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–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.

The recent emergence of machine-manipulated media raises an important societal question: How can we know whether a video that we watch is real or fake? In two online studies with 15,016 participants, we present authentic videos and deepfakes and ask participants to identify which is which. We compare the performance of ordinary human observers with the leading computer vision deepfake detection model and find them similarly accurate, while making different kinds of mistakes. Together, participants with access to the model’s prediction are more accurate than either alone, but inaccurate model predictions often decrease participants’ accuracy. To probe the relative strengths and weaknesses of humans and machines as detectors of deepfakes, we examine human and machine performance across video-level features, and we evaluate the impact of preregistered randomized interventions on deepfake detection. We find that manipulations designed to disrupt visual processing of faces hinder human participants’ performance while mostly not affecting the model’s performance, suggesting a role for specialized cognitive capacities in explaining human deepfake detection performance.

Unemployment rates of educated youth are high throughout the Middle East, and female labor force participation is low. We study the impact of a randomized experiment in Jordan designed to assist female community college graduates find employment. One randomly chosen group of graduates was given a voucher that would pay an employer a subsidy equivalent to the minimum wage for up to 6 months if they hired the graduate; a second group was invited to attend 45 hours of employability skills training designed to provide them with the soft skills employers say graduates often lack; a third group was offered both interventions; and the fourth group forms the control group. We find that the job voucher led to a 40 percentage point increase in employment in the short-run, but that most of this employment is not formal, and that the average effect is much smaller and no longer statistically significant 4 months after the voucher period has ended. The voucher does appear to have persistent impacts outside of Amman, where it almost doubles the employment rate of graduates, but this appears likely to largely reflect displacement effects. Soft skills training has no average impact on employment, although again there is a weakly significant impact outside of Amman. The training does lead to graduates believing their life will be better in five years, and to them having better mental health. The results suggest that wage subsidies can help increase employment in the short-term, but are no panacea for the problems of high urban female youth unemployment.

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Employers around the world complain that youth lack the soft skills needed for success in the workplace. In response, a number of employment programs have begun to incorporate soft skills training, but to date there has been little evidence as to the effectiveness of such programs. This paper reports on a randomized experiment in Jordan in which female community college graduates were randomly assigned to a soft skills training program. Despite this program being twice as long in length as the average program in the region, and taught by a well-regarded provider, we find soft skills training does not have any significant employment impact in three rounds of follow-up surveys. We elicit expectations of academics and development professionals and reveal that these findings are novel and unexpected. JEL codes: O12, O15, J08, J16