One concern about the application of medical artificial intelligence(AI)regards the“black box”feature which can only be viewed in terms of itsinputs and outputs,with no way to understand the AI’s algorithm.Thisis p...One concern about the application of medical artificial intelligence(AI)regards the“black box”feature which can only be viewed in terms of itsinputs and outputs,with no way to understand the AI’s algorithm.Thisis problematic because patients,physicians,and even designers,do not understand why or how a treatment recommendation is produced by AI technologies.One view claims that the worry about black-box medicine is unreasonable because AI systems outperform human doctors in identifying the disease.Furthermore,under the medical AI-physicianpatient model,the physician can undertake the responsibility of interpreting the medical AI’s diagnosis.In this study,we focus on the potential harm caused by the unexplainability feature of medical AI and try to show that such possible harm is underestimated.We will seek to contribute to the literature from three aspects.First,we appealed to a thought experiment to show that although the medical AI systems perform better on accuracy,the harm caused by medical AI’s misdiagnoses may be more serious than that caused by human doctors’misdiagnoses in some cases.Second,in patient-centered medicine,physicians were obligated to provide adequate information to their patients in medical decision-making.However,the unexplainability feature of medical AI systems would limit the patient’s autonomy.Last,we tried to illustrate the psychological and financial burdens that may be caused by the unexplainablity feature of medical AI systems,which seems to be ignored by the previous ethical discussions.展开更多
针对机器人优化设计等工程应用中普遍存在的黑箱问题,提出了一种高效、稳定的遗传算法-非均匀Kriging-梯度投影混合全局优化(Hybrid global optimization,HGO)算法。该方法使用非均匀Kriging模型对目标函数进行评估,能够在不苛求近似模...针对机器人优化设计等工程应用中普遍存在的黑箱问题,提出了一种高效、稳定的遗传算法-非均匀Kriging-梯度投影混合全局优化(Hybrid global optimization,HGO)算法。该方法使用非均匀Kriging模型对目标函数进行评估,能够在不苛求近似模型全局精度的情况下保证优化过程的精度,并节省大量计算时间。使用梯度投影法对遗传算法种群进行变异,可以在提升优化收敛效率的同时确保优化约束条件,从而可以避免使用并不严格的罚函数法处理约束函数。为验证算法的有效性和优越性,将本算法应用于两个数学测试算例和一个模块化机械臂截面优化实例中,并与其他优化算法比较。结果表明,本算法能够兼顾结果精度、优化效率和算法稳定性,发挥更好的综合性能,从而实现对工程问题的全局优化设计。展开更多
基金the Young Scholars Program of the National Social Science Fund of China(Grant No.22CZX019).
文摘One concern about the application of medical artificial intelligence(AI)regards the“black box”feature which can only be viewed in terms of itsinputs and outputs,with no way to understand the AI’s algorithm.Thisis problematic because patients,physicians,and even designers,do not understand why or how a treatment recommendation is produced by AI technologies.One view claims that the worry about black-box medicine is unreasonable because AI systems outperform human doctors in identifying the disease.Furthermore,under the medical AI-physicianpatient model,the physician can undertake the responsibility of interpreting the medical AI’s diagnosis.In this study,we focus on the potential harm caused by the unexplainability feature of medical AI and try to show that such possible harm is underestimated.We will seek to contribute to the literature from three aspects.First,we appealed to a thought experiment to show that although the medical AI systems perform better on accuracy,the harm caused by medical AI’s misdiagnoses may be more serious than that caused by human doctors’misdiagnoses in some cases.Second,in patient-centered medicine,physicians were obligated to provide adequate information to their patients in medical decision-making.However,the unexplainability feature of medical AI systems would limit the patient’s autonomy.Last,we tried to illustrate the psychological and financial burdens that may be caused by the unexplainablity feature of medical AI systems,which seems to be ignored by the previous ethical discussions.
文摘针对机器人优化设计等工程应用中普遍存在的黑箱问题,提出了一种高效、稳定的遗传算法-非均匀Kriging-梯度投影混合全局优化(Hybrid global optimization,HGO)算法。该方法使用非均匀Kriging模型对目标函数进行评估,能够在不苛求近似模型全局精度的情况下保证优化过程的精度,并节省大量计算时间。使用梯度投影法对遗传算法种群进行变异,可以在提升优化收敛效率的同时确保优化约束条件,从而可以避免使用并不严格的罚函数法处理约束函数。为验证算法的有效性和优越性,将本算法应用于两个数学测试算例和一个模块化机械臂截面优化实例中,并与其他优化算法比较。结果表明,本算法能够兼顾结果精度、优化效率和算法稳定性,发挥更好的综合性能,从而实现对工程问题的全局优化设计。
