Best practices on collaborations between people and AI systems – including those for issues of transparency and trust, responsibility for specific decisions, and appropriate levels of autonomy – depend on a nuanced understanding of the nature of those collaborations.
With the support of the Collaborations Between People and AI Systems (CPAIS) Expert Group, PAI has developed a Human-AI Collaboration Framework, containing 36 questions that identify some characteristics that differentiate examples of human-AI collaborations. We have also prepared a collection of seven case studies that illustrate the Framework and its applications in the real world.
This project explores the relevant features one should consider when thinking about human-AI collaboration, and how these features present themselves in real-world examples. By drawing attention to the nuances – including the distinct implications and potential social impacts – of specific AI technologies, the Framework can serve as a helpful nudge toward responsible product/tool design, policy development, or even research processes on or around AI systems that interact with humans.
As a software engineer from a leading technology company suggested, this Framework would be useful to them because it would enable focused attention on the impact of their AI system design, beyond the typical parameters of how quickly it goes to market or how it performs technically.
“By thinking through this list, I will have a better sense of where I am responsible to make the tool more useful, safe, and beneficial for the people using it. The public can also be better assured that I took these parameters into consideration when working on the design of a system that they may trust and then embed in their everyday life.”
Software Engineer, PAI Research Participant
Case Studies:
To illustrate the application of this Framework, PAI spoke with AI practitioners from a range of organizations, and collected seven case studies designed to highlight the variety of real world collaborations between people and AI systems. The case studies provide descriptions of the technologies and their use, followed by author answers to the questions in the Framework:
- Virtual Assistants and Users (Claire Leibowicz, Partnership on AI)
- Mental Health Chatbots and Users (Yoonsuck Choe, Samsung)
- Intelligent Tutoring Systems and Learners (Amber Story, American Psychological Association)
- Assistive Computing and Motor Neuron Disease Patients (Lama Nachman, Intel)
- AI Drawing Tools and Artists (Philipp Michel, University of Tokyo)
- Magnetic Resonance Imaging and Doctors (Bendert Zevenbergen, Princeton Center for Information Technology Policy)
- Autonomous Vehicles and Passengers (In Kwon Choi, Samsung)