Ethical Considerations for Language Modeling within Brain-Computer Interfaces

项目摘要 机器学习和自然语言处理有可能改变交流方式 针对神经退行性疾病患者通过个性化和实时增强和替代 通信(AAC)设备。有严重沟通障碍的个人，不能再控制 他们的日常对话或参与前世生活的角色都需要AAC设备。他们想让他们工作--去 要可靠、高效、快速。ML和NLP正在成为连接当前技术和 下一代设备，适用于语音和身体障碍最严重的个人，如RSVP 键盘™，家长格兰特正在开发的脑机接口(BCI)。脑-机接口系统 通信被称为AAC-BCI。NLP努力将大型公共数据集与私有数据集相结合， 例如个人电子邮件，承诺向有沟通障碍的人提供他们自己的 个性化的语言模型，足够健壮的模型，可以更接近实时交流。这个 然而，专注于让AAC-BCI与机器学习一起工作，导致了该领域的一个关键疏忽： 对个人为什么想要下一代设备以及他们愿意进行哪些权衡理解不足 以实现更快、更个性化的交流。转向ML使这种疏忽突显出来。 个人应该提供关于用于构建其个人语言模型的数据集的输入，但这 提出了重要的伦理问题，如个人重视什么，他们如何理解自己的身份，以及 他们愿意与他们的个性化通信数据进行权衡。本补充文件的目的是 是为了填补这一认识上的空白，以便研究人员能够将ML实现到下一代AAC-BCI系统中 以一种对未来用户的道德关切敏感的方式。这一道德规范有四个组成部分 附录：(1)设计一个针对BCI和BCI提出的伦理问题量身定做的伦理小插曲工具箱 传播和ML；(2)每月对患有严重疾病的个人进行基于Vignette的在线道德调查 运动神经元病(如肌萎缩侧索硬化症)(n=25)或运动障碍(如， 帕金森氏病)(n=25)；(3)对患有前驱疾病的个人进行半结构化的小插曲访谈 运动神经元病(n=10)或运动障碍(n=10)引起的临床或轻度沟通障碍。 构成部分(2)和(3)将采用迭代、并行的混合方法。利克特风格的在线趋势 将使用对严重沟通障碍队列中的道德小插曲的回应来告知和修改 半结构化访谈提示询问临床前或轻度损害队列。同时，主题 从访谈的直接内容分析中产生的问题将被用于提炼在线调查问题。这样做的结果是 将使用迭代的混合方法(4)勾勒出核心伦理领域的框架和初步的 AAC-BCI研究人员可用来评估伦理问题的工具(小插曲和讨论提示) 开发和迭代提炼个性化语言模型的交流技术。

Project Summary Machine learning (ML) and Natural Language Processing (NLP) have the potential to transform communication for patients with neurodegenerative disease through personalized and real-time augmentative and alternative communication (AAC) devices. Individuals with severe communication impairments who can no longer control their daily conversations or participate in previous life roles want AAC devices. And they want them to work – to be reliable, effective, and fast. ML and NLP are emerging as promising tools to bridge current technology and next generation devices for individuals with the most severe speech and physical impairments, like the RSVP Keyboard™, a brain-computer interface (BCI) being developed by the parent grant. BCI systems for communication are referred to as AAC-BCIs. NLP efforts to combine large public data sets with private data sets, such as personal email messages, promise to give individuals with communication impairments their own personalized language models, models that are sufficiently robust to get closer to real-time communication. The focus on getting AAC-BCIs to work with machine learning, however, has led to a critical oversight in the field: an inadequate understanding of why individuals want next-generation devices and what trade-offs they are willing to make for faster and more personalized communication. The turn to ML brings this oversight into sharp relief. Individuals should provide input about the data sets used to construct their personal language models, but this raises important ethical questions about what individuals value, how they understand their identity, and what trade-offs they are willing to make relative to their personalized communication data. The goal of this supplement is to fill this gap in understanding so that researchers can implement ML into next generation AAC-BCI systems in a way that is sensitive to the ethical concerns of future users. There are four components to this ethics supplement: (1) to design a toolbox of ethics vignettes tailored to ethical concerns raised by both BCI communication and ML; (2) to administer monthly vignette-based online ethics surveys to individuals with severe communication impairments due to motor neuron disease (e.g., ALS) (n=25) or movement disorders (e.g., Parkinson's disease) (n=25); (3) to conduct semi-structured vignette-based interviews with individuals with pre- clinical or mild communication impairment due to motor neuron disease (n=10) or movement disorder (n=10). Components (2) and (3) will employ an iterative, parallel mixed-method approach. Trends in Likert-style online responses to ethics vignettes in the severe communication impairment cohort will be used to inform and modify the semi-structured interview prompts asked of the pre-clinical or mild impairment cohort. In parallel, themes emerging from direct content analysis of interviews will be used to refine online survey questions. Results of this iterative, mix-methods approach will be used (4) to outline a framework of core ethical domains and preliminary tools (vignettes and discussion prompts) that AAC-BCI researchers can use to assess ethical concerns while developing and iteratively refining communication technology for personalized language models.