Sonja Zillner

Lead Trustworthy AI , Siemens

Sonja Zillner studied mathematics and psychology at the Albert-Ludwig-University Freiburg, Germany and accomplished her PHD in computer science specializing on the topic of Semantics at Technical University in Vienna.  Since 2020 she is leading the Trustworthy AI Core Technology Module at Siemens Technology. From 2016 to 2019 she was invited to consult the Siemens Advisory Board in strategic decisions regarding artificial intelligence. She is chief-editor of the Strategic Research Innovation and Deployment Agenda of the new Partnership in AI, Data and Robotics, leading editor of the Strategic Research and Innovation Agenda of the Big Data Value Association (BDVA). She is author of more than 80 publications and more than 25 patents in the area of semantics, artificial intelligence and data-driven innovation.

Sonja is speaking at

Focus Track 3 - Market uptake: Bringing AI and Data Sciences to Practice
November 3, 2020
3:30 pm - 5:30 pm



The Data-driven Innovation (DDI) Workshop is based on the (DDI) Framework addressing the challenges of identifying and exploring data-driven innovation in an efficient manner. It guides entrepreneurs in scoping promising data-driven business opportunities by reflecting the dynamics of supply and demand by investigating the co-evolution and interactions between the scope of the offering (supply) and the context of the market (demand) in systematic manner.

In this workshop, business champions, entrepreneurs and interested techies will gain practical experiences of how to use the DDI Framework and Canvas for the continuous analysis of all influencing factors of data-driven business opportunities. The participants of the workshops will get to know a set of methods and tools that will guide them in redefining their own data-driven business opportunity.

We will take the opportunity of this EBDVF workshop to officially launch the DDI canvas site. We encourage you to contact us to assist you in using DDI, to share with us your return of experience to help us to improve the methodology or provide domain specific know how.

The DDI framework was developed and tested in the context of the Horizon 2020 BDVe project[1] and is backed by empirical data and scientific research encompassing a quantitative and representative study of more than 90 data-driven business opportunities. The results of the research study guided the fine-tuning and updating the DDI framework as well as helped to identify success patterns of successful data-driven innovation. Currrently the DDI framework is used to run workshops with PPP projects, data-driven start-ups, SMEs and with corporates. It consists of a

  • The DDI Canvas guides you in exploring all relevant dimensions on the supply and demand side of a data-driven innovation in systematic manner.
  • The DDI Navigator will support you in exploring each dimension in more detail by carefully selected deep dives.
  • Specific DDI Tools will help you to work through each of the eight DDI dimensions, producing a conclusive set of results that will guide a company-specific setup of new, data-based products and services.
The DDI Framework is based on a conceptual model in form of an ontology with a set of categories and concepts describing all relevant aspects of data-driven business opportunities. Its categories are divided into supply side and demand side aspects. On the supply side the focus is on the development of new offerings. For a clearly defined value proposition, this includes the identification of and access to required data sources, as well as the analysis of underlying technologies. On the demand side the focus is on the dynamics of the addressed markets and associated ecosystems. The analysis includes the development of a revenue strategy, a way forward of how to harness network effects as well as an understanding of the type of business. As data-driven innovations are never done in isolation, the identification and analysis of potential development partners as well as partners in the ecosystem help to align / balance the supply and demand aspects in a way that its competitive nature will stand out. 

Focus Track 2 - Technology, Platforms and Trust
November 4, 2020
12:00 pm - 1:30 pm


  • Prof. Ray Walshe (Speaker) Asst Professor of Emerging Technology Standardizartion, ADAPT Research Centre at Dublin City University
  • Pat McCarthy (Speaker) VP Strategy and Business Planning, Huawei Ireland Research Center
  • Silvana Muscella (Speaker) CEO, Trust-IT
  • Rob Brennan (Speaker) Assistant Professor, ADAPT, Dublin City University
  • Sonja Zillner (Speaker) Lead Trustworthy AI , Siemens
  • Lindsay Frost (Speaker) Chief Standardisation Eng., NEC Laboratories Europe GmbH
  • Ashok Ganesh (Speaker) Director Market Perspectives & Innovation, CEN-CENELEC


AI standards work is ongoing at ISO and IEEE primarily focusing on standards to improve market efficiency and address ethical concerns. Further policy objectives, like responsible deployment and use of safety specifications in fundamental research are absent and leading AI research organizations that have concerns about such policy objectives are not involved in ongoing standardization efforts. The panel will discuss Standards and the link with international strategies and policies.

Focus Track 4 - New challenges ahead: Data, AI and the new Society
November 5, 2020
12:00 pm - 1:30 pm



Recently the EC has issued a consultation in which the need for AI regulation played an important role.  There were 1200 contributions, so 50% more than the Data Strategy consultation. Some 350 companies (the same as data sharing), 400 from citizens (!), and 150 from research institutions. So, AI is really something that keeps citizens busy! Like with data sharing, everybody agrees that the EU should ‘do more’. However, there are highlights. Skills is found very important (89%) to address, also testing facilities (76%) and European Data spaces (75%), Improving existing networks on AI found much support (86%) but a lighthouse research center much less (64%). The section on the risks of AI has been very well addressed by the respondents. This is clearly a concern with many people.
However, the way forward (introduce new regulation, adapt current legislation) is not decided, and there was even less agreement on the identification of high-risk AI applications, how to define them and what to do with them.
The debate on how to move forward with AI applications in terms of requirements, risk, and labelling/certification is clearly still open. In this session we aim to continue the debate and bring it a step further.
The session aims to probe further into the topic ‘AI regulation’ and provide the audience with insights and directions that help them to further shape this EU-wide debate. Specifically, since many of these topics are expected to play a role in the forthcoming Work Programs, the session will create additional links between researchers and practitioners from different backgrounds.
Further description:
The speakers / panelists will be proposed the following questions:
  1. is there a need for a regulatory system for AI systems in Europe? What are the advantages and disadvantages of regulating AI systems?
  2. which AI systems should be regulated? Which criteria should be used to define the need for regulation? 
  3. what types of regulation exist and what is considered to be effective?
  4. how can regulation of AI systems be successful? 
  5. how is the certification of AI systems related to data?
  6. what role do ethical considerations play in the regulation of AI systems?

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