Description:

In recent years, AI agents have rapidly advanced in autonomous task performance and are increasingly deployed in workplace settings, including software development, customer support, logistics, content moderation, and education. As a result, organizations increasingly face the question of whether tasks should be performed by human workers or delegated to AI systems. However, there are still reasons to employ human workers. Human involvement is often justified by superior ethical reasoning, increased trust and accountability, and complementary strengths that can improve joint human–AI performance. These factors are crucial, particularly in high-risk domains such as healthcare, law, finance, and aviation. Consequently, many regulatory frameworks and organizational guidelines mandate human oversight of AI systems. As AI systems increasingly perform core work tasks, human workers are required to collaborate with and oversee these systems, thereby creating new challenges for workers. Since these changes easily result in an unfulfilling and enjoyable work task, the sociotechnical design of human oversight must understand their effects on workers and aim to safeguard long-term well-being. 

The goal of this project is to investigate how human oversight (interactions or interfaces) can be designed to be a fulfilling and enjoyable work task. The project involves designing, implementing, and evaluating a user interface or oversight interaction that supports one of many characteristics (e.g., autonomy, meaningfulness, relatedness) that contribute to “good work”. The findings will contribute to foundational insights into how effective and fulfilling human oversight can be designed. The results may inform the design of future oversight systems, including high-stakes domains such as education, finance, healthcare, or law.

Requirements:

• Very good programming skills (e.g., Web-based, Python)
• Experience or strong interest in conducting empirical user studies and data analysis

References:

• https://doi.org/10.48550/arXiv.2510.19512 
• https://doi.org/10.1145/3630106.3659051 
• https://doi.org/10.1016/j.clsr.2022.105681 

Contact:

• Cedric Faas: faas@cs.uni-saarland.de