Ethical implications of hybrid teams of humans and artificial intelligence systems (Ethyde)
Year: 2024
Type: other
Abstract: AI-based recommendation and classification systems, especially those relying on data-intensive machine learning methods, are becoming increasingly important in various fields of application. These include various applications in the medical field – from diagnostic support to therapy recommendations. Especially in critical areas such as medicine, human control and supervision are among the key requirements for trustworthy AI systems. In order for human experts to be able to assess and evaluate AI-generated recommendations and classifications, it must be possible to trace the information on which an AI system arrives at a particular output. Hybrid human-AI teams offer the possibility for better decisions to be made in partnership than by humans or AI systems alone. For this to succeed, AI systems must be designed in such a way that people have a certain amount of trust in them on the one hand, but also critically reflect on the output of these systems on the other. This might also be affected by human's knowledge on AI-models and classification systems. The goal of this experiment is to assess the effect of explanations and the possibility of correction on trust and performance in the context of multi-class classification systems - while considering the covariate of AI-expertise.
Source: Open Science Framework
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6962534546
Cites:
Cited by:
Related to: