Applied Responsible AI

We, the research group Applied Responsible AI (ARAI) investigate how (generative) artificial intelligence can be applied, managed, and integrated in real-world contexts: We study AI as part of socio-technical environments - focusing on how people, organizations, and technologies interact to foster reliability, transparency, and value creation. Our research bridges the gap between technical innovation, human interaction, and organizational application through empirical and practical-oriented work. ARAI aimes to examine how AI can enhance collaboration, decision-making, and sustainable value creation in applied settings, such as adaptive workplace technologies, sustainability topics, and human-AI interaction. We further study how AI literacy and user competencies shape the responsible and effective use of AI.

 

 

Head of Area

Thimo Schulz
+49 (721) 608-48382

 thimo.schulz∂kit.edu

 

 

Members

Benjamin Gaube
+49 (721) 608-48374
benjamin.gaube∂kit.edu		 Marvin Motz Marvin Motz
+49 (721) 608-48344
marvin.motz∂kit.edu		 Alexander Grote
+49 (721) 608-48344
 alexander.grote∂kit.edu

 
Tom Luca Weinhardt
+49 (721) 608-48374
tom.weinhardt∂kit.edu		        

 

Student Assistants

  Kübra Nur Çetin
 		   Zeynep Demirel    

Research Areas

  • Adaptive (video) systems: Enhancing teams and organizational collaboration [1]
  • LLMs for Business Development: LLMs for Open Set Recognition Problems in business contexts [1]
  • Smart & Sustainable Finance: Data-Analytics to enhance ESG (Environmental, Social, Governance)-based investment decisions. [1] [2]
  • Detecting Greenwashing: Quantify greenwashing tendencies of firms, using natural language processing and text-mining. [1] [2]
  • AI & Data Literacy: Fostering AI competences through technology-assisted learning formats [1] [2] [3]
  • Data Quality & Lineage:  Open-source Data Lineage to safeguard consumer & organizational interests in cross-party data exchanges. [1]
  • Human-AI Interaction: Investigate cognitive and affective influences of (generative) AI on users to promote wellbeing and performance [1] [2]

Research Methods

  • Experimental and Behavioural Research (Lab and Field)
  • Quantitative Research (e.g. Machine Learning, Panel Studies, Online Surveys)
  • Qualitative Research (e.g. Expert Interviews, Focus Groups, Workshops)
  • Natural Language Processing and Text Mining
  • Design Science Research

Ongoing Projects

Project Cooperations

Ongoing Industrial Cooperations

 

 

Past Industrial Cooperations & Thesis Projects

Former Projects

Open Thesis Positions