The core computational logic of most generative AI models is probabilistic, not deterministic. When given a prompt or a set of data, the model calculates a probability distribution over a vast range of possible next words or tokens. The final output is then generated by sampling from this distribution. Techniques like temperature scaling and nucleus sampling are used to control the randomness of this sampling process. Because the output is a result of probabilistic sampling, running the same query multiple times can produce different, yet plausible, results. An IS auditor must understand this inherent stochasticity to evaluate the model's consistency, reliability, and the associated risks for high-stakes applications like financial advice.

A. Limitations can arise in the quantification of risk profiles. This is a consequence of the model's variable output, not the fundamental reason for the variability itself.

B. Neural node access varies each time the process is executed. This is an inaccurate description of the inference process; the variability is primarily due to probabilistic sampling of the output, not random internal pathways.

D. Servers are reconfigured periodically. This is an operational infrastructure issue and is unrelated to the fundamental mathematical principles upon which the generative AI model operates.

1. Goodfellow

I.

Bengio

Y.

& Courville

A. (2016). Deep Learning. MIT Press. Chapter 3

"Probability and Information Theory

" establishes the probabilistic framework that underpins modern machine learning models

including generative AI.

2. Stanford University. (2023). CS224N: Natural Language Processing with Deep Learning

Lecture 11: Language Models and RNNs Part 2. This lecture explains that language models work by producing a probability distribution over the vocabulary for the next word

P(xt | x1

...

x{t-1})

and then sampling from this distribution to generate text. This sampling is the source of variability.

3. Holtzman

A.

Buys

J.

Du

L.

Forbes

M.

& Choi

Y. (2019). The Curious Case of Neural Text Degeneration. Proceedings of the International Conference on Learning Representations (ICLR). This paper details sampling strategies (e.g.

nucleus sampling) that are explicitly probabilistic and designed to control the randomness in text generation to produce higher-quality

non-deterministic outputs. (Available at: https://arxiv.org/abs/1904.09751)