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; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1]
Counterfactual harm
Authors: Jonathan Richens, Rory Beard, Daniel H. Thompson
NeurIPS 2022 | Venue PDF | LLM Run Details
| 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. |