Research Agenda

Our shared research question is:

How can we ensure that big data analysis and algorithm-based decision-making are unbiased and nondiscriminatory?

Our core systematic commitment is:

It is not just the algorithms, but rather the entire system of computer predictions and human decisions, which must qualify as unbiased and nondiscriminatory.

Working Groups


Main task

Focus points

and ethical issues

Institute of Philosophy (IPhil)


In-depth analysis of the cognitive and moral dimensions
of basic notions and principles concerning bias and discrimination in big data and algorithmic processing

• Human biases and computer biases
• Alternative measures of statistical fairness
• Debiasing strategies and affirmative action
• Justification and

Legal challenges

Institute for Legal Informatics (IRI)

Comprehensive analysis of legal
standards and questions regarding the application of AI under the overarching heading of bias and discrimination

• Data protection law
• Consumer, competition and anti-discrimination law

Technical aspects
and solutions

Research Center L3S


Institute for Information Processing (TNT)



Derivation of strategies,
methods and tools for
identifying computer bias and discrimination, for ensuring statistical fairness in the operations of AI systems, and for debiasing AI systems

• Computer bias, esp. caused
by imbalanced data or rare classes
• Statistical fairness, esp. for non-stationary data
• Debiasing strategies, esp. focusing on explainability