Ziosi / Sartor / Cunha AI for People, Democratizing AI

Second EAI International Conference, CAIP 2023, Bologna, Italy, November 24-26, 2023, Proceedings
Erscheinungsjahr 2024
ISBN: 978-3-031-71304-0
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 131 Seiten

Reihe: Mathematics and Statistics

This book constitutes the refereed post-conference proceedings of the Second EAI International Conference on AI for People, Democratizing AI, CAIP 2023, held in Bologna, Italy, during November 24-26, 2023.

The 11 regular papers were carefully reviewed and selected from 27 submissions. The papers are organized in thematic sessions on ethical AI and innovation; democratization of AI and governance; AI in society and legal aspects; and data privacy and technology ethics.

Zielgruppe

Research

Autoren/Hrsg.

Ziosi, Marta

Sartor, Giovanni

Cunha, João Miguel

Trotta, Angelo

Wicke, Philipp

Inhaltsverzeichnis

.- Ethical AI and innovation .
 .- A Human-Centered Decision Support System in Customer Support.
.- An AI-based Remote Rehabilitation System to Promote Access to Physical Rehabilitation.
.- Democratization of AI and Governance .
.- Democratization is a process, not a destination: Operationalizing ethics and democratization in a cyberinfrastructure for AI project.
.- (Im)possibilities in the Ethics of AI: Biometric Surveillance, Complicity, and Refusal in India and Beyond.
.- Research methods of the impact of AI on elections systematic review.
.- AI in Society and Legal Aspects .
.- On the Legal Aspects of Responsible AI: Algorithmic Change, Human Oversight, and Societal Outcomes.
.- Limitations of Transparency in Democratising and Regulating Algorithmic Management.
.- Breaking the Filtered Lens: A Feminist Examination of Beauty Ideals in Augmented Reality Filters.
.- Data Privacy and Technology Ethics .
.- Your body should not belong to the internet: online bodily integrity in the world of deepfake pornography.
.- The Promise and Peril of Responsible AI Principles.
.- Accelerating Machine Learning Primitives on Commodity Hardware.