In the past, machine learning and decision-making have been treated as independent research areas. However, with the increasing emphasis on human-centered AI, there has been a growing interest in combining these two areas. Researchers have explored approaches that aim to complement human decision-making rather than replace it, as well as strategies that leverage machine predictions to improve overall decision-making performance.
Despite these advances, our understanding of this topic is still in its infancy and that there is much to be learned about the interplay between human and machine learning and decision making. To facilitate this exploration, there is a need for interdisciplinary events where researchers from multiple fields can come together to share their perspectives and insights.
The goal of this workshop is to bring together researchers with diverse backgrounds and expertise to explore effective hybrid machine learning and decision making. This will include approaches that explicitly consider the human-in-the-loop and the downstream goals of the human-machine system, as well as decision making strategies and HCI principles that promote rich and diverse interactions between humans and machines. Additionally, cognitive and legal aspects will be considered to identify potential pitfalls and ensure that trustworthy and ethical hybrid decision-making systems are developed.