Ethical Considerations for Language Modeling within Brain-Computer Interfaces

项目概要 机器学习 (ML) 和自然语言处理 (NLP) 具有改变沟通方式的潜力 通过个性化和实时的增强和替代治疗神经退行性疾病患者 通信（AAC）设备。患有严重沟通障碍且无法再控制的人 他们的日常对话或参与前世角色需要 AAC 设备。他们希望他们能够工作—— 可靠、有效、快速。 ML 和 NLP 正在成为连接当前技术和技术的有前途的工具。 为具有最严重言语和身体障碍的个人提供的下一代设备，例如 RSVP Keyboard™，一种由家长资助开发的脑机接口（BCI）。 BCI 系统用于 通信称为 AAC-BCI。 NLP 努力将大型公共数据集与私有数据集结合起来， 例如个人电子邮件信息，承诺为有沟通障碍的个人提供自己的信息 个性化语言模型，这些模型足够强大，可以更接近实时通信。的 然而，专注于让 AAC-BCIs 与机器学习配合使用导致了该领域的严重监督： 对个人为何想要下一代设备以及他们愿意做出哪些权衡了解不够 实现更快、更个性化的沟通。机器学习的转向使这种疏忽变得更加明显。 个人应该提供有关用于构建个人语言模型的数据集的输入，但这 提出了重要的道德问题，涉及个人的价值观、他们如何理解自己的身份以及什么 他们愿意就其个性化通信数据做出权衡。本补充的目标 是为了填补这一理解上的空白，以便研究人员可以将机器学习应用到下一代 AAC-BCI 系统中 以对未来用户的道德问题敏感的方式。这种道德有四个组成部分 补充：（1）针对 BCI 提出的道德问题设计一个道德小插图工具箱 沟通和机器学习； (2) 每月对患有严重疾病的个人进行基于小插曲的在线道德调查 由于运动神经元疾病（例如 ALS）（n=25）或运动障碍（例如， 帕金森病）（n=25）； (3) 对有预兆的个人进行半结构化的小插曲访谈 由于运动神经元疾病 (n=10) 或运动障碍 (n=10) 导致的临床或轻度沟通障碍。 组件 (2) 和 (3) 将采用迭代、并行混合方法。李克特式在线趋势 严重沟通障碍群体中对道德小插曲的反应将用于告知和修改 半结构化访谈提示询问临床前或轻度损伤队列。同时，主题 对访谈的直接内容分析得出的结果将用于完善在线调查问题。结果 将使用迭代、混合方法方法 (4) 概述核心道德领域和初步框架 AAC-BCI 研究人员可以使用工具（插图和讨论提示）来评估道德问题，同时 开发和迭代完善个性化语言模型的通信技术。

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.