Bridging the Gap Between AI and Reality

This open access book constitutes revised selected papers from the First International Conference on Bridging the Gap between AI and Reality, AISoLA 2023, which took place in Crete, Greece, in October 2023.

The papers included in this book focus on the following topics: The nature of AI-based systems; ethical, economic and legal implications of AI-systems in practice; ways to make controlled use of AI via the various kinds of formal methods-based validation techniques; dedicated applications scenarios which may allow certain levels of assistance; and education in times of deep learning.

This book is open access, which means that you have free and unlimited access

Inhalt

Digital Humanities and Cultural Heritage in AI and IT-enabled Environments.- Common Language for Accessibility, Interoperability, and Reusability in Historical Demography.- Coding historical causes of death data with Large Language Models.- Teaching the specialized language of Mathematics with a data-driven approach: what data do we use?.- Interoperating Civil Registration of Death and Census Data: Old Age and Marriage as Categories of Analysis.- From Data Science to Modular Workflows - Changing Perspectives from Data to Platform: DBDIrl 1864-1922 Case Study.- Mapping Madness: HGIS and the granular analysis of Irish patient records.- Digitised historical sources and non-digital humanists: an interdisciplinary challenge?.- Using Passive Sensing to Identify Depression.- The GraphBRAIN Framework for Knowledge Graph Management and its Applications to Cultural Heritage.- Challenges for AI in Healthcare Systems.- Towards a Multi-dimensional Health Data Analysis Framework.- Future Opportunities for Systematic AI Support in Healthcare.- CRISP-PCCP A Development Methodology Supporting FDA Approval for Machine Learning Enabled Medical Devices.- Model Driven Development for AI-based Healthcare Systems: A Review.- Balancing Transparency and Risk: An Overview of the Security and Privacy Risks of Open-Source Machine Learning Models.- AI-related risk and uncertainty.- Leveraging Actionable Explanations to Improve People's Reactions to AI-based Decisions.- From Explanation Correctness to Explanation Goodness: Only Provably Correct Explanations can Save the World.- Thinking Outside the Box? Regulatory Sandboxes as a Tool for AI Regulation.- AI and Democratic Equality: How Surveillance Capitalism and Computational Propaganda Threaten Democracy.- Safeguarding AI-Based Software Development and Verification using Witnesses (Position Paper).- End-to-End AI Generated Runtime Verification from Natural Language Specification.- AI-Assisted Programming with Test-based Refinement.- Safer Than Perception: Increasing Resilience of Automated Vehicles Against Misperception.- Towards ML-Integration and Training Patterns for AI-Enabled Systems.- The Reachability Problem for Neural-Network Control Systems.