Elections are moments when nations confront uncertainty about their future and re-examine their past. The present research investigated how two temporal, group-based emotions jointly shape political preferences and behaviour: collective nostalgia (longing for the group’s past) and collective angst (concern for the group’s future). We focused on the 2024 United States federal election, examining how civic-focused nostalgia (longing for civility and institutional trust) and homogeneity-focused nostalgia (longing for cultural and moral uniformity), together with collective angst, predicted three outcomes: support for strong leadership, voting intentions, and actual voting behaviour. Participants (N = 282) completed measures of these constructs pre-election (Time 1), with voting behaviour assessed post-election (Time 2). Results revealed that both civic- and homogeneity-focused nostalgia were associated with greater general support for strong leadership, but collective angst only predicted such support when civic nostalgia was low. Homogeneity-focused nostalgia robustly predicted Trump voting intentions and behaviour, whereas civic nostalgia predicted support for Harris. Collective angst interacted with homogeneity nostalgia to amplify pro-Trump voting, suggesting that anxiety about the group’s future magnifies the political consequences of longing for a homogeneous past. These findings illuminate how emotional orientations towards the past and future jointly guide democratic decision-making.

While codes/architectures in cyberspace are usually considered to regulate human activity, the productive dimension of such power has less been discussed. Codes/architectures also change market mechanisms, largely due to the evolution of digital platforms as drivers of new modes of production, increasingly becoming a powerful organizing body that mobilizes and manages productive resources like goods, services, information, human capital, money, et cetera, ostensibly through market mechanisms and processes. It is therefore necessary to further explore how market mechanisms have changed under the shaping of digital platforms and, indirectly, how the legal rules surrounding market resources, structures, competitive behaviors, distribution, and other dimensions need to be adjusted accordingly. This Article attempts to further identify the dual attributes of platforms, focusing on how market mechanisms have been affected by changes in modes of production, changing their operation and adapting to advances in productivity, and evolving from a traditional free market to a more cybernetic marketplace, and indirectly to a more controlled marketplace, which indirectly requires the corresponding legal rules to be adjusted. In this sense, digital markets and platform organizations are two sides of the same coin, and this Article discusses the functions of the two in some of the argumentative paragraphs without distinguishing between them.

The emergence of generative artificial intelligence (generative AI) has profoundly reshaped content creation by lowering marginal costs, altering the role of human creators, and restructuring industrial processes. These shifts pose three critical challenges for intellectual property (IP) frameworks: redefining creative behaviors in human–AI collaborations, sustaining incentives for innovation, and addressing gaps in copyright mechanisms. Against this backdrop, the Beijing Internet Court (BIC) issued China’s first judicial ruling on the copyrightability of AI-generated images in 2023. The court held that AI-generated content (AIGC) could qualify for copyright if there is demonstrable human intellectual input and originality. However, the ruling also emphasized the need for case-by-case assessments, particularly in hybrid human–AI creative processes. This landmark decision has sparked intense debate over whether and how AIGC should be considered as a “work” under copyright law, with particular focus on requirement of human authorship and the threshold of originality. In addition, a systematic analysis of the AIGC value chain highlights the far-reaching implications of human authorship claims for various stakeholders, including creators, AI developers, prompt engineers, and end users. To address these emerging legal and ethical issues, this Article proposes two key reforms: first, extending the scope of fair use under Article 24 of China’s Copyright Law to account for AI-generated works, and second establishing a “whitelist” mechanism in the Regulations for the Implementation of the Copyright Law. These measures aim to balance the protection of human creativity, ensure equitable value distribution among stakeholders, and foster sustainable innovation in the AI-driven creative ecosystem.

This Article examines the “human in the loop” argument regarding the increasing use of artificial intelligence (AI) in court proceedings, challenging the intuition that AI assistance is inherently less problematic than full delegation. It argues that even limited AI support can risk blurring the lines between human and AI decision-making, posing significant dangers for human judgment in critical judicial functions.

The Article starts out by identifying the main arguments in favor of human oversight and categorizing them into technological, legal, and psychological reasoning in Section B. It then shifts focus to the potential dangers of applying the “human in the loop” concept to judicial decision-making in Section C. The analysis highlights the fundamentally different modes of operation between humans and AI, particularly in handling natural language and legal reasoning. Furthermore, it explores how human judges might become over-reliant on AI, effectively acting as “rubber stampers” and leading to eroding human vigilance, skills, and independent judgment. To illustrate the complexities of these dynamics, the Article categorizes various AI tools used in court proceedings based on their degree of involvement and effect on judicial decision-making in Section D.

The Article concludes by urging us to rethink our current understanding of “co-working” with AI as a universal remedy and putting emphasis on a clear division of labor instead, as discussed in Section E. For those scenarios where human and AI contributions are deeply interwoven, the Article stresses the need for making a conscious decision whether AI or a human judge should perform the underlying tasks in the future.