This paper explores the development of an ethical guardrail framework for AI systems, emphasizing the importance of customizable guardrails that align with diverse user values and underlying ethics. We address the challenges of AI ethics by proposing a structure that integrates rules, policies, and AI assistants to ensure responsible AI behavior, while comparing the proposed framework to the existing state-of-the-art guardrails. By focusing on practical mechanisms for implementing ethical standards, we aim to enhance transparency, user autonomy, and continuous improvement in (...) AI systems. Our approach accommodates ethical pluralism, offering a flexible and adaptable solution for the evolving landscape of AI governance. The paper concludes with strategies for resolving conflicts between ethical directives, underscoring the present and future need for robust, nuanced and context-aware AI systems. (shrink)

This paper explores the intersection of AI hallucinations and the question of AI consciousness, examining whether the erroneous outputs generated by large language models (LLMs) could be mistaken for signs of emergent intelligence. AI hallucinations, which are false or unverifiable statements produced by LLMs, raise significant philosophical and ethical concerns. While these hallucinations may appear as data anomalies, they challenge our ability to discern whether LLMs are merely sophisticated simulators of intelligence or could develop genuine cognitive processes. By analyzing the (...) causes of AI hallucinations, their impact on the perception of AI cognition, and the potential implications for AI consciousness, this paper contributes to the ongoing discourse on the nature of artificial intelligence and its future evolution. (shrink)

The purpose of this paper is to show that understanding AI hallucination requires an interdisciplinary approach that combines insights from epistemology and cognitive science to address the nature of AI-generated knowledge, with a terminological worry that concepts we often use might carry unnecessary presuppositions. Along with terminological issues, it is demonstrated that AI systems, comparable to human cognition, are susceptible to errors in judgement and reasoning, and proposes that epistemological frameworks, such as reliabilism, can be similarly applied to enhance the (...) trustworthiness of AI outputs. This exploration seeks to deepen our understanding of the possibility of AI cognition and its implications for the broader philosophical questions of knowledge and intelligence. (shrink)