Set of metrics that indicate the state of equality and the quality of the database used.
Published in Chapter:
Analysis of Ethical Development for Public Policies in the Acquisition of AI-Based Systems
Reinel Tabares-Soto (Universidad Autónoma de Manizales, Colombia & Universidad Adolfo Ibáñez, Chile), Joshua Bernal-Salcedo (Universidad Autónoma de Manizales, Colombia), Zergio Nicolás García-Arias (Universidad Autónoma de Manizales, Colombia), Ricardo Ortega-Bolaños (Universidad Autónoma de Manizales, Colombia), María Paz Hermosilla (Universidad Adolfo Ibáñez, Chile), Harold Brayan Arteaga-Arteaga (Universidad Autónoma de Manizales, Colombia), and Gonzalo A. Ruz (Universidad Adolfo Ibáñez, Chile & Center of Applied Ecology and Sustainability (CAPES), Santiago, Chile)
Copyright: © 2022
|Pages: 29
DOI: 10.4018/978-1-6684-5892-1.ch010
Abstract
The exponential growth of AI and its applications in different areas of society, such as the financial, agricultural, telecommunications, or health sectors, poses new challenges for the government's public sector, mainly in regulating these systems. Governments and entities in general address these challenges by formulating soft laws such as manuals or guidelines. They seek full transparency, privacy, and bias reduction when implementing an AI-based system, including its life cycle and respective data management or governance. These tools and documents aim to develop an ethical AI that addresses or solves the aforementioned ethical implications. The revision of 22 documents within frameworks, guides, articles, toolkits, and manuals proposed by different governments and entities are examined in detail. Analyses include a general summary, the main objective, characteristics to be highlighted, advantages and disadvantages if any, and possible improvements.