Position: The Right to AI
Authors: Rashid Mushkani, Hugo Berard, Allison Cohen, Shin Koseki
ICML 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Drawing on Sherry Arnstein s Ladder of Citizen Participation and analyzing nine case studies, the paper develops a four-tier model for the Right to AI that situates the current paradigm and envisions an aspirational future. This section draws on empirical insights from a range of participatory AI initiatives (Table 1) to explore whether stakeholder engagement can meaningfully influence AI design or remains largely symbolic. |
| Researcher Affiliation | Academia | 1Universite de Montreal 2Mila Quebec AI Institute. Correspondence to: Rashid Mushkani <EMAIL>. |
| Pseudocode | No | The paper discusses conceptual frameworks, arguments, and case studies for a 'Right to AI' but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper is a position paper proposing a conceptual framework and analyzing case studies, and as such, it does not describe specific methodologies or implementations for which source code would be provided. |
| Open Datasets | No | The paper analyzes case studies of participatory AI initiatives and mentions several existing datasets or data collection efforts (e.g., PRISM Alignment Dataset, Māori Data Sovereignty Initiative) within these case studies, but it does not describe experiments performed by the authors on specific datasets for which access information is provided. |
| Dataset Splits | No | The paper does not report on experiments with specific datasets and therefore does not provide any dataset split information. |
| Hardware Specification | No | The paper is a position paper and does not describe computational experiments, thus no hardware specifications are provided. |
| Software Dependencies | No | The paper is a position paper and does not describe software implementations or computational experiments, thus no specific software dependencies with version numbers are provided. |
| Experiment Setup | No | The paper is a position paper and does not describe computational experiments, therefore no experimental setup details like hyperparameters or training configurations are provided. |