The application of intelligent algorithms in the financial field has significantly improved the efficiency and accuracy of financial services, but it also hides potential algorithmic ethical risks. This article first ...
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ISBN:
(纸本)9789819756742;9789819756759
The application of intelligent algorithms in the financial field has significantly improved the efficiency and accuracy of financial services, but it also hides potential algorithmic ethical risks. This article first explains the application of intelligent algorithms in financial technology, and conducts an in-depth analysis of the ethical issues of intelligent algorithms in the financial field. Secondly, it defines the possible data privacy and security, algorithm discrimination, algorithm distortion, algorithm black box, and large model risks of intelligent algorithms. As well as specific ethical phenomena such as the issue of responsibility attribution, the causes of their occurrence are analyzed. Acknowledging that such dilemmas may pervade the entire life cycle from product development to application, this article elaborates on the possible social impacts and how to carry out the strategies for prevention or governance at various levels-technical, organizational, legal, and professional ethics. Finally, it was emphasized that while pursuing technological innovation and commercial benefits in the financial field, ethical considerations must be balanced, and an outlook was put forward on the development trend of intelligent algorithms in the future financial technology field.
AI technologies have made significant advancements across various sectors, especially healthcare. Although AI algorithms in healthcare showcase remarkable predictive capabilities, apprehensions have emerged owing to e...
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This paper argues that data-driven medicine gives rise to a particular normative challenge. Against the backdrop of a distinction between thegoodand theright, harnessing personal health data towards the development an...
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This paper argues that data-driven medicine gives rise to a particular normative challenge. Against the backdrop of a distinction between thegoodand theright, harnessing personal health data towards the development and refinement of data-driven medicine is to be welcomed from the perspective of thegood. Enacting solidarity drives progress in research and clinical practice. At the same time, such acts of sharing could-especially considering current developments in big data and artificial intelligence-compromise therightby leading to injustices and affecting concrete modes of individual self-determination. In order to address this potential tension, two key elements for ethical reflection on data-driven medicine are proposed: thecontrollabilityof information flows, including technical infrastructures that are conducive towards controllability, and a paradigm shift towardsoutput-orientationin governance and policy.
In recent years, the ethical impact of AI has been increasingly scrutinized, with public scandals emerging over biased outcomes, lack of transparency, and the misuse of data. This has led to a growing mistrust of AI a...
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In recent years, the ethical impact of AI has been increasingly scrutinized, with public scandals emerging over biased outcomes, lack of transparency, and the misuse of data. This has led to a growing mistrust of AI and increased calls for mandated ethical audits of algorithms. Current proposals for ethical assessment of algorithms are either too high level to be put into practice without further guidance, or they focus on very specific and technical notions of fairness or transparency that do not consider multiple stakeholders or the broader social context. In this article, we present an auditing framework to guide the ethical assessment of an algorithm. The audit instrument itself is comprised of three elements: a list of possible interests of stakeholders affected by the algorithm, an assessment of metrics that describe key ethically salient features of the algorithm, and a relevancy matrix that connects the assessed metrics to stakeholder interests. The proposed audit instrument yields an ethical evaluation of an algorithm that could be used by regulators and others interested in doing due diligence, while paying careful attention to the complex societal context within which the algorithm is deployed.
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