Dashboard | Paper | Embeddings
The utilization of artificial intelligence (AI) applications has experienced tremendous growth in recent years, bringing forth numerous benefits and conveniences. However, this expansion has also provoked ethical concerns, such as privacy breaches, algorithmic discrimination, security and reliability issues, transparency, and other unintended consequences. To determine whether a global consensus exists regarding the ethical principles that should govern AI applications and to contribute to the formation of future regulations, this paper conducts a meta-analysis of 200 governance policies and ethical guidelines for AI usage published by public bodies, academic institutions, private companies, and civil society organizations worldwide. We identified at least 17 resonating principles prevalent in the policies and guidelines of our dataset, released as an open-source database and tool. We present the limitations of performing a global scale analysis study paired with a critical analysis of our findings, presenting areas of consensus that should be incorporated into future regulatory efforts.
Here you can find the source code used to create our Worldwide AI Ethics dashboard. This panel was created using the Dash library. All the tables that feed our dashboard (data_en.rar), images (png_files.rar), and HTML-Plotly graphs (html_files.rar) are available in the data folder (in csv and parquet). Auxiliary notebooks for creating the graphs (make_graphs.ipynb) and processing the text data(principle_mining.ipynb) are also available. We also make available the notebook we used to infer the gender of all authors in our sample (gender_infer.ipynb), and the notebook to create the geojson file that sets the boundaries of the polygons on the Mapbox. To render the dash app in your browser, simply run the worldwide.py script.
In the waie-embeddings, you can find one of the first spin-offs of our study.
After curating a dataset with 1400+ definitions across 17 ethical principles in AI (the Worldwide AI Ethics dataset), we leverage OpenAI's text-embedding-ada-002 to perform a different kind of analysis. In short, we have transformed these definitions into vectors so we can visualize them in 3D space using PCA and t-SNE.
The generated plots are available on our website.
@article{correa2023worldwide,
author={Corr{\^e}a, Nicholas Kluge and Galv{\~a}o, Camila and Santos, James William and Del Pino, Carolina and Pinto, Edson Pontes and Barbosa, Camila and Massmann, Diogo and Mambrini, Rodrigo and Galv{\~a}o, Luiza and Terem, Edmund and Oliveira, Nythamar},
title={Worldwide AI Ethics: a review of 200 guidelines and recommendations for AI governance},
journal={Patterns},
year={2023},
month={October},
volume={4},
number={10},
doi={10.1016/j.patter.2023.100857}
}
This research was funded by RAIES (Rede de Inteligência Artificial Ética e Segura). RAIES is a project supported by FAPERGS (Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul) and CNPq (Conselho Nacional de Desenvolvimento Científico e Tecnológico).
Worldwide AI Ethics is licensed under the CC-BY-NC license, Version 4.0. See the LICENSE file for more details.