In the digital era, corporate social responsibility (CSR) has become a crucial factor influencing consumer purchase behavior. To investigate the mechanism through which CSR affects customer purchase intention (CPI) and to examine the mediating role o...
Should developers be held responsible for the predictions of their neural networks-and if not, does that introduce a responsibility gap? The claim that neural networks introduce a responsibility gap has seen significant pushback, with philosophers ar...
It is commonly accepted that clinicians are ethically obligated to disclose their use of medical machine learning systems to patients, and that failure to do so would amount to a moral fault for which clinicians ought to be held accountable. Call thi...
This commentary explores the critical roles of health equity and ethical considerations in the deployment of artificial intelligence (AI) in public health and medicine. As AI increasingly permeates these fields, it promises substantial benefits but a...
Due to its enormous potential, artificial intelligence (AI) can transform healthcare on a seemingly infinite scale. However, as we continue to explore the immense potential of AI, it is vital to consider the ethical concerns associated with its devel...
Artificial intelligence (AI) has long been recognised as a challenge to responsibility. Much of this discourse has been framed around robots, such as autonomous weapons or self-driving cars, where we arguably lack control over a machine's behaviour a...
BACKGROUND: Responsible artificial intelligence (RAI) emphasizes the use of ethical frameworks implementing accountability, responsibility, and transparency to address concerns in the deployment and use of artificial intelligence (AI) technologies, i...
The growing prominence of artificial intelligence (AI) in mobile health (mHealth) has given rise to a distinct subset of apps that provide users with diagnostic information using their inputted health status and symptom information-AI-powered symptom...
BACKGROUND: Machine learning (ML) is utilized increasingly in health care, and can pose harms to patients, clinicians, health systems, and the public. In response, regulators have proposed an approach that would shift more responsibility to ML develo...
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