"Inferring Work Task Automatability from AI Expert Evidence"
by Duckworth, Paul; Graham, Logan; Osborne, Michael (2019)
Abstract
Despite growing alarm about machine learning technologies automating jobs, there is little good evidence on what activities can be automated using such technologies. We contribute the first dataset of its kind by surveying over 150 top academics and industry experts in machine learning, robotics and AI, receiving over 4,500 ratings of how automatable specific tasks are today. We present a probabilistic machine learning model to learn the patterns connecting expert estimates of task automatability and the skills, knowledge and abilities required to perform those tasks. Our model infers the automatability of over 2,000 work activities, and we show how automation differs across types of activities and types of occupations. Sensitivity analysis identifies the specific skills, knowledge and abilities of activities that drive higher or lower automatability. We provide quantitative evidence of what is perceived to be automatable using the state-of-the-art in machine learning technology. We consider the societal impacts of these results and of task-level approaches.
Links to Reference
- https://doi.org/10.1145/3306618.3314247
- http://dx.doi.org/10.1145/3306618.3314247
- https://dl.acm.org/doi/abs/10.1145/3306618.3314247?casa_token=C6x6k5yUXUUAAAAA:MQDIxE03QOOYZWmfetV-va8xwjQUseX3FaL9R9-0qLJBGtR4w9ZddUXiL5uLK9EC3Q8qsyYfirV0dm8
- https://dl.acm.org/doi/pdf/10.1145/3306618.3314247?casa_token=0M9gBukZwgUAAAAA:z3DYnKBi79oJLZa5eiYyMpPjw-mNuCaxvMLkZnOWJrAsCVrunETx-sQThzg8mnhk0IbPRkAyNMNBacs
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