Justifying a Capability Approach to Brain Computer Interface

被引：0
|
作者：
Nancy S. Jecker
Andrew Ko
机构：
[1] University of Washington School of Medicine,Department of Bioethics and Humanities
[2] Chinese University of Hong Kong,Faculty of Medicine, Centre for Bioethics
[3] University of Johannesburg,Department of Philosophy
[4] University of Washington School of Medicine,Department of Neurological Surgery
来源：
Philosophy & Technology | 2023年 / 36卷 / 1期
关键词：
Artificial intelligence; Brain-computer interface; Ethics; Neurotechnology; Human dignity; Human capabilities;
D O I：
10.1007/s13347-022-00603-6
中图分类号：
学科分类号：
摘要：
Previously, we introduced a capability approach to assess the responsible use of brain-computer interface. In this commentary, we say more about the ethical basis of our capability view and respond to three objections. The first objection holds that by stressing that capability lists are provisional and subject to change, we threaten the persistence of human dignity, which is tied to capabilities. The second objection states that we conflate capabilities and abilities. The third objection claims that the goal of using neuroenhancements should be preserving capabilities, not altering them.
引用
收藏
相关论文
共 50 条
  • [21] MICROPROGRAMMED COMPUTER INTERFACE WITH ERROR DETECTION AND CORRECTION CAPABILITY
    LEWIS, JW
    REVIEW OF SCIENTIFIC INSTRUMENTS, 1974, 45 (11): : 1457 - 1459
  • [22] Network Brain-Computer Interface (nBCI): An Alternative Approach for Cognitive Prosthetics
    Buch, Vivek P.
    Richardson, Andrew G.
    Brandon, Cameron
    Stiso, Jennifer
    Khattak, Monica N.
    Bassett, Danielle S.
    Lucas, Timothy H.
    FRONTIERS IN NEUROSCIENCE, 2018, 12
  • [23] A Novel Brain-Computer Interface Approach Designed for Dynamic Target Selection
    Liu, Yaru
    Liu, Yadong
    Yu, Yang
    Jiang, Jun
    Zhou, Zongtan
    Hu, Dewen
    2016 8TH INTERNATIONAL CONFERENCE ON INTELLIGENT HUMAN-MACHINE SYSTEMS AND CYBERNETICS (IHMSC), VOL. 2, 2016, : 217 - 220
  • [24] An Approach to Minimize Calibration Time for Brain Computer Interface Based on Motion Imagery
    Zou Yijun
    Zhao Xingang
    Xu Weiliang
    Han Jianda
    2017 IEEE 7TH ANNUAL INTERNATIONAL CONFERENCE ON CYBER TECHNOLOGY IN AUTOMATION, CONTROL, AND INTELLIGENT SYSTEMS (CYBER), 2017, : 484 - 489
  • [25] Brain Computer Interface Approach using Sensor Covariance Matrix with Forced Whitening
    Shin, Hyuksoo
    Chung, Wonzoo
    2017 5TH INTERNATIONAL WINTER CONFERENCE ON BRAIN-COMPUTER INTERFACE (BCI), 2017, : 66 - 68
  • [26] A Novel Multimodal Approach for Hybrid Brain&x2013;Computer Interface
    Sun, Zhe
    Huang, Zihao
    Duan, Feng
    Liu, Yu
    IEEE ACCESS, 2020, 8 : 89909 - 89918
  • [27] Factorization Approach for Sparse Spatio-Temporal Brain-Computer Interface
    Lee, Byeong-Hoo
    Cho, Jeong-Hyun
    Kwon, Byoung-Hee
    Lee, Seong-Whan
    2022 26TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR), 2022, : 1090 - 1097
  • [28] Brain-Computer Interface with Corrupted EEG Data: a Tensor Completion Approach
    J. Solé-Casals
    C. F. Caiafa
    Q. Zhao
    A. Cichocki
    Cognitive Computation, 2018, 10 : 1062 - 1074
  • [29] Brain-Computer Interface with Corrupted EEG Data: a Tensor Completion Approach
    Sole-Casals, J.
    Caiafa, C. F.
    Zhao, Q.
    Cichocki, A.
    COGNITIVE COMPUTATION, 2018, 10 (06) : 1062 - 1074
  • [30] Filter Bank Feature Combination (FBFC) approach for Brain-Computer Interface
    Chin, Zheng Yang
    Ang, Kai Keng
    Guan, Cuntai
    Wang, Chuanchu
    Zhang, Haihong
    2011 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2011, : 1352 - 1357
12345