Petelka Justin, Van Kleunen Lucy, Albright Liam, Murnane Elizabeth, Voida Stephen, Snyder Jaime
Information School, University of Washington.
Department of Computer Science, University of Colorado Boulder.
Proc SIGCHI Conf Hum Factor Comput Syst. 2020 Apr;2020. doi: 10.1145/3313831.3376573.
Research in personal informatics (PI) calls for systems to support social forms of tracking, raising questions about how privacy can and should support intentionally sharing sensitive health information. We focus on the case of personal data related to the self-tracking of bipolar disorder (BD) in order to explore the ways in which disclosure activities intersect with other privacy experiences. While research in HCI often discusses privacy as a disclosure activity, this does not reflect the ways in which privacy can be passively experienced. In this paper we broaden conceptions of privacy by defining experiences and contributing factors in contrast to disclosure activities and preferences. Next, we ground this theoretical move in empirical analysis of personal narratives shared by people managing BD. We discuss the resulting emergent model of transparency in terms of implications for the design of socially-enabled PI systems. CAUTION: This paper contains references to experiences of mental illness, including self-harm, depression, suicidal ideation, etc.
个人信息学(PI)研究要求系统支持社交形式的跟踪,这引发了关于隐私如何以及应该如何支持有意共享敏感健康信息的问题。我们聚焦于与双相情感障碍(BD)自我跟踪相关的个人数据案例,以探索披露活动与其他隐私体验相交的方式。虽然人机交互(HCI)研究常常将隐私视为一种披露活动,但这并未反映出隐私可以被被动体验的方式。在本文中,我们通过定义与披露活动和偏好形成对比的体验及促成因素,拓宽了对隐私的概念理解。接下来,我们通过对管理BD的人们所分享的个人叙述进行实证分析,为这一理论举措奠定基础。我们从对具备社交功能的PI系统设计的影响方面,讨论由此产生的透明度新兴模型。注意:本文包含对精神疾病经历的提及,包括自我伤害、抑郁、自杀意念等。