Ethical Generative AI: What Kind of AI Results are Desired by Society?

Authors

  • Marc Lehmann Institute for Information Systems, Hof University of Applied Sciences, Hof, Germany
  • René Peinl Institute for Information Systems, Hof University of Applied Sciences, Hof, Germany https://orcid.org/0000-0001-8457-1801
  • Andreas Wagener Institute for Information Systems, Hof University of Applied Sciences, Hof, Germany https://orcid.org/0009-0001-5820-1601

DOI:

https://doi.org/10.34190/icair.4.1.3061

Keywords:

generative AI, ethical considerations, missing society consensus, bias

Abstract

There are many publications talking about the biases to be found in in generative AI solutions like large language models (LLMs, e.g., Mistral) or text-to-image models (T2IMs, e.g., Stable Diffusion). However, there is merely any publication to be found that questions what kind of behavior is actually desired, not only by a couple of researchers, but by society in general. Most researchers in this area seem to think that there would be a common agreement, but political debate in other areas shows that this is seldom the case, even for a single country. Climate change, for example, is an empirically well-proven scientific fact, 197 countries (including Germany) have declared to do their best to limit global warming to a maximum of 1.5°C in the Paris Agreement, but still renowned German scientists are calling LLMs biased if they state that there is human-made climate change and humanity is doing not enough to stop it. This trend is especially visible in Western individualistic societies that favor personal well-being over common good. In this article, we are exploring different aspects of biases found in LLMs and T2IMs, highlight potential divergence in the perception of ethically desirable outputs and discuss potential solutions with their advantages and drawbacks from the perspective of society. The analysis is carried out in an interdisciplinary manner with the authors coming from as diverse backgrounds as business information systems, political sciences, and law. Our contribution brings new insights to this debate and sheds light on an important aspect of the discussion that is largely ignored up to now.

Author Biographies

Marc Lehmann, Institute for Information Systems, Hof University of Applied Sciences, Hof, Germany

Marc Lehmann is a research assistant in the research group “Law in Sustainability, Compliance and IT” at the Institute for Information Systems at Hof University of Applied Sciences. His research interests focus on the areas of data governance and IT law.

René Peinl, Institute for Information Systems, Hof University of Applied Sciences, Hof, Germany

René Peinl studied business information systems, got a PhD in knowledge management and worked in both small and large companies before joining Hof University in 2010 as a profssor. He dedicated his research to deep learning, especially voice assistants in 2018 and has authored numerous scientific articles in this field.

Andreas Wagener, Institute for Information Systems, Hof University of Applied Sciences, Hof, Germany

Andreas Wagener is a political scientist and economist and Professor of Digital Marketing at Hof University. He heads the research group ‘Digital Transformation in Economy and Society’ at iisys - Institute for Information Systems, Hof. His work focuses on the integration of digital technologies into business and political processes.

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Published

2024-12-04