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..
Viewpoint: Human-in-the-loop Artificial Intelligence
Authors: Fabio Massimo Zanzotto
JAIR 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Viewpoint: Human-in-the-loop Artificial Intelligence. In this paper, we propose Human-in-the-loop Artificial Intelligence (Hit AI) as a fairer paradigm for AI systems. This paper proposes Human-in-the-loop Artificial Intelligence (Hit AI) as a novel paradigm for a responsible Artificial Intelligence. The paper primarily discusses conceptual frameworks and proposals rather than presenting empirical studies or experimental results. |
| Researcher Affiliation | Academia | Fabio Massimo Zanzotto EMAIL University of Rome Tor Vergata |
| Pseudocode | No | No pseudocode or algorithm blocks were found in the paper. The paper is a conceptual viewpoint and does not describe specific algorithms in a structured code-like format. |
| Open Source Code | No | The paper discusses a conceptual framework and does not describe a specific methodology that would involve source code. There is no mention of code release, repository links, or supplementary materials containing code. |
| Open Datasets | No | The paper is a viewpoint and theoretical discussion on Human-in-the-loop AI and does not present experiments utilizing specific datasets. Therefore, no information about open datasets used by the authors is provided. |
| Dataset Splits | No | The paper is a theoretical viewpoint and does not describe any experiments that would involve dataset splits. |
| Hardware Specification | No | The paper is a conceptual work and does not describe any experiments that would require specific hardware specifications (e.g., GPU/CPU models, memory details). |
| Software Dependencies | No | The paper is a theoretical viewpoint and does not describe any experiments that would require specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is a theoretical viewpoint and does not describe any experiments that would require specific experimental setup details or hyperparameters. |