14 May 2024 | Junfeng Jiao, Saleh Afroogh, Yiming Xu, David Atkinson, Connor Phillips
This study explores the ethical challenges posed by Large Language Models (LLMs) in the field of artificial intelligence, focusing on common issues such as privacy, fairness, and unique challenges specific to LLMs, including hallucination, verifiable accountability, and decoding censorship complexity. The authors emphasize the need to address these complexities to ensure accountability, reduce biases, and enhance transparency in LLMs' role in information dissemination. They propose mitigation strategies and future directions, advocating for interdisciplinary collaboration and the development of ethical frameworks tailored to specific domains. The study highlights the importance of dynamic auditing systems adapted to diverse contexts and aims to guide responsible development and integration of LLMs in society. Key ethical theories and approaches, such as Utilitarianism, Deontology, and Virtue Ethics, are discussed, along with the need for a multidimensional approach to embedding ethical considerations into LLM development. The paper also reviews existing studies on ethical concerns in LLMs, including bias, fairness, privacy, misinformation, accountability, and transparency, and presents case studies from various sectors to illustrate the wide-ranging effects of LLMs. Finally, it discusses mitigation strategies, transparency, censorship, intellectual property, and the detection of abusive language, hate speech, and cyberbullying. The authors conclude by emphasizing the advantages of Pre-trained Language Models (PLMs) in integrating normative ethics and the importance of multidisciplinary perspectives in addressing ethical AI in LLMs.This study explores the ethical challenges posed by Large Language Models (LLMs) in the field of artificial intelligence, focusing on common issues such as privacy, fairness, and unique challenges specific to LLMs, including hallucination, verifiable accountability, and decoding censorship complexity. The authors emphasize the need to address these complexities to ensure accountability, reduce biases, and enhance transparency in LLMs' role in information dissemination. They propose mitigation strategies and future directions, advocating for interdisciplinary collaboration and the development of ethical frameworks tailored to specific domains. The study highlights the importance of dynamic auditing systems adapted to diverse contexts and aims to guide responsible development and integration of LLMs in society. Key ethical theories and approaches, such as Utilitarianism, Deontology, and Virtue Ethics, are discussed, along with the need for a multidimensional approach to embedding ethical considerations into LLM development. The paper also reviews existing studies on ethical concerns in LLMs, including bias, fairness, privacy, misinformation, accountability, and transparency, and presents case studies from various sectors to illustrate the wide-ranging effects of LLMs. Finally, it discusses mitigation strategies, transparency, censorship, intellectual property, and the detection of abusive language, hate speech, and cyberbullying. The authors conclude by emphasizing the advantages of Pre-trained Language Models (PLMs) in integrating normative ethics and the importance of multidisciplinary perspectives in addressing ethical AI in LLMs.