Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Counterfactual harm
Authors: Jonathan Richens, Rory Beard, Daniel H. Thompson
NeurIPS 2022 | Venue PDF | LLM Run Details | Input Tokens: 22,630 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,980 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our work reports no experimental results. In section 6 we use a three parameter parametric equation as an example, and cite the corresponding experimental paper where these parameters were learned. Our study does not use any datasets. All results were derived by hand. |
| Researcher Affiliation | Industry | |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | Our work reports no experimental results. |
| Open Datasets | No | Our study does not use any datasets. |
| Dataset Splits | No | Our study does not use any datasets. |
| Hardware Specification | No | All results were derived by hand. |
| Software Dependencies | No | All results were derived by hand. |
| Experiment Setup | No | Our work reports no experimental results. |