IKAHAA

Alignment between AI agents and their users is a key open problem in the field of AI. Misaligned AI systems are not only unusable, but can often have dramatic safety consequences, e.g., in safely-critical or high-stakes situations. Although widely researched, the problem of AI alignment has to date been mostly explored from unidirectional perspectives, looking at isolated problems: either aligning AI systems to human values, goals, and preferences, or training humans to align with AI systems capabilities and idiosyncrasies. Furthermore, existing work tends to consider this alignment as a finite, pre-deployment process. Instead, this project aims to change this vision by reframing alignment as a bidirectional and continual process where both the agent and the human interact during deployment to align their knowledge. Taking inspiration from the concept of Johari windows from interpersonal communication theories in psychology, we will tackle three knowledge misalignment situations — knowledge only known by the agent, knowledge only known by the human, and knowledge unknown by both the agent and the human — and three types of knowledge — knowledge about the agent, about the human, and about the task at hand. Contextualizing this research specifically in human-robot interaction, we will research novel behaviors that an interactive robot, as a type of embodied AI agent, can take to align these different types of knowledge in the different misalignment situations. Our key premise is that AI agents can achieve alignment through proactivity, by 1) sharing relevant knowledge to the user, 2) soliciting knowledge from the user, and 3) supporting shared knowledge discovery. This project will break new ground in AI alignment research, distinguishing itself by its unique user-centered and multidisciplinary approach, combining insights about human interpersonal communication and interactive AI techniques. Concretely, this project will create novel proactive strategies for human-AI alignment through interaction with a focus on robot assistants as a domain of investigation. These strategies will be created by leveraging recent advances in interactive machine learning and end-user programming methods, and will be systematically tested with users. This project aims at transforming passive prompt-responding agents into proactive aligning agents that leverage user feedback to better align with them, while shaping their expectations to match system capabilities. Finally, the outputs of this Dutch-Swiss collaborative project will be disseminated to the wider international community through open-access publications and datasets, and outreach activities.