(in Polish) Praca w dobie AI: dane, zmiany, wyzwania 2400-ZEWW963-OG

During the course we will explore the impact of technology, automation and AI on labor markets. On the one hand, we will focus on reviewing and critically analysing recent reports and empirical studies. On the other - we will work with real data, process and interpret it ourselves. So if you want to understand how technology is changing the world of work - based on data and research, not just media headlines - this class is for you.
In the first part of the course, we will:
Review empirical studies and reports from international institutions analysing the effects of technology on labour markets,
Discuss the mechanisms behind these changes and consider why, despite ongoing automation, we continue to work,
Analyse key reports forecasting the future of work,
Explore and critically assess major indicators of automation risk—their assumptions, limitations, and applications,
Examine current literature on the impact of artificial intelligence (including generative AI) on work,
Discuss the broader consequences of automation exposure—not only in terms of employment and wages but also in relation to workers’ perceptions and fears, job satisfaction, willingness to reskill or change occupation, and even political preferences.
In the second part of the course, we will:
Develop practical skills in working with empirical data related to the digital transformation of labour markets, using sources such as OECD datasets, Eurostat (including DESI), and the O*NET database,
Compare selected automation risk indicators, analysing their variation across occupations, sectors, and countries,
Apply these indicators to survey data (e.g., on perceptions, concerns, willingness to retrain, or job satisfaction), conducting thematic analyses,
Work with data; therefore, basic skills in Python, R, or Excel are required, and students will be expected to bring their own laptops to several practical sessions.
By the end of the course, students will be able to critically assess forecasts about the future of work and interpret the data and indicators commonly used in analyses of technology’s impact on labour markets.
Estimated ECTS x 25h = 75h Student Workload: 3
(K – contact hours; S – self-study)
Classes (seminars/workshops): 30h (K), 0h (S)
Preparation for classes: 0h (K), 25h (S)
Preparation of final assignment: 0h (K), 19.5h (S)
Presentation of final assignment: 0.5h (K), 0h (S)
Total: 30.5h (K) + 44.5h (S) = 75h

Type of course

general courses

Course coordinators

Satia Rożynek
Katarzyna Śledziewska

Learning outcomes

Knowledge
Students
understand the main processes and mechanisms related to the impact of technology, automation, and artificial intelligence on the labour market,
are familiar with the assumptions, limitations, and applications of key indicators used in research on automation and AI,
have knowledge of current forecasts and reports and is able to interpret them in the context of the future of work.

Skills
Students:
are able to acquire, process, and analyse empirical data from open sources,
can apply data analysis tools,
are able to compare and interpret various indicators of exposure to automation across occupations, sectors, and countries,
can apply selected indicators to survey data and formulate original research conclusions based on them.

Social Skills
Students:
are capable of participating in informed, evidence-based discussions grounded in data and academic literature,
are able to formulate and justify their own point of view.

Assessment criteria

Active participation in classes and final presentation.

Bibliography

Arntz, M., Blesse, S., & Dörrenberg, P. (2022). The End of Work is Near, Isn’t It? Survey Evidence on Automation Angst. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4247981
Autor, D. H. (2022). The Labor Market Impacts of Technological Change: From Unbridled Enthusiasm to Qualified Optimism to Vast Uncertainty. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4122803
Autor, D., Chin, C., Salomons, A., & Seegmiller, B. (2024). New Frontiers: The Origins and Content of New Work, 1940–2018. The Quarterly Journal of Economics, 139(3), 1399–1465. https://doi.org/10.1093/qje/qjae008
Brynjolfsson, E., Mitchell, T., & Rock, D. (2018). What Can Machines Learn, and What Does It Mean for Occupations and the Economy? AEA Papers and Proceedings, 108, 43–47. https://doi.org/10.1257/PANDP.20181019
Felten, E., Raj, M., & Seamans, R. (2021). Occupational, industry, and geographic exposure to artificial intelligence: A novel dataset and its potential uses. Strategic Management Journal, 42(12), 2195–2217. https://doi.org/10.1002/smj.3286
Innocenti, S., & Golin, M. (2022). Human capital investment and perceived automation risks: Evidence from 16 countries. Journal of Economic Behavior and Organization, 195, 27–41. https://doi.org/10.1016/j.jebo.2021.12.027

Additional information

Information on level of this course, year of study and semester when the course unit is delivered, types and amount of class hours - can be found in course structure diagrams of apropriate study programmes. This course is related to the following study programmes:

Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system:

Description of 2400-ZEWW963-OG in USOSweb