Machine learning (ML) approaches can support decision-making in key societal settings including healthcare and criminal justice, empower creative discovery in mathematics and the arts, and guide educational interventions. However, deploying such human-machine teams in practice raises critical questions, such as how a learning algorithm may know when to defer to a human teammate and broader systemic questions of when and which tasks to dynamically allocate to a human versus a machine, based on complementary strengths while avoiding dangerous automation bias. Effective synergistic teaming necessitates a prudent eye towards explainability and offers exciting potential for personalisation in interaction with human teammates while considering real-world distribution shifts. In light of these opportunities, our workshop offers a forum to focus and inspire core algorithmic developments from the ICML community towards efficacious human-machine teaming, and an open environment to advance critical discussions around the issues raised by human-AI collaboration in practice.