AI, big data, and the future of consent

被引:44
|
作者
Andreotta, Adam J. [1 ]
Kirkham, Nin [2 ]
Rizzi, Marco [3 ]
机构
[1] Curtin Univ, Sch Management, Kent St, Bentley, WA 6102, Australia
[2] Univ Western Australia, Dept Philosophy, 35 Stirling Hwy, Crawley, WA 6009, Australia
[3] Univ Western Australia, UWA Law Sch, 35 Stirling Hwy, Crawley, WA 6009, Australia
关键词
Big data; AI; Privacy; Informed consent; Moral responsibility; INFORMED-CONSENT; CONTRACTS;
D O I
10.1007/s00146-021-01262-5
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In this paper, we discuss several problems with current Big data practices which, we claim, seriously erode the role of informed consent as it pertains to the use of personal information. To illustrate these problems, we consider how the notion of informed consent has been understood and operationalised in the ethical regulation of biomedical research (and medical practices, more broadly) and compare this with current Big data practices. We do so by first discussing three types of problems that can impede informed consent with respect to Big data use. First, we discuss the transparency (or explanation) problem. Second, we discuss the re-repurposed data problem. Third, we discuss the meaningful alternatives problem. In the final section of the paper, we suggest some solutions to these problems. In particular, we propose that the use of personal data for commercial and administrative objectives could be subject to a 'soft governance' ethical regulation, akin to the way that all projects involving human participants (e.g., social science projects, human medical data and tissue use) are regulated in Australia through the Human Research Ethics Committees (HRECs). We also consider alternatives to the standard consent forms, and privacy policies, that could make use of some of the latest research focussed on the usability of pictorial legal contracts.
引用
收藏
页码:1715 / 1728
页数:14
相关论文
共 50 条
  • [31] The AI-Powered Evolution of Big Data
    Kumar, Yulia
    Marchena, Jose
    Awlla, Ardalan H.
    Li, J. Jenny
    Abdalla, Hemn Barzan
    APPLIED SCIENCES-BASEL, 2024, 14 (22):
  • [32] Situating methods in the magic of Big Data and AI
    Elish, M. C.
    Boyd, Danah
    COMMUNICATION MONOGRAPHS, 2018, 85 (01) : 57 - 80
  • [33] AI in Medicine: Big Data Remains a Challenge
    Lin, Ming-Chin
    Iqbal, Usman
    Li, Yu-Chuan
    COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, 2018, 164 : A1 - A1
  • [34] Benefits and Challenges in the Use of Big Data and AI
    Bacon, Liz
    2018 19TH IEEE/ACIS INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING, ARTIFICIAL INTELLIGENCE, NETWORKING AND PARALLEL/DISTRIBUTED COMPUTING (SNPD), 2018, : 1 - 1
  • [35] Big data and AI for gender equality in health: bias is a big challenge
    Joshi, Anagha
    FRONTIERS IN BIG DATA, 2024, 7
  • [36] Joint Editorial: Informed Consent and AI Transcription of Qualitative Data
    Samuel, Gabrielle
    Wassenaar, Doug
    JOURNAL OF EMPIRICAL RESEARCH ON HUMAN RESEARCH ETHICS, 2024,
  • [37] Rethinking Patient Consent in the Era of Artificial Intelligence and Big Data
    Kotsenas, Amy L.
    Balthazar, Patricia
    Andrews, David
    Geis, J. Raymond
    Cook, Tessa S.
    JOURNAL OF THE AMERICAN COLLEGE OF RADIOLOGY, 2021, 18 (01) : 180 - 184
  • [38] Open Medical Big Data and Open Consent and their Impact on Privacy
    Li, Jingquan
    2017 IEEE 6TH INTERNATIONAL CONGRESS ON BIG DATA (BIGDATA CONGRESS 2017), 2017, : 511 - 514
  • [39] Demand for Future Skills: Education on AI in Comprehensive Digital Business Development, Big Data Analytics, and Ubiquitous Approach to Data in Business
    Zagar, Martin
    Samardzija, Jasminka
    Mestrovic, Ana Havelka
    Amer, Muhieddin
    Mounsef, Jinane
    SMART TECHNOLOGIES FOR A SUSTAINABLE FUTURE, VOL 1, STE 2024, 2024, 1027 : 114 - 121
  • [40] Delegating consent to AI
    Paul Hellyer
    British Dental Journal, 2023, 235 (11) : 878 - 878