14.08.2026
Stephan Günnemann Featured in t3n on Data Poisoning and AI Security
Understanding the Growing Threat of Manipulated Data
MCML PI Stephan Günnemann is featured in a recent t3n article examining data poisoning as a growing threat to the security and reliability of AI systems. Data poisoning involves deliberately manipulating data to influence how AI models behave, potentially causing them to produce misleading or harmful results.
Günnemann highlights the broader implications of data poisoning, including its potential use to pursue political influence or economic advantages. The article explores how manipulated data can affect AI-powered systems such as recommendation services and why detecting such attacks remains a significant challenge.
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Transparency for Global Health Aid
MCML PI Stefan Feuerriegel and his team use machine learning to uncover disparities in the global allocation of health aid.
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David Rügamer: We Need Uncertainty Quantification
MCML PI David Rügamer explains why uncertainty quantification is essential for building trustworthy AI and making informed decisions.
