The prevalence of artificial intelligence (AI) increases social concern surrounding unethical consumer behavior in human–AI interaction. Existing research has mainly focused on anthropomorphic characteristics of AI and unethical consumer behavior (UCB). However, the role of algorithms in unethical consumer behavior, which is central to AI, is not yet fully understood. Drawing on social exchange theory, this study investigates the impact of algorithmic discrimination on UCB and explores the interrelationships and underlying mechanisms. Through three experiments, this study found that experiencing algorithmic discrimination significantly increases UCB, with anticipatory guilt mediating this relationship. Moreover, consumers’ negative reciprocity beliefs moderated the effects of algorithmic discrimination on anticipatory guilt and UCB. In addition, this study distinguish between active and passive UCB based on their underlying ethical motivations. This enhances the study’s universality by assessing both types of behaviors and highlighting their differences. These insights extend current research on UCB within the purview of AI agents and provide valuable insights into effectively mitigating losses caused by UCB behaviors, offering improved directions for facilitating AI agents to provide fair, reliable, and efficient interactions for both businesses and consumers.

Driven by the rapid proliferation of generative artificial intelligence, the computing power industry is undergoing a paradigm shift from traditional linear supply chains toward complex, interdependent innovation ecosystems. This study investigates the evolutionary dynamics of the computing power ecosystem, specifically examining the strategic interplay between antitrust regulation and vertical integration. We construct a tripartite evolutionary game framework involving the government regulators, leading computing power incumbents, and downstream AI innovators. By deriving evolutionarily stable strategies, we analyze the underlying mechanisms of system transitions and employ numerical simulations to explore key parametric sensitivities. The theoretical analysis suggests that the evolution of the AI computing power innovation ecosystem manifests distinct stage-based progressions and threshold-driven bifurcation characteristics—potentially transitioning from an initial efficiency-based state of “natural monopoly and passive dependence” during the industry’s emergence, through transitionary states such as the “comfort zone trap” or “regulatory stalemate” during the expansion phase, and ultimately converging toward a mature configuration of “co-opetition and endogenous growth.” The model suggests that downstream AI firms may benefit from advancing vertical integration, achieving hardware–software co-optimization through self-developed domain-specific architectures, The analysis further implies that the leading computing power firm could strengthen its ecological niche by opening its underlying interfaces and software stacks to maintain its ecological niche as the industry cornerstone in integrated form. For the government, it is necessary to establish precise dynamic intervention and orderly exit mechanisms.

The application of artificial intelligence (AI) and the improvement of environmental, social, and governance (ESG) performance have become important concerns for contemporary firms. Understanding whether AI can be effectively integrated into corporate ESG practices has significant implications for sustainable development. Using panel data from Chinese A-share listed firms from 2009 to 2025, this study empirically examines the effect of AI application on corporate ESG performance through a fixed-effects model. The results show that AI application significantly improves corporate ESG performance, indicating that firms with higher levels of AI adoption tend to achieve better ESG outcomes. The heterogeneity analysis further reveals that this effect varies according to firms’ technological intensity, industry pollution characteristics, and the strength of regional environmental regulation. Specifically, the positive effect of AI application is more pronounced among firms with lower technological intensity, firms operating in non-heavily polluting industries, and firms located in regions with stricter environmental regulation. The mediation analysis shows that AI application enhances ESG performance by promoting human capital upgrading and green technological innovation, thereby strengthening firms’ internal capabilities and technological foundations for sustainable development. This study contributes to the literature by integrating AI application and ESG-oriented sustainable development within a unified analytical framework.