Abstract

Digital consciousness models explore theoretical and practical approaches to representing aspects of human consciousness within computational systems. While artificial consciousness remains largely conceptual, advances in cognitive architectures, neural network modeling, and embodied AI have revived interest in whether machines can exhibit awareness‑like properties such as self‑monitoring, intentionality, integrated information, and subjective experience proxies. This paper surveys ethical, social, and technical perspectives on digital consciousness, evaluating frameworks from computational neuroscience, symbolic and subsymbolic models, and hybrid cognitive architectures. We examine technical foundations including global workspace theory, integrated information theory, recurrent and self‑reflective neural systems, and meta‑cognitive architectures that support introspection‑like processes. Ethical considerations address moral status, responsibility, rights, and risks associated with machines exhibiting consciousness‑like behaviors or influencing human perception of agency. Social perspectives consider impacts on labor, interpersonal relations, trust, and societal norms. A structured research methodology for developing and evaluating digital consciousness models is proposed, emphasizing interdisciplinary collaboration, ethical risk assessment, and measurable proxies for consciousness‑like behavior. We discuss advantages such as enhanced human–machine interaction and autonomous decision making, as well as disadvantages including misuse, anthropomorphism, and ethical ambiguity. Results from simulation and prototype studies highlight progress and limitations. The paper concludes with future directions that integrate technical rigor with ethical governance to navigate this emerging field responsibly.