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.
Moral Responsibility for AI Systems
Authors: Sander Beckers
NeurIPS 2023 | Venue PDF | LLM Run Details | Input Tokens: 20,658 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,355 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 | This paper presents a formal definition of both conditions within the framework of causal models. I compare my approach to the existing approaches of Braham and van Hees (Bv H) and of Halpern and Kleiman Weiner (HK). I then generalize my definition into a degree of responsibility. |
| Researcher Affiliation | Academia | Sander Beckers University of Amsterdam |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper is theoretical and presents formal definitions and arguments; it does not describe a software implementation for which source code would be released. |
| Open Datasets | No | The paper focuses on theoretical definitions and arguments, not on empirical studies involving dataset training. |
| Dataset Splits | No | The paper focuses on theoretical definitions and arguments, not on empirical studies involving dataset validation. |
| Hardware Specification | No | The paper is theoretical and does not mention any specific hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not mention any specific software dependencies or versions. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup or training configurations. |