Enmeng Lu

The ongoing evolution of advanced AI systems will have profound, enduring, and significant impacts on human existence that must not be overlooked. These impacts range from empowering humanity to achieve unprecedented transcendence to potentially causing catastrophic threats to our existence. To proactively and preventively mitigate these potential threats, it is crucial to establish clear redlines to prevent AI-induced existential risks by constraining and regulating advanced AI and their related AI actors. This paper explores different concepts of AI existential risk, connects the enactment of AI red lines to broader efforts addressing AI's impacts, constructs a theoretical framework for analyzing the direct impacts of AI existential risk, and upon that proposes a set of exemplary AI red lines. By contemplating AI existential risks and formulating these red lines, we aim to foster a deeper and systematic understanding of the potential dangers associated with advanced AI and the importance of proactive risk management. We hope this work will contribute to the strengthening and refinement of a comprehensive AI redline system for preventing humanity from AI existential risks.

This paper presents social data and knowledge collective intelligence platform for TRaining Ethical AI Models (STREAM) to address the challenge of aligning AI models with human moral values, and to provide ethics datasets and knowledge bases to help promote AI models “follow good advice as naturally as a stream follows its course”. By creating a comprehensive and representative platform that accurately mirrors the moral judgments of diverse groups including humans and AIs, we hope to effectively portray cultural and group variations, and capture the dynamic evolution of moral judgments over time, which in turn will facilitate the Establishment, Evaluation, Embedding, Embodiment, Ensemble, and Evolvement (6Es) of the moral capabilities of AI models. Currently, STREAM has already furnished a comprehensive collection of ethical scenarios, and amassed substantial moral judgment data annotated by volunteers and various popular Large Language Models, collectively portraying the moral preferences and performances of both humans and AIs across a range of moral contexts. This paper will outline the current structure and construction of STREAM, explore its potential applications, and discuss its future prospects.

Humans often unconsciously perceive social robots involved in their lives as partners rather than mere tools, imbuing them with qualities of companionship. This anthropomorphization can lead to a spectrum of emotional risks, such as deception, disappointment, and reverse manipulation, that existing approaches struggle to address effectively. In this paper, we argue that a Virtual Interactive Environment (VIE) exists between humans and social robots, which plays a crucial role and demands necessary consideration and clarification in order to mitigate potential emotional risks. By analyzing the relational nature of human-social robot interaction, we discuss the connotation of such a virtual interactive environment that is similar to the emotional states aroused when reading novels. Building on this comprehension, we further demonstrate that manufacturers should carry out comprehensive Virtual Interactive Environment Indication (VIEI) measures during human-social robot interaction with a stricter sense of responsibility when applying social robots. Finally, we contemplate the potential contributions of virtual interactive environment indication to existing robot ethics guidelines.