Keynote Lectures

Machines That Know What Matters: Engineering Awareness of Social Values
Carles Sierra, IIIA-CSIC, Spain, Spain

Responsible AI Engineering through the Systematic Management of Generative AI Risks
Sonja Zillner, Siemens AG, Germany, Germany

On the Challenges of Sustainable Data Preparation
Barbara Pernici, Politecnico di Milano, Italy, Italy

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.

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.

Abstract
Processing datasets to achieve high data (and metadata) quality is mandatory in modern applications. However, the data preparation activities that are needed to reach such levels may easily become unsustainable due to, for example, resource intensity or scalability challenges. Moreover, some preparation efforts may become unnecessary if they result in negligible improvements or duplicate actions. The talk examines the sustainability aspects of data preparation through the lens of a circular economy. Within the data landscape, this perspective encourages practices that minimize waste, extend the data life cycle, and maximize reuse. We explore these practices and their impact on selecting and configuring effective data preparation strategies to design sustainable, high-quality data preparation pipelines. To this end, we present an evaluation model that integrates data quality metrics with sustainability parameters for human and computational tasks. Finally, we discuss the main challenges that are present in the evaluation of sustainability and data quality.