Towards Trustworthy AI: A Review of Ethical and Robust Large Language Models
<|reference_start|>Towards Trustworthy AI: A Review of Ethical and Robust Large Language Models: The rapid progress in Large Language Models (LLMs) could transform many fields, but their fast development creates significant challenges for oversight, ethical creation, and building user trust. This comprehensive review looks at key trust issues in LLMs, such as unintended harms, lack of transparency, vulnerability to attacks, alignment with human values, and environmental impact. Many obstacles can undermine user trust, including societal biases, opaque decision-making, potential for misuse, and the challenges of rapidly evolving technology. Addressing these trust gaps is critical as LLMs become more common in sensitive areas like finance, healthcare, education, and policy. To tackle these issues, we suggest combining ethical oversight, industry accountability, regulation, and public involvement. AI development norms should be reshaped, incentives aligned, and ethics integrated throughout the machine learning process, which requires close collaboration across technology, ethics, law, policy, and other fields. Our review contributes a robust framework to assess trust in LLMs and analyzes the complex trust dynamics in depth. We provide contextualized guidelines and standards for responsibly developing and deploying these powerful AI systems. This review identifies key limitations and challenges in creating trustworthy AI. By addressing these issues, we aim to build a transparent, accountable AI ecosystem that benefits society while minimizing risks. Our findings provide valuable guidance for researchers, policymakers, and industry leaders striving to establish trust in LLMs and ensure they are used responsibly across various applications for the good of society.<|reference_end|>
@article{ferdaus2024towards,
    title={Towards Trustworthy AI: A Review of Ethical and Robust Large Language
  Models},
    author={Md Meftahul Ferdaus, Mahdi Abdelguerfi, Elias Ioup, Kendall N. Niles,
  Ken Pathak, and Steven Sloan},
    journal={arXiv preprint arXiv:2407.13934},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.13934},
    primaryClass={cs.CY cs.AI}
}