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Solve complex societal problems with artificial intelligence

SAP focuses on developing artificial intelligence (AI) to improve people’s lives by helping protect the environment and build fairer societies.

Luka Mucic
Chief Financial Officer

“SAP considers the ethical use of data a core value. We want to create software that enables the intelligent enterprise and actually improves people’s lives. Such principles will serve as the basis to make AI a technology that augments human talent.”

SAP’s guiding principles for artificial intelligence (AI)

We are driven by our values

We design for people

We enable business beyond bias

We strive for transparency and integrity in all that we do

We uphold quality and safety standards

We place data protection and privacy at our core

We engage with the wider societal challenges of AI

SAP AI Ethics Advisory Panel

Prof. Dr. theol. Peter Dabrock
Chair of Systematic Theology (Ethics)
University of Erlangen-Nuremberg
Dr. Susan Liautaud
Lecturer in Public Policy and Law
Stanford University
Prof. Dr. Helen Nissenbaum
Professor, IS
Cornell Tech
Dr. Nicholas Wright
Consultant at Intelligent Biology
Affiliated Scholar at Georgetown University Medical Center
Honorary Research Associate at University College London
Paul Twomey
Cofounder, Stash.Global
Founding figure of ICANN
Co-founder of STASH

SAP AI Ethics Steering Committee

Alexander Lingg
Head of SAP User Experience
Mathias Cellarius
Data Protection Officer
Head of Data Protection and Privacy
Sebastian Wieczorek
Vice President for Artificial Intelligence Technology
Peter Selfridge
Global Head of Government Affairs
Freek Staehr
Head of Global Legal
Commercial and Operations
Maggie Buggie
Global Head of Innovation
Services Strategy
Daniel Schmid
Chief Sustainability Officer
Alexandra Seemann
Legal Department Manager / German
Labor Relations, Labor & Social Law
Feiyu Xu
Global Head of Artificial Intelligence,
Martin Will
Head of Innovation Center


Moin Nabi
Senior Research Scientist at SAP Machine Learning Research

Preserve privacy in machine learning

Review approaches for learning as much as possible from a crowd, without revealing information from an individual member, in collaborative machine learning.

Tassilo Klein
Senior Researcher of Machine Learning

Implement a private federated learning approach

Learn how federated learning is helping safeguard privacy in machine learning by preventing the tracing of individual clients contributing to the model.

Machine Learning Research

Meet privacy requirements with a private cloud

See how using a virtual private cloud infrastructure can help you handle Big Data effectively, while establishing robust data safeguarding measures.  

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