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 [1].
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. |