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Generative Artificial Intelligence (GenAI) and a Taxonomy of Undesirable Results

Eric E. Cohen & Gregory J. Gerard

Journal of Emerging Technologies in Accounting2026https://doi.org/10.2308/jeta-2024-045article
AJG 1ABDC B
Weight
0.50

Abstract

This paper makes the case that the term “hallucinations” is inadequate for describing undesirable results from Generative Artificial Intelligence (GenAI) output. We explain hallucinations, how the term is used, and why it is inadequate. We propose a taxonomy for categorizing these undesirable results, facilitating clearer communication about them. This taxonomy aids in understanding, troubleshooting, and developing resilience against undesirable results, which is important for corporate governance as GenAI integrates both specifically into enterprise resource planning systems, and also software, generally. We give several examples illustrating how our taxonomy provides the necessary language to describe and address undesirable results. We discuss the implications of using the taxonomy widely, particularly in the context of text-to-text Large Language model output, and consider its relevance across different GenAI modalities, as common GenAI multimodal solutions expand.

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https://doi.org/https://doi.org/10.2308/jeta-2024-045

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@article{eric2026,
  title        = {{Generative Artificial Intelligence (GenAI) and a Taxonomy of Undesirable Results}},
  author       = {Eric E. Cohen & Gregory J. Gerard},
  journal      = {Journal of Emerging Technologies in Accounting},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.2308/jeta-2024-045},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.