Generative AI and Large Language Models (LLMs) hold promise for automating spreadsheet formula creation. However, due to hallucinations, bias and variable user skill, outputs obtained from generative AI cannot be assumed to be accurate or trustworthy. To address these challenges, a trustworthiness framework is proposed based on evaluating the transparency and dependability of the formula. The transparency of the formula is explored through explainability (understanding the formula's reasoning) and visibility (inspecting the underlying algorithms). The dependability of the generated formula is evaluated in terms of reliability (consistency and accuracy) and ethical considerations (bias and fairness). The paper also examines the drivers to these metrics in the form of hallucinations, training data bias and poorly constructed prompts. Finally, examples of mistrust in technology are considered and the consequences explored.

翻译：生成式人工智能与大型语言模型（LLMs）在自动化生成电子表格公式方面展现出巨大潜力。然而，由于存在幻觉、偏见及用户技能差异等问题，不能假定生成式人工智能的输出结果必然准确或可信。为应对这些挑战，本文提出一个基于公式透明度和可靠性评估的可信度框架。公式的透明度通过可解释性（理解公式的推理逻辑）和可见性（检查底层算法）进行探究。生成公式的可靠性则从稳定性（一致性与准确性）和伦理考量（偏见与公平性）两个维度进行评估。本文还探讨了影响这些指标的关键驱动因素，包括幻觉现象、训练数据偏见及提示词构建不当等问题。最后，通过分析技术不信任的典型案例，深入探讨了其可能引发的后果。