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The Oxford Handbook of Digital Ethics is a lively guide to ethical issues related to digital technologies, with a special emphasis on AI. Philosophers with a wide range of expertise cover 37 topics, including: the right to have access to the internet, trolling and online shaming, speech on social media, fake news, sex robots and dating online, persuative technology, value alignment, algorithmic bias, predictive policing, price discrimination online, medical AI, privacy and surveillance, automating democracy, the future of work, and AI and existential risk.
Technology --- Digital media --- Moral and ethical aspects. --- Mass media --- Digital communications --- Online journalism --- Electronic media --- New media (Digital media) --- Technology and ethics --- Moral and ethical aspects --- digital ethics --- Artificial intelligence --- Computer science --- Information technology --- General ethics --- Artificial intelligence. Robotics. Simulation. Graphics --- Society. --- Ethics & moral philosophy.
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This This chapter aims to explore the challenge that big data brings to medical privacy. Section I offers a brief overview of the role of privacy in medical settings. I define define privacy as having one's personal information and one's personal sensorial space (what I call autotopos) unaccessed. Section II discusses how the challenge of big data differs differs from other risks to medical privacy. Section III is about what can be done to minimise those risks. I argue that the most effective way of protecting people from suffering suffering suffering unfair medical consequences is by having a public universal healthcare system in which coverage is not influenced influenced influenced by personal data (e.g., genetic predisposition, exercise habits, eating habits, etc.).
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This This chapter aims to explore the challenge that big data brings to medical privacy. Section I offers a brief overview of the role of privacy in medical settings. I define define privacy as having one's personal information and one's personal sensorial space (what I call autotopos) unaccessed. Section II discusses how the challenge of big data differs differs from other risks to medical privacy. Section III is about what can be done to minimise those risks. I argue that the most effective way of protecting people from suffering suffering suffering unfair medical consequences is by having a public universal healthcare system in which coverage is not influenced influenced influenced by personal data (e.g., genetic predisposition, exercise habits, eating habits, etc.).
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