Abstract In this essay, a new approach for the ethical study of emerging technology ethics will be presented, called anticipatory technology ethics (ATE). The ethics of emerging technology is the study of ethical issues at the R&D and introduction stage of technology development through anticipation of possible future devices, applications, and social consequences. I will argue that a major problem for its development is the problem of uncertainty, which can only be overcome through methodologically sound forecasting and futures studies. I (...) will then consider three contemporary approaches to the ethics of emerging technologies that use forecasting: ethical technology assessment, the techno-ethical scenarios approach and the ETICA approach, and I considered their strengths and weaknesses. Based on this critical study, I then present my own approach: ATE. ATE is a conceptually and methodologically rich approach for the ethical analysis of emerging technologies that incorporates a large variety of ethical principles, issues, objects and levels of analysis, and research aims. It is ready to be applied to contemporary and future emerging technologies. Content Type Journal Article Category Original Paper Pages 1-13 DOI 10.1007/s11569-012-0141-7 Authors Philip A. E. Brey, Department of Philosophy, School of Behavioral Sciences, University of Twente, P.O. Box 217, 7500 AE Enschede, The Netherlands Journal NanoEthics Online ISSN 1871-4765 Print ISSN 1871-4757. (shrink)

The problem. The model. Inventory models. Allocation models. Waiting-time models. Replacement models. Competitive models. Testing, control and implementation. Administration of operations research. Index.

To address the ethical challenges in big data health research we propose the concept of systemic oversight. This approach is based on six defining features and aims at creating a common ground across the oversight pipeline of biomedical big data research. Current trends towards enhancing granularity of informed consent and specifying legal provisions to address informational privacy and discrimination concerns in data-driven health research are laudable. However, these solutions alone cannot have the desired impact unless oversight activities by different stakeholders (...) acquire a common substantive orientation. (shrink)

Artificial intelligence and machine learning have the potential to revolutionize the delivery of health care. But designing machine learning‐based decision support systems is not a merely technical challenge. It also requires attention to bioethical principles. As AI and machine learning advance, bioethical frameworks need to be tailored to address the problems that these evolving systems might pose, and the development of these automated systems also needs to be tailored to incorporate bioethical principles.

Recently, there is increased attention to the integration of moral values into the conception, design, and development of emerging IT. The most reviewed approach for this purpose in ethics and technology so far is Value-Sensitive Design (VSD). This article considers VSD as the prime candidate for implementing normative considerations into design. Its methodology is considered from a conceptual, analytical, normative perspective. The focus here is on the suitability of VSD for integrating moral values into the design of technologies in a (...) way that joins in with an analytical perspective on ethics of technology. Despite its promising character, it turns out that VSD falls short in several respects: (1) VSD does not have a clear methodology for identifying stakeholders, (2) the integration of empirical methods with conceptual research within the methodology of VSD is obscure, (3) VSD runs the risk of committing the naturalistic fallacy when using empirical knowledge for implementing values in design, (4) the concept of values, as well as their realization, is left undetermined and (5) VSD lacks a complimentary or explicit ethical theory for dealing with value trade-offs. For the normative evaluation of a technology, I claim that an explicit and justified ethical starting point or principle is required. Moreover, explicit attention should be given to the value aims and assumptions of a particular design. The criteria of adequacy for such an approach or methodology follow from the evaluation of VSD as the prime candidate for implementing moral values in design. (shrink)

From predicting medical conditions to administering health behavior interventions, artificial intelligence technologies are being developed to enhance patient care and outcomes. However, as Mélanie Terrasse and coauthors caution in an article in this issue of the Hastings Center Report, an overreliance on virtual technologies may depersonalize medical interactions and erode therapeutic relationships. The increasing expectation that patients will be actively engaged in their own care, regardless of the patients’ desire, technological literacy, and economic means, may also violate patients’ autonomy and (...) exacerbate access. Moreover, since AI design is both a technical and social process, algorithms may mirror human biases, calling into question the vision of AI technologies surpassing human judgment and avoiding prejudices in decision‐making. The best answer to these problems is to develop AI health technologies as part of a culture of health care quality improvement, responding to existing needs while being proactive about potential technical and ethical problems that can arise from the technologies’ design and implementation. (shrink)

BackgroundSystems medicine is the name for an assemblage of scientific strategies and practices that include bioinformatics approaches to human biology ; “big data” statistical analysis; and medical informatics tools. Whereas personalized and precision medicine involve similar analytical methods applied to genomic and medical record data, systems medicine draws on these as well as other sources of data. Given this distinction, the clinical translation of systems medicine poses a number of important ethical and epistemological challenges for researchers working to generate systems (...) medicine knowledge and clinicians working to apply it.DiscussionThis article focuses on three key challenges: First, we will discuss the conflicts in decision-making that can arise when healthcare providers committed to principles of experimental medicine or evidence-based medicine encounter individualized recommendations derived from computer algorithms. We will explore in particular whether controlled experiments, such as comparative effectiveness trials, should mediate the translation of systems medicine, or if instead individualized findings generated through “big data” approaches can be applied directly in clinical decision-making. Second, we will examine the case of the Riyadh Intensive Care Program Mortality Prediction Algorithm, pejoratively referred to as the “death computer,” to demonstrate the ethical challenges that can arise when big-data-driven scoring systems are applied in clinical contexts. We argue that the uncritical use of predictive clinical algorithms, including those envisioned for systems medicine, challenge basic understandings of the doctor-patient relationship. Third, we will build on the recent discourse on secondary findings in genomics and imaging to draw attention to the important implications of secondary findings derived from the joint analysis of data from diverse sources, including data recorded by patients in an attempt to realize their “quantified self.”SummaryThis paper examines possible ethical challenges that are likely to be raised as systems medicine to be translated into clinical medicine. These include the epistemological challenges for clinical decision-making, the use of scoring systems optimized by big data techniques and the risk that incidental and secondary findings will significantly increase. While some ethical implications remain still hypothetical we should use the opportunity to prospectively identify challenges to avoid making foreseeable mistakes when systems medicine inevitably arrives in routine care. (shrink)