Ethics and Machine Learning: Adolescent Suicide Prediction on Social Media

伦理与机器学习：社交媒体上的青少年自杀预测

• 批准号: 2274620
• 金额: --
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2274620
• 关键词: Ethics; Machine; Learning; Adolescent; Suicide

Suicide is a leading cause of death for British adolescents aged 10-24 (Public Health England, 2018). Identifying adolescents who are at increased risk of suicide is a major challenge for clinicians because traditional assessment instruments (e.g. the Scale for Suicide Ideation and Beck's Suicide IntentScale) have low Positive Prediction Value (PPV; Runeson et al., 2017). The rapid growth of social media combined with its increasing integration into the daily lives of adolescents presents researchers with new opportunities for predicting suicide risk through new sources of behavioral data (e.g. McClellan, Ali,Mutter, Kroutil, & Landwehr, 2017; Torous et al., 2018). This process is enabled by Machine Learning Algorithms (MLAs), which use inductive methods to generate new theories about patterns found in social media datasets (McClellan et al., 2017). These methods are used by universities, hospitals, and socialmedia companies. In the United States, Facebook uses MLAs to scan accounts for people at-risk of suicide (Kaste, 2018). While their algorithm is undisclosed, if it deems a person to be at "imminent risk" of suicide Facebook will alert local emergency responders, who may in turn visit the person for a wellnesscheck. In 2017 Facebook reportedly alerted local emergency responders over 3,500 times (Kaste, 2018). Ethicists have studied the concerns that may arise when adults are deemed "at-risk" of suicide by algorithms, however their exploration remains theoretical (Gomes de Andrade, Pawson, Muriello,Donahue, & Guadagno, 2018; McKernan, Clayton, & Walsh, 2018; Tucker, Tackett, Glickman, & Reger, 2018). In addition, there have been no comparable discussions or empirical studies focusing on the impact of this practice on adolescents. Using MLAs with social media data may cause unique ethical concerns foradolescents who spend more time on social media and use a greater variety of platforms compared to their adult counterparts (Smith & Anderson, 2018). In addition, suicide risk and attempts differ in form and fatality between adolescents and adults (Parellada et al., 2008). Legislation on consent to medical practicesmay be another cause of age-related differences, as young people under the age of 16 can only provideindependent consent proportionate to their competence and therefore are not automatically entitled to give consent to medical practices (Gillick v. West Norfolk and Wisbech AHA, 1985). As such, a legal guardian may be making decisions on their child's behalf. Considering the range of ethical issues within this context is fundamental to minimize the potential for harm and maximize any benefits of interventions for adolescents. Therefore, the aim of my DPhil within the Department of Psychiatry is to study the ethical questions that may arise when adolescents are targeted by algorithm-based suicide predictions and interventions. Most of the ethics literature on suicide prediction with adults uses a principle-driven approach and three overarching bioethical principles: autonomy, beneficence, and privacy (Gomes de Andrade et al., 2018; McKernan et al., 2018; Tucker et al., 2018). Within the first phase of my DPhil, I will create a bioethics framework founded on these principles for the use of MLAs in adolescent suicide prediction on social media.

自杀是英国10-24岁青少年死亡的主要原因（英国公共卫生，2018）。识别自杀风险增加的青少年是临床医生面临的主要挑战，因为传统的评估工具（例如自杀意念量表和贝克自杀意图量表）具有较低的阳性预测值（PPV; Runeson等人，2017年）。社交媒体的快速发展及其日益融入青少年的日常生活，为研究人员提供了通过新的行为数据来源预测自杀风险的新机会（例如McClellan，Ali，Mutter，Kroutil和Landwehr，2017; Torous等人，2018年）。该过程由机器学习算法（MLA）实现，其使用归纳方法来生成关于在社交媒体数据集中发现的模式的新理论（McClellan等人，2017年）。这些方法被大学、医院和社交媒体公司使用。在美国，Facebook使用MLAs来扫描有自杀风险的人的账户（Kaste，2018）。虽然他们的算法尚未公开，但如果Facebook认为一个人有自杀的“迫在眉睫的风险”，它会提醒当地的紧急救援人员，他们可能会反过来访问这个人进行健康检查。据报道，2017年Facebook向当地紧急救援人员发出了超过3,500次警报（Kaste，2018）。伦理学家已经研究了当成年人被算法视为“有自杀风险”时可能出现的问题，但他们的探索仍然是理论性的（Gomes de Andrade，Pawson，Muriello，Donahue，& Guadagno，2018; McKernan，克莱顿，&沃尔什，2018;塔克，塔克特，Glickman，& Reger，2018）。此外，还没有类似的讨论或实证研究，侧重于这种做法对青少年的影响。使用MLAs与社交媒体数据可能会导致独特的道德问题，因为与成年人相比，青少年在社交媒体上花费更多时间并使用更多种类的平台（Smith &安德森，2018）。此外，青少年和成人之间的自杀风险和企图在形式和死亡率上不同（Parellada等人，2008年）。关于同意医疗行为的立法可能是与年龄有关的差异的另一个原因，因为16岁以下的年轻人只能提供与其能力相称的独立同意，因此不能自动有权同意医疗行为（Gillick诉West诺福克和维斯贝赫AHA，1985年）。因此，法律的监护人可能会代表他们的孩子做出决定。在这种情况下考虑一系列道德问题是至关重要的，以尽量减少潜在的伤害和最大限度地提高任何好处的干预措施，为青少年。因此，我在精神病学系的博士学位的目的是研究当青少年被基于算法的自杀预测和干预作为目标时可能出现的伦理问题。大多数关于成人自杀预测的伦理学文献都使用原则驱动的方法和三个首要的生物伦理学原则：自主，仁慈和隐私（Gomes de Andrade et al.，2018; McKernan等人，2018; Tucker等人，2018年）。在我的博士学位的第一阶段，我将创建一个基于这些原则的生物伦理学框架，用于在社交媒体上预测青少年自杀。