Human-AI Collaboration: Exploring interfaces for interactive Machine Learning

Abstract

Human-AI collaboration embodies the idea that AI systems and humans work together synergistically, leveraging each other's strengths to achieve more than what either can do in isolation. It's a shift from the traditional notion of AI as a replacement for human labour to a partnership where AI augments human capabilities. This collaboration is founded on trust, where humans rely on AI for data-driven insights, and AI relies on human expertise for nuanced decision-making. In the ever-evolving landscape of technology, one of the most profound transformations is the collaboration between humans and artificial intelligence (AI). This collaboration is further facilitated and enhanced through the interfaces of machine learning (ML), where humans interact with AI algorithms to achieve collective goals. As artificial intelligence (AI) continues to advance, the synergy between humans and machines becomes increasingly significant. This paper delves into the evolving landscape of Human-AI Collaboration, with a particular focus on interactive Machine Learning (iML) interfaces. In a world where AI systems permeate numerous facets of society, understanding how humans can effectively collaborate with AI through intuitive interfaces is paramount. This research comprehensively explores the pivotal role of user interfaces in facilitating collaborative machine learning. It encompasses an analysis of existing iML interfaces, user experience evaluations, and the proposition of innovative design principles to enhance the effectiveness of AI as a collaborative tool. This study contributes to advancing our understanding of harnessing AI's potential to empower users in various domains.