Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Ensuring trustworthy use of artificial intelligence and big data analytics in health insurance
75
Zitationen
3
Autoren
2020
Jahr
Abstract
Technological advances in big data (large amounts of highly varied data from many different sources that may be processed rapidly), data sciences and artificial intelligence can improve health-system functions and promote personalized care and public good. However, these technologies will not replace the fundamental components of the health system, such as ethical leadership and governance, or avoid the need for a robust ethical and regulatory environment. In this paper, we discuss what a robust ethical and regulatory environment might look like for big data analytics in health insurance, and describe examples of safeguards and participatory mechanisms that should be established. First, a clear and effective data governance framework is critical. Legal standards need to be enacted and insurers should be encouraged and given incentives to adopt a human-centred approach in the design and use of big data analytics and artificial intelligence. Second, a clear and accountable process is necessary to explain what information can be used and how it can be used. Third, people whose data may be used should be empowered through their active involvement in determining how their personal data may be managed and governed. Fourth, insurers and governance bodies, including regulators and policy-makers, need to work together to ensure that the big data analytics based on artificial intelligence that are developed are transparent and accurate. Unless an enabling ethical environment is in place, the use of such analytics will likely contribute to the proliferation of unconnected data systems, worsen existing inequalities, and erode trustworthiness and trust.
Ähnliche Arbeiten
World Medical Association Declaration of Helsinki: Ethical Principles for Medical Research Involving Human Subjects
2003 · 10.819 Zit.
Estimating the mean and variance from the median, range, and the size of a sample
2005 · 8.975 Zit.
SPIRIT 2013 explanation and elaboration: guidance for protocols of clinical trials
2013 · 6.970 Zit.
The ARRIVE guidelines 2.0: Updated guidelines for reporting animal research
2020 · 5.293 Zit.
The global landscape of AI ethics guidelines
2019 · 4.603 Zit.