Many studies have focused on estimating the impact of automation on work around the world with results ranging widely. Despite the disagreement about the level of impact that automation will have, experts agree that new technologies tend to be applied to every economic sector, thus impacting work regardless of substituting or complementing it. The purpose of this study is to move on from the discussion about the size of the impact of automation to understanding the main social impacts that automation will cause and what actions should be taken to deal with them. For this purpose, we reviewed literature about technological unemployment found in Scopus and Web of Science published since 2000, presenting an academic view of the actions necessary to deal with the social impact of automation. Our results summarize causes, consequences, and solutions for the technological unemployment found in the literature. We also found that the literature is mainly concentrated on the areas of economy, sociology, and philosophy, with the authors situated in developed economies such as the USA, Europe, and New Zealand. Finally, we present the research agenda proposed by the reviewed papers that could motivate new research on the subject.

Work has been continuously changing throughout history. The most severe changes to work occurred because of the industrial revolutions, and we are living in one of these moments. To allow us to address these changes as early as possible, mitigating important problems before they occur, we need to explore the future of work. As such, our purpose in this paper is to discuss the main global trends and provide a likely scenario for work in 2050 that takes into consideration the recent changes caused by the COVID-19 pandemic. The study was performed by thirteen researchers with different backgrounds divided into five topics that were analyzed individually using four future studies methods: Bibliometrics, Brainstorming, Futures Wheel, and Scenarios. As the study was done before COVID-19, seven researchers of the original group later updated the most likely scenario with new Bibliometrics and Brainstorming. Our findings include that computerization advances will further reduce the demand for low-skill and low-wage jobs; non-standard employment tends to be better regulated; new technologies will allow a transition to a personalized education process; workers will receive knowledge-intensive training, making them more adaptable to new types of jobs; self-employment and entrepreneurship will grow in the global labor market; and universal basic income would not reach its full potential, but income transfer programs will be implemented for the most vulnerable population. Finally, we highlight that this study explores the future of work in 2050 while considering the impact of the COVID-19 pandemic.

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During a Futures Study, researchers analyze a significant quantity of information dispersed across multiple document databases to gather conjectures about future events, making it challenging for researchers to retrieve all predicted events described in publications quickly. Generating a timeline of future events is time-consuming and prone to errors, requiring a group of experts to execute appropriately. This work introduces NERMAP, a system capable of semi-automating the process of discovering future events, organizing them in a timeline through Named Entity Recognition supported by machine learning, and gathering up to 83% of future events found in documents when compared to humans. The system identified future events that we failed to detect during the tests. Using the system allows researchers to perform the analysis in significantly less time, thus reducing costs. Therefore, the proposed approach enables a small group of researchers to efficiently process and analyze a large volume of documents, enhancing their capability to identify and comprehend information in a timeline while minimizing costs.