Computer Science > Artificial Intelligence
arXiv:2410.15471 (cs)
[Submitted on 20 Oct 2024 (v1), last revised 14 Feb 2025 (this version, v2)]
Title:Generative Models, Humans, Predictive Models: Who Is Worse at High-Stakes Decision Making?
View a PDF of the paper titled Generative Models, Humans, Predictive Models: Who Is Worse at High-Stakes Decision Making?, by Keri Mallari and Julius Adebayo and Kori Inkpen and Martin T. Wells and Albert Gordo and Sarah Tan
View PDFHTML (experimental)Abstract:Despite strong advisory against it, large generative models (LMs) are already being used for decision making tasks that were previously done by predictive models or humans. We put popular LMs to the test in a high-stakes decision making task: recidivism prediction. Studying three closed-access and open-source LMs, we analyze the LMs not exclusively in terms of accuracy, but also in terms of agreement with (imperfect, noisy, and sometimes biased) human predictions or existing predictive models. We conduct experiments that assess how providing different types of information, including distractor information such as photos, can influence LM decisions. We also stress test techniques designed to either increase accuracy or mitigate bias in LMs, and find that some to have unintended consequences on LM decisions. Our results provide additional quantitative evidence to the wisdom that current LMs are not the right tools for these types of tasks.
| Subjects: | Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2410.15471 [cs.AI] |
| (orarXiv:2410.15471v2 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2410.15471 arXiv-issued DOI via DataCite |
Submission history
From: Sarah Tan [view email][v1] Sun, 20 Oct 2024 19:00:59 UTC (165 KB)
[v2] Fri, 14 Feb 2025 05:41:23 UTC (370 KB)
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View a PDF of the paper titled Generative Models, Humans, Predictive Models: Who Is Worse at High-Stakes Decision Making?, by Keri Mallari and Julius Adebayo and Kori Inkpen and Martin T. Wells and Albert Gordo and Sarah Tan
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