspective for understanding individual privacy psychology and decision-making,and provide new intellectual support for privacy risk governance.