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].
Viewpoint: Ethical By Designer - How to Grow Ethical Designers of Artificial Intelligence
Authors: Loïs Vanhée, Melania Borit
JAIR 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Building on interdisciplinary knowledge and practical experience from computer science, moral psychology and development, and pedagogy, we propose a functional way to provide this training. Thus, to those interested in becoming the kind of AI systems developers that the society needs and to those willing to contribute with training such developers, we propose using the GEDAI framework Growing Ethical Designers of Artificial Intelligence. This framework is based on an integrative curricular strategy that is consistent with approaches that make the ethical and value aspects explicit as a part of the design process |
| Researcher Affiliation | Academia | Loïs Vanhée (Corresponding author) EMAIL Umeå Universitet Department of Computing Science Linnaeus väg 49, 907 36 Umeå, Sweden Melania Borit EMAIL UiT The Arctic University of Norway Norwegian College of Fisheries Science Muninbakken 21, 9019 Tromsø, Norway |
| Pseudocode | No | The paper presents a conceptual framework through a diagram (Figure 1), but it does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper proposes a pedagogical framework and does not mention any associated source code or provide links to its implementation. |
| Open Datasets | No | The paper proposes a theoretical framework and does not conduct empirical studies requiring specific datasets, nor does it provide access information for any open datasets. |
| Dataset Splits | No | The paper is theoretical and does not involve experiments with datasets, therefore, no dataset splits are provided. |
| Hardware Specification | No | The paper introduces a conceptual framework for teaching ethics in AI and does not describe any experimental hardware specifications. |
| Software Dependencies | No | The paper proposes a theoretical framework and does not mention any specific software dependencies or version numbers. |
| Experiment Setup | No | The paper focuses on a pedagogical framework and does not present experimental results, thus no experimental setup details such as hyperparameters are provided. |