In Data We Trust: The Logic of Trust-Based Beliefs
Authors: Junli Jiang, Pavel Naumov
IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | The paper proposes a data-centred approach to reasoning about the interplay between trust and beliefs. At its core, is the modality under the assumption that one dataset is trustworthy, another dataset informs a belief in a statement. The main technical result is a sound and complete logical system capturing the properties of this modality. |
| Researcher Affiliation | Academia | Junli Jiang1 and Pavel Naumov2 1Institute of Logic and Intelligence, Southwest University, China 2University of Southampton, the United Kingdom |
| Pseudocode | No | The paper focuses on theoretical definitions, axioms, and proofs within a logical system and does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any information or links regarding the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not involve empirical training on datasets. It uses 'data variables' in a logical context, not for machine learning training. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical validation using dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental setups that would require hardware specifications. |
| Software Dependencies | No | The paper is theoretical and describes a logical system; it does not mention any software dependencies with specific version numbers for implementation. |
| Experiment Setup | No | The paper is theoretical and does not describe any empirical experimental setup details like hyperparameters or training configurations. |