Switch to: References

Add citations

You must login to add citations.
  1. Medical AI and human dignity: Contrasting perceptions of human and artificially intelligent (AI) decision making in diagnostic and medical resource allocation contexts.Paul Formosa, Wendy Rogers, Yannick Griep, Sarah Bankins & Deborah Richards - 2022 - Computers in Human Behaviour 133.
    Forms of Artificial Intelligence (AI) are already being deployed into clinical settings and research into its future healthcare uses is accelerating. Despite this trajectory, more research is needed regarding the impacts on patients of increasing AI decision making. In particular, the impersonal nature of AI means that its deployment in highly sensitive contexts-of-use, such as in healthcare, raises issues associated with patients’ perceptions of (un) dignified treatment. We explore this issue through an experimental vignette study comparing individuals’ perceptions of being (...)
    Download  
     
    Export citation  
     
    Bookmark  
  • An Ethics Framework for Big Data in Health and Research.Vicki Xafis, G. Owen Schaefer, Markus K. Labude, Iain Brassington, Angela Ballantyne, Hannah Yeefen Lim, Wendy Lipworth, Tamra Lysaght, Cameron Stewart, Shirley Sun, Graeme T. Laurie & E. Shyong Tai - 2019 - Asian Bioethics Review 11 (3):227-254.
    Ethical decision-making frameworks assist in identifying the issues at stake in a particular setting and thinking through, in a methodical manner, the ethical issues that require consideration as well as the values that need to be considered and promoted. Decisions made about the use, sharing, and re-use of big data are complex and laden with values. This paper sets out an Ethics Framework for Big Data in Health and Research developed by a working group convened by the Science, Health and (...)
    Download  
     
    Export citation  
     
    Bookmark   19 citations  
  • Professional expectations and patient expectations concerning the development of Artificial Intelligence (AI) for the early diagnosis of Pulmonary Hypertension (PH).Peter Winter & Annamaria Carusi - 2022 - Journal of Responsible Technology 12 (C):100052.
    Download  
     
    Export citation  
     
    Bookmark  
  • Delivering a Practical Framework for Ethical Decision-Making Involving Big Data in Health and Research.Graeme T. Laurie & E. Shyong Tai - 2019 - Asian Bioethics Review 11 (3):223-225.
    Download  
     
    Export citation  
     
    Bookmark   1 citation  
  • AI led ethical digital transformation: framework, research and managerial implications.Kumar Saurabh, Ridhi Arora, Neelam Rani, Debasisha Mishra & M. Ramkumar - 2022 - Journal of Information, Communication and Ethics in Society 20 (2):229-256.
    Purpose Digital transformation leverages digital technologies to change current processes and introduce new processes in any organisation’s business model, customer/user experience and operational processes. Artificial intelligence plays a significant role in achieving DT. As DT is touching each sphere of humanity, AI led DT is raising many fundamental questions. These questions raise concerns for the systems deployed, how they should behave, what risks they carry, the monitoring and evaluation control we have in hand, etc. These issues call for the need (...)
    Download  
     
    Export citation  
     
    Bookmark   3 citations  
  • Ethical Data Collection for Medical Image Analysis: a Structured Approach.S. T. Padmapriya & Sudhaman Parthasarathy - 2023 - Asian Bioethics Review 16 (1):95-108.
    Due to advancements in technology such as data science and artificial intelligence, healthcare research has gained momentum and is generating new findings and predictions on abnormalities leading to the diagnosis of diseases or disorders in human beings. On one hand, the extensive application of data science to healthcare research is progressing faster, while on the other hand, the ethical concerns and adjoining risks and legal hurdles those data scientists may face in the future slow down the progression of healthcare research. (...)
    Download  
     
    Export citation  
     
    Bookmark  
  • The decision-point-dilemma: Yet another problem of responsibility in human-AI interaction.Laura Crompton - 2021 - Journal of Responsible Technology 7:100013.
    AI as decision support supposedly helps human agents make ‘better’decisions more efficiently. However, research shows that it can, sometimes greatly, influence the decisions of its human users. While there has been a fair amount of research on intended AI influence, there seem to be great gaps within both theoretical and practical studies concerning unintended AI influence. In this paper I aim to address some of these gaps, and hope to shed some light on the ethical and moral concerns that arise (...)
    Download  
     
    Export citation  
     
    Bookmark   1 citation  
  • Consideration and Disclosure of Group Risks in Genomics and Other Data-Centric Research: Does the Common Rule Need Revision?Carolyn Riley Chapman, Gwendolyn P. Quinn, Heini M. Natri, Courtney Berrios, Patrick Dwyer, Kellie Owens, Síofra Heraty & Arthur L. Caplan - forthcoming - American Journal of Bioethics:1-14.
    Harms and risks to groups and third-parties can be significant in the context of research, particularly in data-centric studies involving genomic, artificial intelligence, and/or machine learning technologies. This article explores whether and how United States federal regulations should be adapted to better align with current ethical thinking and protect group interests. Three aspects of the Common Rule deserve attention and reconsideration with respect to group interests: institutional review board (IRB) assessment of the risks/benefits of research; disclosure requirements in the informed (...)
    Download  
     
    Export citation  
     
    Bookmark   1 citation  
  • Scoping Review Shows the Dynamics and Complexities Inherent to the Notion of “Responsibility” in Artificial Intelligence within the Healthcare Context.Sarah Bouhouita-Guermech & Hazar Haidar - 2024 - Asian Bioethics Review 16 (3):315-344.
    The increasing integration of artificial intelligence (AI) in healthcare presents a host of ethical, legal, social, and political challenges involving various stakeholders. These challenges prompt various studies proposing frameworks and guidelines to tackle these issues, emphasizing distinct phases of AI development, deployment, and oversight. As a result, the notion of responsible AI has become widespread, incorporating ethical principles such as transparency, fairness, responsibility, and privacy. This paper explores the existing literature on AI use in healthcare to examine how it addresses, (...)
    Download  
     
    Export citation  
     
    Bookmark  
  • Beyond ideals: why the (medical) AI industry needs to motivate behavioural change in line with fairness and transparency values, and how it can do it.Alice Liefgreen, Netta Weinstein, Sandra Wachter & Brent Mittelstadt - 2024 - AI and Society 39 (5):2183-2199.
    Artificial intelligence (AI) is increasingly relied upon by clinicians for making diagnostic and treatment decisions, playing an important role in imaging, diagnosis, risk analysis, lifestyle monitoring, and health information management. While research has identified biases in healthcare AI systems and proposed technical solutions to address these, we argue that effective solutions require human engagement. Furthermore, there is a lack of research on how to motivate the adoption of these solutions and promote investment in designing AI systems that align with values (...)
    Download  
     
    Export citation  
     
    Bookmark   1 citation  
  • Life and Death Decisions and COVID‐19: Investigating and Modeling the Effect of Framing, Experience, and Context on Preference Reversals in the Asian Disease Problem.Shashank Uttrani, Neha Sharma & Varun Dutt - 2022 - Topics in Cognitive Science 14 (4):800-824.
    Prior research in judgment and decision making (JDM) has investigated the effect of problem framing on human preferences. Furthermore, research in JDM documented the absence of such reversal of preferences when making decisions from experience. However, little is known about the effect of context on preferences under the combined influence of problem framing and problem format. Also, little is known about how cognitive models would account for human choices in different problem frames and types (general/specific) in the experience format. One (...)
    Download  
     
    Export citation  
     
    Bookmark