Consent governs innumerable everyday social interactions, including sex, medical exams, the use of property, and economic transactions. Yet little is known about how ordinary people reason about the validity of consent. Across the domains of sex, medicine, and police entry, Study 1 showed that when agents lack autonomous decision-making capacities, participants are less likely to view their consent as valid; however, failing to exercise this capacity and deciding in a nonautonomous way did not reduce consent judgments. Study 2 found that (...) specific and concrete incapacities reduced judgments of valid consent, but failing to exercise these specific capacities did not, even when the consenter makes an irrational and inauthentic decision. Finally, Study 3 showed that the effect of autonomy on judgments of valid consent carries important downstream consequences for moral reasoning about the rights and obligations of third parties, even when the consented-to action is morally wrong. Overall, these findings suggest that laypeople embrace a normative, domain-general concept of valid consent that depends consistently on the possession of autonomous capacities, but not on the exercise of these capacities. Autonomous decisions and autonomous capacities thus play divergent roles in moral reasoning about consent interactions: while the former appears relevant for assessing the wrongfulness of consented-to acts, the latter plays a role in whether consent is regarded as authoritative and therefore as transforming moral rights. (shrink)

This work aims to explore the application of deep learning-based artificial intelligence technology in sentencing, to promote the reform and innovation of the judicial system. First, the concept and the principles of sentencing are introduced, and the deep learning model of intelligent robot in trials is proposed. According to related concepts, the issues that need to be solved in artificial intelligence sentencing based on deep learning are introduced. The deep learning model is integrated into the intelligent robot system, to assist (...) in the sentencing of cases. Finally, an example is adopted to illustrate the feasibility of the intelligent robot under deep learning in legal sentencing. The results show that the general final trial periods for cases of traffic accidents, copyright information, trademark infringement, copyright protection, and theft are 1,049, 796, 663, 847, and 201 days, respectively; while the final trial period under artificial intelligence evaluation based on the restricted Boltzmann deep learning model is 458, 387, 376, 438, and 247 days, respectively. The accuracy of trials is above 92%, showing a high application value. It can be observed that expect theft cases, the final trial period for others cases has been effectively reduced. The intelligent robot assistance under the restricted Boltzmann deep learning model can shorten the trial period of cases. The deep learning intelligent robot has a certain auxiliary role in legal sentencing, and this outcome provides a theoretical basis for the research of artificial intelligence technology in legal sentencing. (shrink)