Data Subjects' Conceptualizations of and Attitudes Toward Automatic Emotion Recognition-Enabled Wellbeing Interventions on Social Media
Data Subjects' Conceptualizations of and Attitudes Toward Automatic Emotion Recognition-Enabled Wellbeing Interventions on Social Media
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数据主体对社交媒体上自动情绪识别支持的健康干预措施的概念和态度
DOI：
10.1145/3476049
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发表时间：
2021
影响因子：
--
通讯作者：
Andalibi, Nazanin
中科院分区：
文献类型：
--
作者：
Roemmich, Kat;Andalibi, Nazanin
支持自动情绪识别（ER）的健康干预使用ER算法来推断数据主体的情绪（即，根据在线互动（如社交媒体活动）产生的数据，收集或处理数据以启用ER的人），并进行相应的干预。这项技术的潜在商业应用得到了广泛认可，特别是在社交媒体的背景下。然而，数据主体对自动急诊室福利干预的概念和态度知之甚少。为了解决这一差距，我们就基于社交媒体的自动急诊室健康干预措施采访了13名美国成人社交媒体数据主体。我们发现参与者对自动急诊室福利干预的态度主要是消极的。消极态度很大程度上取决于参与者如何将他们对人工智能（AI）的概念与传统上提供幸福支持的人类进行比较。人工智能与人类福祉干预之间的比较基于参与者怀疑人工智能是否具备的人类属性：1)乐于助人和真实的关怀；2)个人和专业技能；3)道德;4)仁爱源于共同的人性。在某些情况下，当参与者将自动ER-enabled幸福干预对他人的影响概念化时，参与者对自动ER-enabled幸福干预的态度发生了转变，而不是对自己的影响。尽管不情愿，少数参与者对他们的自动急诊室福利干预的概念持更积极的态度，认为它们有可能造福他人：1)支持学术研究；2)增加获得福利支持的机会；3)通过过度伤害预防。然而，大多数参与者预计到与他们对他人的自动急诊室福利干预的概念相关的危害，例如再创伤，不准确的健康信息的传播，不适当的监测以及由不准确的预测告知的干预。最后，虽然参与者对自动ER-enabled幸福干预有疑虑，但我们确定了自动ER-enabled幸福干预的三个开发和交付质量，这取决于他们对它们的态度：1)准确性；2)语境敏感性；3)积极的结果。我们的研究并不是为了对是否应该存在或如何存在自动急诊室福利干预做出规范性陈述，而是为了集中受这项技术影响的数据主体的声音。我们主张将数据主体纳入道德和可信赖的ER应用程序的开发要求中。为此，我们讨论了我们研究结果的伦理、社会和政策含义，表明参与者想象的自动急诊室福祉干预措施与在美国当前的实践和监管环境中促进值得信赖、具有社会意识和负责任的人工智能技术的目标是不相容的。
Automatic emotion recognition (ER)-enabled wellbeing interventions use ER algorithms to infer the emotions of a data subject (i.e., a person about whom data is collected or processed to enable ER) based on data generated from their online interactions, such as social media activity, and intervene accordingly. The potential commercial applications of this technology are widely acknowledged, particularly in the context of social media. Yet, little is known about data subjects' conceptualizations of and attitudes toward automatic ER-enabled wellbeing interventions. To address this gap, we interviewed 13 US adult social media data subjects regarding social media-based automatic ER-enabled wellbeing interventions. We found that participants' attitudes toward automatic ER-enabled wellbeing interventions were predominantly negative. Negative attitudes were largely shaped by how participants compared their conceptualizations of Artificial Intelligence (AI) to the humans that traditionally deliver wellbeing support. Comparisons between AI and human wellbeing interventions were based upon human attributes participants doubted AI could hold: 1) helpfulness and authentic care; 2) personal and professional expertise; 3) morality; and 4) benevolence through shared humanity. In some cases, participants' attitudes toward automatic ER-enabled wellbeing interventions shifted when participants conceptualized automatic ER-enabled wellbeing interventions' impact on others, rather than themselves. Though with reluctance, a minority of participants held more positive attitudes toward their conceptualizations of automatic ER-enabled wellbeing interventions, citing their potential to benefit others: 1) by supporting academic research; 2) by increasing access to wellbeing support; and 3) through egregious harm prevention. However, most participants anticipated harms associated with their conceptualizations of automatic ER-enabled wellbeing interventions for others, such as re-traumatization, the spread of inaccurate health information, inappropriate surveillance, and interventions informed by inaccurate predictions. Lastly, while participants had qualms about automatic ER-enabled wellbeing interventions, we identified three development and delivery qualities of automatic ER-enabled wellbeing interventions upon which their attitudes toward them depended: 1) accuracy; 2) contextual sensitivity; and 3) positive outcome. Our study is not motivated to make normative statements about whether or how automatic ER-enabled wellbeing interventions should exist, but to center voices of the data subjects affected by this technology. We argue for the inclusion of data subjects in the development of requirements for ethical and trustworthy ER applications. To that end, we discuss ethical, social, and policy implications of our findings, suggesting that automatic ER-enabled wellbeing interventions imagined by participants are incompatible with aims to promote trustworthy, socially aware, and responsible AI technologies in the current practical and regulatory landscape in the US.
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