Workshop on Human-Machine Collaboration and Teaming
Abstract
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.
Video
Schedule
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9:55 PM
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10:25 PM
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10:30 PM
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11:30 PM
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12:10 AM
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12:40 AM
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1:10 AM
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1:15 AM
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2:05 AM
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2:40 AM
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4:00 AM
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5:40 AM
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6:00 AM
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