@conference {Das2020,
	title = {Learning occupational task-shares dynamics for the future of work},
	booktitle = {AIES 2020 - Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society},
	year = {2020},
	pages = {36{\textendash}42},
	abstract = {The recent wave of AI and automation has been argued to differ from previous General Purpose Technologies (GPTs), in that it may lead to rapid change in occupations{\textquoteright} underlying task requirements and persistent technological unemployment. In this paper, we apply a novel methodology of dynamic task shares to a large dataset of online job postings to explore how exactly occupational task demands have changed over the past decade of AI innovation, especially across high, mid and low wage occupations. Notably, big data and AI have risen significantly among high wage occupations since 2012 and 2016, respectively. We built an ARIMA model to predict future occupational task demands and showcase several relevant examples in Healthcare, Administration, and IT. Such task demands predictions across occupations will play a pivotal role in retraining the workforce of the future.},
	keywords = {AI, automation, future of work, Occupational Task Demands},
	isbn = {9781450371100},
	doi = {10.1145/3375627.3375826},
	author = {Das, Subhro and Steffen, Sebastian and Clarke, Wyatt and Reddy, Prabhat and Brynjolfsson, Erik and Fleming, Martin}
}
@article {Frank2019,
	title = {Toward understanding the impact of artificial intelligence on labor},
	journal = {Proceedings of the National Academy of Sciences},
	volume = {116},
	year = {2019},
	month = {apr},
	pages = {6531{\textendash}6539},
	abstract = {<p>Rapid advances in artificial intelligence (AI) and automation technologies have the potential to significantly disrupt labor markets. While AI and automation can augment the productivity of some workers, they can replace the work done by others and will likely transform almost all occupations at least to some degree. Rising automation is happening in a period of growing economic inequality, raising fears of mass technological unemployment and a renewed call for policy efforts to address the consequences of technological change. In this paper we discuss the barriers that inhibit scientists from measuring the effects of AI and automation on the future of work. These barriers include the lack of high-quality data about the nature of work (e.g., the dynamic requirements of occupations), lack of empirically informed models of key microlevel processes (e.g., skill substitution and human{\textendash}machine complementarity), and insufficient understanding of how cognitive technologies interact with broader economic dynamics and institutional mechanisms (e.g., urban migration and international trade policy). Overcoming these barriers requires improvements in the longitudinal and spatial resolution of data, as well as refinements to data on workplace skills. These improvements will enable multidisciplinary research to quantitatively monitor and predict the complex evolution of work in tandem with technological progress. Finally, given the fundamental uncertainty in predicting technological change, we recommend developing a decision framework that focuses on resilience to unexpected scenarios in addition to general equilibrium behavior.</p>},
	issn = {0027-8424},
	doi = {10.1073/pnas.1900949116},
	url = {http://www.pnas.org/lookup/doi/10.1073/pnas.1900949116},
	author = {Frank, Morgan R and Autor, David and Bessen, James E and Brynjolfsson, Erik and Cebrian, Manuel and Deming, David J and Feldman, Maryann and Groh, Matthew and Lobo, Jos{\'e} and Moro, Esteban and Wang, Dashun and Youn, Hyejin and Rahwan, Iyad}
}
@article {2018,
	title = {The productivity j-curve: How intangibles complement general purpose technologies},
	year = {2018},
	institution = {National Bureau of Economic Research},
	address = {Cambridge, MA},
	doi = {10.3386/w25148},
	author = {Brynjolfsson, Erik and Rock, Daniel and Syverson, Chad}
}
@article {1999,
	title = {Information technology, workplace organization and the demand for skilled labor},
	year = {1999},
	institution = {National Bureau of Economic Research},
	address = {Cambridge, MA},
	doi = {10.3386/w7136},
	author = {Bresnahan, Timothy and Brynjolfsson, Erik and Hitt, Lorin}
}
