Jiao, J., Afroogh, S., Xu, Y., & Phillips, C. (2024).
arXiv (Cornell University).
Abstract
This study addresses ethical issues surrounding Large Language Models (LLMs) within the field of artificial intelligence. It explores the common ethical challenges posed by both LLMs and other AI systems, such as privacy and fairness, as well as ethical challenges uniquely arising from LLMs. It highlights challenges such as hallucination, verifiable accountability, and decoding censorship complexity, which are unique to LLMs and distinct from those encountered in traditional AI systems. The study underscores the need to tackle these complexities to ensure accountability, reduce biases, and enhance transparency in the influential role that LLMs play in shaping information dissemination. It proposes mitigation strategies and future directions for LLM ethics, advocating for interdisciplinary collaboration. It recommends ethical frameworks tailored to specific domains and dynamic auditing systems adapted to diverse contexts. This roadmap aims to guide responsible development and integration of LLMs, envisioning a future where ethical considerations govern AI advancements in society.
Here are some thoughts:
This study examines the ethical issues surrounding Large Language Models (LLMs) within artificial intelligence, addressing both common ethical challenges shared with other AI systems, such as privacy and fairness, and the unique ethical challenges specific to LLMs. The authors emphasize the distinct challenges posed by LLMs, including hallucination, verifiable accountability, and the complexities of decoding censorship. The research underscores the importance of tackling these complexities to ensure accountability, reduce biases, and enhance transparency in how LLMs shape information dissemination. It also proposes mitigation strategies and future directions for LLM ethics, advocating for interdisciplinary collaboration, ethical frameworks tailored to specific domains, and dynamic auditing systems adapted to diverse contexts, ultimately aiming to guide the responsible development and integration of LLMs.