Blogs on bias » Lamarr Institute

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Conscious use of Large Language Models: How to avoid biases?

Dr. Anna Lena Emonds, Julia Kaballo,

17. July 2024

Bias in training data of large language models is a problem. Learn to recognize and minimize the effects of unconscious biases to achieve fairer and more diverse results. ...

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Ethical Use of Training Data: Ensuring Fairness and Data Protection in AI

Thomas Dethmann, Jannis Spiekermann,

03. July 2024

Discover how ethical use of training data minimizes bias, ensures data protection, and fosters trust in Artificial Intelligence....

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AI changes in the workplace and possible gender-related effects

Dr. Jörg Weingarten, Caroline Heß, Deutscher Gewerkschaftsbund ,

07. September 2022

In cooperation with the State Women's Council RLP: The post by our guest authors from the DGB NRW outlines the topic of AI in the workplace and answers the question of what possible gender-specific effects it can have....

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How will AI change schools and educational institutions?

Center of Advanced Internet Studies (CAIS),

31. August 2022

In cooperation with the Landesfrauenrat RLP: This article takes a look at the question of how AI can change schools and educational institutions and highlights the possibilities, limits and framework conditions of intelligent educational technologies....

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How does Artificial Intelligence change medical care and diagnosis?

Dario Antweiler,

24. August 2022

In cooperation with the Landesfrauenrat RLP: This article takes a look behind the scenes of Artificial Intelligence in medicine and shows how AI is changing medical care and diagnosis....

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That wasn’t me! – Who is responsible for AI-supported decisions?

Prof. Dr. Eva Schmidt,

08. December 2021

The use of Artificial Intelligence systems makes it more difficult for us to hold people responsible for wrong decisions. The integration of human actors and explainable AI systems makes it possible to attribute responsibility in AI-supported decision-making processes....

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BlogResearch

Data Annotation for Computer Vision: How Power Shapes Datasets and Systems

Milagros Miceli, Martin Schuessler,

04. August 2021

Within Computer Vision, humans define how machines interpret the world. Researchers at the Weizenbaum Institute identified power differentials and commercial interests in data annotation that influence how systems learn to “see“....