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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Governance by Glass-Box: Implementing Transparent Moral Bounds for AI Behaviour
Authors: Andrea Aler Tubella, Andreas Theodorou, Frank Dignum, Virginia Dignum
IJCAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we present an approach to evaluate the moral bounds of an AI system based on the monitoring of its inputs and outputs. We place a Glass-Box around the system by mapping moral values into explicit veri๏ฌable norms that constrain inputs and outputs, in such a way that if these remain within the box we can guarantee that the system adheres to the value. [...] In the penultimate section we provide an example of how our approach can be applied to a real-world intelligent system. |
| Researcher Affiliation | Academia | Andrea Aler Tubella , Andreas Theodorou , Frank Dignum and Virginia Dignum Ume a University EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. It describes the two stages of the Glass-Box approach conceptually, accompanied by a diagram (Figure 1). |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. It is a conceptual paper outlining an approach. |
| Open Datasets | No | The paper presents a conceptual approach and illustrates it with a use case (mortgage decisions) rather than conducting experiments on a dataset. Therefore, no information on public datasets for training is provided. |
| Dataset Splits | No | The paper does not describe experiments with data. Consequently, there are no training, validation, or test dataset splits mentioned. |
| Hardware Specification | No | The paper is conceptual and does not describe running experiments. Therefore, no specific hardware specifications are mentioned. |
| Software Dependencies | No | The paper does not describe running experiments or implementing software that would require specific dependencies with version numbers. |
| Experiment Setup | No | The paper describes a conceptual framework and a use case, but does not detail an experimental setup, hyperparameters, or system-level training settings. |