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].
Moral Responsibility for AI Systems
Authors: Sander Beckers
NeurIPS 2023 | Venue PDF | LLM Run Details
| 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. |