Towards Friendly AI: A Comprehensive Review and New Perspectives on Human-AI Alignment

December 19, 2024 Β· The Cartographer Β· πŸ› arXiv.org

πŸ“š THE CARTOGRAPHER: The Cartographer
Survey/review paper β€” maps the landscape rather than implementing a method.

"No code URL or promise found in abstract"
"Title-pattern auto-detect: Towards Friendly AI: A Comprehensive Review and New Perspectives on Human-AI Alignment"

Evidence collected by the PWNC Scanner

Authors Qiyang Sun, Yupei Li, Emran Alturki, Sunil Munthumoduku Krishna Murthy, BjΓΆrn W. Schuller arXiv ID 2412.15114 Category cs.AI: Artificial Intelligence Cross-listed cs.CY Citations 9 Venue arXiv.org Last Checked 3 days ago
Abstract
As Artificial Intelligence (AI) continues to advance rapidly, Friendly AI (FAI) has been proposed to advocate for more equitable and fair development of AI. Despite its importance, there is a lack of comprehensive reviews examining FAI from an ethical perspective, as well as limited discussion on its potential applications and future directions. This paper addresses these gaps by providing a thorough review of FAI, focusing on theoretical perspectives both for and against its development, and presenting a formal definition in a clear and accessible format. Key applications are discussed from the perspectives of eXplainable AI (XAI), privacy, fairness and affective computing (AC). Additionally, the paper identifies challenges in current technological advancements and explores future research avenues. The findings emphasise the significance of developing FAI and advocate for its continued advancement to ensure ethical and beneficial AI development.
Community shame:
Not yet rated
Community Contributions

Found the code? Know the venue? Think something is wrong? Let us know!

πŸ“œ Similar Papers

In the same crypt β€” Artificial Intelligence