FW-HTF-P/Collaborative Research: Exploring Tools to Help Workers and Organizations Adapt to AI-enabled Robots

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This project will promote exploration of scalable tools to aid workers and organizations adapt to artificially-intelligent robots. In a sharp departure from current robotic systems that have to be programmed for a single manipulation task in a very tightly constrained set of conditions, venture-funded firms are designing and beginning to test qualitatively new robotic technologies that promise to flexibly automate entire classes of embodied tasks in widely divergent conditions. In this likely future, robots will adapt as readily to new repetitive manual tasks as a modern microprocessor adapts to new computational tasks. Such "learning" robots would clearly have profound implications for workers and organizations, but previous research on automation offers only limited guidance on how they will adapt. The researchers have recently begun a nationwide, four-year field study that will identify edge cases in which organizations and low-skill workers achieve unlikely yet systematic success, given the introduction of this disruptive technology. This will allow deriving design constraints for potential solutions from grounded theory, centering on the hard-won, demonstrably successful innovations of a suitably-diverse pool of informants. While existing research stands to unveil the mechanisms behind rare, in vivo learning successes to the world, this FW-HTF-P (Future of Work at the Human-Technology Frontier - Planning) award will assemble a world-class team of researchers who are committed to trying to expand and capitalize upon these mechanisms via new tools. This research has high-impact potential for organizations, lower-skilled workers and policy makers on how to expand and enrich work involving increasingly intelligent systems in the 21st century.With AI in robotics as the technology, humans collaborating with robots as the workers, and organizations employing both the robots and the workers as the context of work, the team of researchers will specifically contact and convene a group of top experts in diverse technical domains including social media, massive open online courseware, crowdsourced knowledge repositories, peer assessment and coaching, user experience design and platforms for on-demand labor, crowdsourcing and innovation challenge execution. Beyond these technical disciplines, the researchers will invite policymakers and technologists, as the pathways to local success will likely be deeply intertwined with legal and commercialization processes. The researchers will begin by sharing very preliminary findings, research questions and objectives from the current study with a select group of such researchers who may have interest in a potential collaboration. The researchers will then extend formal invitations to a workshop to no more than ten potential collaborators. This workshop will be one day in length and will be described as an opportunity to explore and decide upon potential collaborative opportunities related to helping workers and organizations adapt more productively to general-purpose robots. The researchers will explore potentially new organizational theories that take perspectives such as: (a) accounting for success as a learning problem in which robots, workers and organizations learn from each other; (b) the character of learning infrastructures evident in various practices for adapting to learning machines acting as co-workers; (c) how the organization of such learning practices impacts skill changes, role transformations, as well as workers and organizations. The researchers will then solicit participants' input and commitment for tools to scale the successes inherent in the findings and select the tool likely to have the greatest benefit for the most Americans. The researchers will then jointly craft an FW-HTF-R (Future of Work at the Human-Technology Frontier - Research) proposal with interested collaborators that reflects a rigorous test of this tool in real-world settings. The ultimate goal of this project is to develop the necessary research personnel, research infrastructure, and foundational work to expand the opportunities for studying future technology, future workers, and future work at the level of a FW-HTF full research proposal.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.