Machines That Know What Matters: Engineering Awareness of Social Values
Carles Sierra, IIIA-CSIC, Spain
Responsible AI Engineering through the Systematic Management of Generative AI Risks
Sonja Zillner, Siemens AG, Germany
Available Soon
Barbara Pernici, Politecnico di Milano, Italy
Machines That Know What Matters: Engineering Awareness of Social Values
Carles Sierra
IIIA-CSIC
Spain
Brief Bio
Carles Sierra is the Director of the Artificial Intelligence Research Institute (IIIA) at the Spanish National Research Council (CSIC) in Barcelona. He served as President of the European Association for Artificial Intelligence (EurAI) from 2020 to 2024. Since 1985, he has made significant contributions to AI in areas such as Knowledge Representation, Auctions, Electronic Institutions, Autonomous Agents, and Multiagent Systems. He has also served as Editor-in-Chief of the journal Autonomous Agents and Multi-Agent Systems (JAAMAS). In 2019, he was awarded the ACM/SIGAI Autonomous Agents Research Award in recognition of his outstanding contributions to the field.
Abstract
Ethics in AI raises big questions about how we design and use autonomous systems responsibly. In this talk, I’ll focus on one core challenge: how to engineer moral values into intelligent agents. We’ll explore how human communities define social values, how these become norm-based social contracts, and how we can build computational models that make machines aware of them.
Responsible AI Engineering through the Systematic Management of Generative AI Risks
Sonja Zillner
Siemens AG
Germany
Brief Bio
Sonja Zillner is the Principal for Industrial Trustworthy Artificial Intelligence at Siemens Foundational Technology. She is Head of the Core Company Technology Module "Trustworthy AI" at Siemens AG as well as leading the design, development, and implementation of the Generative AI Risk Management Process for Siemens industrial products. She earned her doctoral degree in 2005 from the Technical University of Vienna, and has held an honorary professorship at the Technical University of Munich since 2021.
Abstract
Artificial Intelligence (AI) presents significant opportunities for new business ventures within the industrial domain. However, its adoption introduces a broad spectrum of risk sources across dimensions such as cybersecurity, reliability, robustness, safety, transparency, or human oversight. Simultaneously, organizations are confronted with an increasing amount of digital and AI-based legislation in the international environment.
This leads to a fragmented landscape of legal and risk-related requirements, often inconsistent, overlapping, and duplicated. This results in high uncertainty and bureaucratic burden during the development and deployment of AI products. To address these challenges, a systematic, responsible, and efficient approach is required to handle the complex legal requirements as well as the emerging wide range of risk sources.
Responsible AI Engineering describes such a systematic approach, enabling the efficient and innovative development of AI products while ensuring compliance with the complex and fragmented legal landscape.
In this presentation, I will introduce the Siemens Generative Risk Management Process as an example of a responsible AI engineering approach. This demonstrates how organizations can drive AI innovation forward in a compliant and risk-aware manner.
Available Soon
Barbara Pernici
Politecnico di Milano
Italy
Brief Bio
Barbara Pernici is full professor of Computer Engineering at the Politecnico di Milano. Her research interests include workflow and information systems design, cooperative information systems, adaptive information systems, service engineering and web services, data quality, and computer based design support tools, green information systems. She has published more than 50 papers in international journals, co-edited 26 books, and published about 350 papers at international level. She served as elected chair of TC8 Information Systems of the International Federation for Information Processing (IFIP) and of IFIP WG 8.1 on Information Systems Design.