Generative AI Risks and Benefits within Human-Machine Teams
- 1st Edition - January 1, 2027
- Latest edition
- Editors: William Lawless, Marco Brambilla
- Language: English
Generative AI Risks and Benefits within Human-Machine Teams delves into the foundational principles, metrics, and applications of human-machine systems, addressing the legal ramifi… Read more
Description
Description
Key features
Key features
- Explores core concepts and practical applications of human-machine collaboration
- Addresses ethical, legal, and trust issues in generative AI development
- Offers insights into adaptive collaboration patterns and teamwork support
- Provides frameworks for integrating generative AI within autonomous systems
Readership
Readership
Table of contents
Table of contents
2. Human-AI Collaboration for Energy Communities supported by an AI-based Conversational Agent
3. Enhancing Human-Autonomous System Interaction and Team Dynamics in Automated Driving Systems
4. Human and AI-Based Communication and Reasoning in Complex Adversarial Domains
5. Evolution of Data Architecture for AI-Augmented Learning
6. Human-Robot Collaboration Using Natural Language in the Read World
7. Human and Large Language Model Workflows for Engineering Open-Worl Enterprise Dynamics
8. A Human-Centered Comparative Study on LLMs in the Fashion Design Process
9. A Distributed Teaming Testbed for Human-Machine Collaboration in Futuristic Space Missions
10. Identifying uncertainty breakpoints for machine handoff to humans
11. The effect of cascades on human-machine-AI-team communications
12. Toward a generalized model for evaluating human-AI team effectiveness
13. Semantics of Foundation Models
14. Toward Human-AI Partnership: from tools to teammates
15. Synergistic Pedagogy: Integrating AI collaborators into Data Science Education
16. AI Fluidity and AI Act Regulation
17. Toward Human-Centric Adaptation: Bidrectional Feedback Loops in Human-Machine teams
18. Human AI Collaboration for Trust Management: Key Roles and Task Domains
19. Formally Situated Human-Machine Control Affordances
Product details
Product details
- Edition: 1
- Latest edition
- Published: January 1, 2027
- Language: English
About the editors
About the editors
WL
William Lawless
MB
Marco Brambilla
Marco Brambilla is full professor at Politecnico di Milano. He is active in research and innovation, both at industrial and academic level. His research interests include data science, software modeling languages and design patterns, crowdsourcing, social media monitoring, and big data analysis. He has been visiting researcher at CISCO, San Josè, and University of California, San Diego. He has been visiting professor at Dauphine University, Paris. He is founder of various startups and spinoffs, including WebRatio, Fluxedo, and Quantia, focusing on social media analysis, software modeling, Mobile and Business Process based software applications, and data science projects. He is author of various international books including Model Driven Software Development in Practice (II edizione, Morgan-Claypool, 2017, adopted in 100+ universities worldwide), Web Information Retrieval (Springer, 2013), Interaction Flow Modeling Language (Morgan-Kauffman, 2014), Designing Data-Intensive Web Applications (Morgan-Kauffman, 2002). He also authored more than 250 research articles in top research journals and conferences. He was awarded various best paper awards and gave keynotes and speeches at many conferences and organisations. He is the main author of the OMG (Object Management Group) standard IFML (Interaction Flow Modeling Language). He participated in several European and international research projects. He has been reviewer of FP7 projects and evaluator of EU FP7 proposals, as well as of national and local government funding programmes throughout Europe. He has been PC chair of ICWE 2008 and ICWE 2021, as well as co-chair of various tracks, conferences and workshops. He is associate editor of various journals and PC member of several conferences and workshops.