A computational framework for human values
#Value alignment #Computational framework #Stuart Russell #Ethical AI #Machine learning #arXiv #Human values
📌 Key Takeaways
- A new computational framework has been proposed to formalize human values for AI integration.
- The research builds on Stuart Russell's concept of 'provably beneficial' artificial intelligence.
- The study synthesizes insights from psychology and philosophy into a technical engineering context.
- The framework aims to solve the 'alignment problem' by making AI behavior predictable and ethical.
📖 Full Retelling
🏷️ Themes
Artificial Intelligence, Ethics, Technology
📚 Related People & Topics
Machine learning
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Ethics of artificial intelligence
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🔗 Entity Intersection Graph
Connections for Machine learning:
- 🌐 Large language model (7 shared articles)
- 🌐 Generative artificial intelligence (3 shared articles)
- 🌐 Electroencephalography (3 shared articles)
- 🌐 Computer vision (3 shared articles)
- 🌐 Natural language processing (2 shared articles)
- 🌐 Artificial intelligence (2 shared articles)
- 🌐 Graph neural network (2 shared articles)
- 🌐 Neural network (2 shared articles)
- 🌐 Transformer (1 shared articles)
- 🌐 User interface (1 shared articles)
- 👤 Susan Schneider (1 shared articles)
- 🌐 Differential privacy (1 shared articles)
📄 Original Source Content
arXiv:2305.02748v2 Announce Type: replace Abstract: In the diverse array of work investigating the nature of human values from psychology, philosophy and social sciences, there is a clear consensus that values guide behaviour. More recently, a recognition that values provide a means to engineer ethical AI has emerged. Indeed, Stuart Russell proposed shifting AI's focus away from simply ``intelligence'' towards intelligence ``provably aligned with human values''. This challenge -- the value alig