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Legal Aspects and Society


Legal Challenges and Solutions:
Privacy by Design
Transparency and
Trustworthy AI
Bias Analytics: Machine Learning
and Minority Protection


Legal Challenges and Solutions in Big Data Technologies: Privacy by Design
(Prof. Müller-Mall)

  • Motivation
    • Big Data and AI technologies play an increasingly and important role in both individual lives and society
    • However, the omnipresence of these technologies entails numerous risks with regard to data protection and data security
    • Developing technology can therefore not be separated from a further development and investigation of legal framework conditions
  • Aim
    • Conceptual elaboration of the Privacy by Design-approach, which is among others anchored in the European General Data Protection Regulation
    • Identification of eminent tensions between data protection principles and the demands of Big Data/AI technologies
    • Inventory and legal evaluation of currently available privacy enhancing technologies
    • Modelling of an enhanced Privacy by Design-approach tailored to Big Data/AI requirements


Transparency as a Fundamental Principle of Trustworthy AI: Regulatory Framework and Challenges (Prof. Lauber-Rönsberg)

  • Ethics Guidelines for Trustworthy AI:
    • Transparency is a key requirement that AI systems should meet in order to be deemed trustworthy.
    • Implementation: Humans need to be aware that they are interacting with an AI system; transparency of the data, the system’s capabilities and AI business models; explainable AI.
  • Legal Framework:
    • Data Protection Laws: Scope and efficiency of information obligations? Specific obligations for automated decision- making-systems about the “logic involved”?
    • Fair Trading and Consumer Protection Laws: Data-driven marketing tools are shaping decision-making architectures, thus influencing consumers’ decisions. Legal Regulation of subliminal marketing practices?

Bias Analytics: Machine Learning and Minority Protection (Jun.-Prof. Martin Potthast)

This project studies the analysis and the management of bias in sociotechnic systems that employ machine learning as a tool.

Case Study 1: Vandalism on Wikidata

The damage control system of Wikidata is biased against anonymous editors and newcomers.

 Case Study 2: The Direct Answer Dilemma

  • Conversational search interfaces are “narrow”
    • Users are impatient
    • There is only room for one or two search results
    • Questions need to be answered directly
  • Direct answers
    • A direct answer is not necessarily correct
    • Users still presume correctness
    • This presents a strong bias against diversity
    • The dilemma: fast answers vs. accurate ones