SearcharxivSearch

arXiv subjects

Chengke Liu

Publications and source records attributed to Chengke Liu.

2 recordsLinked to original sources

Human-controllable AI: Meaningful Human Control

Developing human-controllable artificial intelligence (AI) and achieving meaningful human control (MHC) has become a vital principle to address these challenges, ensuring ethical alignment and effective governance in AI. MHC is also a critical focus in human-centered AI (HCAI) research and application. This chapter systematically examines MHC in AI, articulating its foundational principles and future trajectory. MHC is not simply the right to operate, but the unity of human understanding, intervention, and the traceablity of responsibility in AI decision-making, which requires technological design, AI governance, and humans to play a role together. MHC ensures AI autonomy serves humans without constraining technological progress. The mode of human control needs to match the levels of technology, and human supervision should balance the trust and doubt of AI. For future AI systems, MHC mandates human controllability as a prerequisite, requiring: (1) technical architectures with embedded mechanisms for human control; (2) human-AI interactions optimized for better access to human understanding; and (3) the evolution of AI systems harmonizing intelligence and human controllability. Governance must prioritize HCAI strategies: policies balancing innovation and risk mitigation, human-centered participatory frameworks transcending technical elite dominance, and global promotion of MHC as a universal governance paradigm to safeguard HCAI development. Looking ahead, there is a need to strengthen interdisciplinary research on the controllability of AI systems, enhance ethical and legal awareness among stakeholders, moving beyond simplistic technology design perspectives, focus on the knowledge construction, complexity interpretation, and influencing factors surrounding human control. By fostering MHC, the development of human-controllable AI can be further advanced, delivering HCAI systems.

cs.HC

Street-based Topological Representations and Analyses for Predicting Traffic Flow in GIS

It is well received in the space syntax community that traffic flow is significantly correlated to a morphological property of streets, which are represented by axial lines, forming a so called axial map. The correlation co-efficient (R square value) approaches 0.8 and even a higher value according to the space syntax literature. In this paper, we study the same issue using the Hong Kong street network and the Hong Kong Annual Average Daily Traffic (AADT) datasets, and find surprisingly that street-based topological representations (or street-street topologies) tend to be better representations than the axial map. In other words, vehicle flow is correlated to a morphological property of streets better than that of axial lines. Based on the finding, we suggest the street-based topological representations as an alternative GIS representation, and the topological analyses as a new analytical means for geographic knowledge discovery.

physics.data-an