Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Viewpoint: Artificial Intelligence Accidents Waiting to Happen?
Authors: Federico Bianchi, Amanda Cercas Curry, Dirk Hovy
JAIR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this essay, we apply and extend Perrow s framework to AI to assess its potential risks. We apply our framework to two case studies. |
| Researcher Affiliation | Academia | Federico Bianchi EMAIL Stanford University, Stanford, California, USA Amanda Cercas Curry EMAIL Bocconi University, Milan, Italy Dirk Hovy EMAIL Bocconi University, Milan, Italy |
| Pseudocode | No | The paper discusses theoretical concepts and frameworks (Perrow's normal accident theory, ACCI framework) and applies them to conceptual case studies, but does not present any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper is a viewpoint essay applying a theoretical framework to AI systems. It does not describe a new methodology or implement any experimental code, therefore, there is no mention of open-source code being provided for the work described. |
| Open Datasets | No | The paper is a theoretical viewpoint essay and does not perform empirical experiments requiring specific datasets. While it references external works that might use datasets (e.g., GPT-3, Amodei et al., 2016), it does not use or provide access information for any open datasets for its own analysis. |
| Dataset Splits | No | The paper is a theoretical essay and does not conduct empirical experiments on datasets, therefore no information about dataset splits (training/test/validation) is provided. |
| Hardware Specification | No | The paper is a theoretical viewpoint essay discussing frameworks and concepts. It does not involve computational experiments, and thus no hardware specifications are mentioned. |
| Software Dependencies | No | The paper presents a theoretical framework and conceptual analysis, not a software implementation. Therefore, it does not list any specific software dependencies or version numbers. |
| Experiment Setup | No | This paper is a theoretical viewpoint essay and does not describe any experimental procedures, models, or training runs. Consequently, there are no details regarding experimental setup, hyperparameters, or training configurations. |