Skill complexity and labor resilience in the future of work

25 Sep 2020 - Arun Balajiee

Talk: Skill complexity and labor resilience in the future of work

Speaker: Morgan Frank

Date: 09/25/2020

The theme of the talk was to apply existing ideas in AI to understand the dynamics of the labor market. Broadly categorized as computational social science, the people like Dr. Frank working in this area of research usually look for patterns in human cooperation, the general beliefs of the people in localized regions such as cities and the different aims of the skilled/unskilled workforce. Of late, with the advancement in AI technologies, there has been a lot of apprehension towards the physical skilled workers (such as warehouse workers) towards being displaced by robots from their jobs and high-skilled workers (such as surgeons) seem to be assisted by these technologies. People like Dr. Frank try to apply “AI for good” from the standpoint of using AI to understand the trends and be able to balance out the skills and people with their goals and career paths they want to choose. While there is the idea of social impact of AI such facial recognition systems not being racially biased against certain communities because of they way their models are trained, Dr. Frank and others working in this area are trying to actively mitigate this from being a hindrance for all types of workers – skilled or unskilled.

In a series of videos, Dr. Frank introduced the idea of utilizing AI to be able to match unskilled workers to the skilled workers and show that it is possible to reduce the economic value of skilled labors when compared with the unskilled workers and the unskilled workers gaining new skills to be able to find jobs that could use those skills. This goes to show that technology can complement a skilled work and really put people from different levels of skills and interests to be all put in the same categories in terms of find work. The knowledge from different fields is transferrable if the right technological tools are used.

With this short introduction, Dr. Frank dived deeper into breaking down the importance of understanding local labor market trends in different local pocket such as cities and how nearby cities could benefit from the skills of the people in the highly economically well to do cities. This could be possible by “embedding” the cities as nodes in a graph with a certain weight based on the skills of the people in the population and the interconnectedness of those skills. For example, relation between financial manager with people who need help with managing finance, etc. Now based on othese relations built using predictive machine learning models, the challenge is next to be able to connect different occupations that people pursue in these well-todo ciites with the skills of the people in the cities nearby. Further, Dr. Frank and colleagues observe the trends in worker mobility to certain cities and the idea of permanent immigration in relation to the work and occupations that can be sought by the workers based on their skills.

To connect different occupations of people, Dr Frank et al. work towards building a network of nodes that are highly polarized between cognitive (physical, social and mental skills in work) to physical ( skills the require physical dexterity, manual labor etc) and see how connecting the ecological co-existence of these different occupations could benefit in improveing the economic climate of the city.

Dr. Frank also discussed the trends and impact of different economic climates before and after COVID and how the workers migration, mobility changed based on these external factors. He also explained the different levels of economic prosperity and loss of the same in different cities based on these different trends

In conclusion, it is really important to be able to understand that the techonlogy is a tool that has to be used wisely. While on the one end AI as tool has been abused towards discrimination and displacement of work, on the other hand AI could be used to make people more connected by observing trends and patterns in data that are not only beneficial to the individuals but to a whole ecological and econmomic climate of localized regions and broadly the nation too. By listening to Dr. Frank’s talk and his passion towards making AI that is beneficial to people, not just the “elite” high skilled workers in the society, there was an optimistic future that I could envision where everyone could prosper and no one had to go bed hungry.