Cited by 118Digital Economy and Work TransformationEthics and Social Impacts of AILegal and Labor Studies

The DOI matches its Crossref record; 161 of the 172 references it cites were found in a registry or an index; 3 of the 137 sources its claims cite were checked for support.

Artificial intelligence and work: a critical review of recent research from the social sciences

Open access

Abstract

This review seeks to present a comprehensive picture of recent discussions in the social sciences of the anticipated impact of AI on the world of work. Issues covered include: technological unemployment, algorithmic management, platform work and the politics of AI work. The review identifies the major disciplinary and methodological perspectives on AI’s impact on work, and the obstacles they face in making predictions. Two parameters influencing the development and deployment of AI in the economy are highlighted: the capitalist imperative and nationalistic pressures.

Overview

AI snapshot

Written from the paper’s full text.

TL;DR

This critical review synthesises recent social-science research on AI's impact on work across four areas: technological unemployment, algorithmic management, platform work, and the politics of AI work. It argues that AI's effects are not technologically determined but shaped by two background imperatives—a capitalist profit imperative and nationalistic/geopolitical pressures.

Key findings

  • Predictions of technological unemployment vary enormously by method: Frey and Osborne estimate 47% of US jobs at risk within two decades, Bowles 54% in the EU and USA, while Arntz et al. (using an alternative task-linking method) find around 9% across the OECD (12% in Austria, 6% in South Korea), and Nedelkoska and Quintini estimate 14% of jobs highly automatable and 32% at 50–70% risk.
  • Optimistic accounts report productivity and job gains: Accenture estimates AI could raise labour productivity by up to 40% by 2035; PwC estimates global GDP up 14% by 2030 (up to $15.7 trillion); the World Economic Forum predicts a net increase of 58 million jobs (133 million new roles, 75 million displaced); McKinsey values AI in banking at $1 trillion annually (15.4% of sales).
  • The review identifies methodological weaknesses in forecasting: models rely on existing job/task descriptions, treat "skill" and "routine work" as unproblematic, and cannot anticipate new tasks and jobs; Clifton et al. are cited that technology's employment impact "is not deterministic."
  • Marxist and historical critiques argue capitalist property relations and profit criteria, weakened labour power, cheap labour, shareholder-value priorities, and sectoral overcapacity all disincentivise job-replacing automation, so absence of mass unemployment coincides instead with precariousness and exploitation.

Methods

  • Design: a critical, non-disciplinary literature review, one output of an Australian Research Council–funded project (DP190103116) on the centrality of work; "critical" is defined via the "Macbeth question" about the social-political context of knowledge claims.
  • Scope: work is restricted to formal economic activity (production of goods and services for profit or wage); AI is defined as computational methods using data to replicate activities in labour processes.
  • Analysis: thematic synthesis across four areas—technological unemployment (Sect. 3), algorithmic management (Sect. 4), platform work (Sect. 5), and the politics of AI work (Sect. 6)—organised around the capitalist and nationalistic imperatives.

Limitations

  • Stated by the authors: the lack of a specific disciplinary perspective and the broad thematic scope carry drawbacks, including the risk of missing important details and important references in each disciplinary field; they also note their restricted definition of work excludes social reproduction activities.
  • Evident from the text: no systematic search or inclusion protocol is described, and the review relies on secondary literature and cited estimates rather than primary data collection; the authors themselves note that case-study methods inform only a particular job at a particular time and place, and that generalising across national contexts is difficult.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.1007/s00146-022-01496-x

Cites

161 of 161 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Verification

Claims the paper makes

88 Not checked yet · 53 Own interpretation · 8 No basis given · 1 Supported so far · 1 Couldn’t confirm · accuracy not yet measured

Verify all 134 references — free account

Show all 58 claims

  • Probably not a substantial claim. 61
  • It may state two separate claims as one. 50
93 of 93
  • The authors state this as an assumption.

    Scientific inquiries are inextricably tied to the historical context in which they occur.

    “The core assumption this question captures is that scientific inquiries are inextricably tied to the historical context in which they occur.”
  • The paper relies on this without support.

    Knowledge claims reflect social and political fault lines, whether researchers are aware of it or not.

    “Whether they are aware of it or not, knowledge claims reflect social and political fault lines.”
2 of 2

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

Each contradiction between two passages listed here was confirmed by an independent check.

2 of 2

Citations

Cites

Show all 172 references

Research works printed but not linked: 40

  1. Acemoglu D (Ed.) (2021) Redesigning AI. MIT Press
  2. Amorim H, Moda F (2020) Work by app: Algorithmic management and working conditions of Uber drivers in Brazil. Work Organ Labour Global 14(1):101–118
  3. Applebaum HA (1992) The Concept of Work: Ancient, medieval, and modern. SUNY Press
  4. Arntz M, Gregory T, Zierahn U (2019) Digitization and the future of work: Macroeconomic consequences. Handbook Labor Hum Resour Popul Econ 1:1–29
  5. Autor DH, Handel MJ (2013) Putting tasks to the test: Human capital, job tasks, and wages. J Law Econ 31(S1):S59–S96
  6. Bales RA, Stone K (2020) The invisible web at work: artificial intelligence and electronic surveillance in the workplace. Berkeley J Employm Labor Law 41:1
  7. Bastani A (2018) Fully Automated Luxury Communism. Verso
  8. Berger T, Chen C, Frey C B (2017) Cities, Industrialization and Job Creation: Evidence from Emerging Countries, pp 1–25. Mimeo, Oxford Martin School
  9. Bhave DP, Teo LH, Dalal RS (2020) Privacy at work: A review and a research agenda for a contested terrain. J Manag 46(1):127–164
  10. Brito DL (2020) Automation Does Not Kill Jobs. Rice University, It Increases Inequality
  11. Brynolfsson E, McAfee A (2011) Race against the Machine. Mass: Digital Frontier Press, Lexington
  12. Clifton J, Glasmeier A, Gray M (2020) When machines think for us: the consequences for work and place. Oxford University Press UK 13:3–23
  13. Datta A, Tschantz MC, Datta A (2015) Automated experiments on ad privacy settings. Proc Priv Enh Technol 2015(1):92–112
  14. Daugherty PR, Wilson HJ (2018) Human+ machine: Reimagining work in the age of AI. Harvard Business Press
  15. David PA (1990) The dynamo and the computer: an historical perspective on the modern productivity paradox. Am Econ Rev 80(2):355–361
  16. De Stefano V (2019) Negotiating the Algorithm: Automation, Artificial Intelligence, and Labor Protection. Comp Lab L & Pol'y J 41:15
  17. Eubanks B (2022) Artificial Intelligence for HR: Use AI to Support and Develop a Successful Workforce. Kogan Page, United Kingdom
  18. Ford M (2015) Rise of the Robots. Cornell University Press, New York
  19. Ford (2021) Rule of the robots: how artificial intelligence will transform everything. UK: John Murray Press
  20. Göranzon B, Josefson I (2012). Knowledge, skill and artificial intelligence, Springer Science & Business Media
  21. Halal W, Kolber J, Davies O, Global T (2017) Forecasts of AI and future jobs in 2030: Muddling through likely, with two alternative scenarios. J Futur Stud 21(2):83–96
  22. Kalleberg AL, Vallas SP (2018) Probing precarious work: Theory, research, and politics. Res Sociol Work 31(1):1–30
  23. Lewis P, Bell K (2019) Understanding the UK’s productivity problems: new technological solutions or a case for the renewal of old institutions?. Empl Relations 41(2):296–312. https://doi.org/10.1108/ER10-2018-0273 · DOI
  24. Luksha P et al (2015) Atlas of Emerging Jobs. Skolkovo, Moscow
  25. Manyika J, Chui M, Miremadi M, Bughin J, George K, Willmott P, Dewhurst M (2017) A future that works: AI, automation, employment, and productivity. McKinsey Global Institute Research. Tech Rep 60:1–135
  26. Moghaddam Y, Yurko H, Demirkan H, Tymann N, Rayes A (2020) The future of work: how artificial intelligence can augment human capabilities, business expert press
  27. Möhlmann M, Henfridsson O (2019) What people hate about being managed by algorithms, according to a study of Uber drivers. Harvard Business Review, 30
  28. Muller Z (2019) Algorithmic harms to workers in the platform economy: The case of Uber. Columbia J Law Soc Prob 53:167
  29. Munoz JM, Naqvi A (2018) Business strategy in the artificial intelligence economy. Business Expert Press
  30. Neufeind M, O'Reilly J, Ranft F (2018) Work in the Digital Age: Challenges of the Fourth Industrial Revolution, Rowman & Littlefield International
  31. Paschkewitz J, Patt D (2020) Can AI make your job more interesting? Issues Sci Technol 37(1):74–78
  32. Purcell C, Brook P (2020) At least I’m my own boss! Explaining consent, coercion and resistance in platform work. Employment and Society, Work
  33. Scarpetta S (2018) The future of work: Advancing labor market resilience. J Int Aff 72(1):51–57
  34. Schmidt FA (2017) Digital labour markets in the platform economy. Mapp Polit Challeng Crowd Work Gig Work 7:2016
  35. Solow R (1987) "We'd better watch out", New York Times Book Review, July 12, 1987
  36. Srnicek N (2017) Platform Capitam. Polity Press, Germany
  37. Srnicek N, Williams A (2016) Inventing the Future (revised and Updated Edition): Postcapitalism and a World Without Work. Verso Books, Spain
  38. Sundararajan A (2017) The sharing economy: The end of employment and the rise of crowd-based capitalism. MIT press
  39. Susskind D (2020) A world without work: Technology, automation and how we should respond, Penguin UK
  40. Wilson HJ, Daugherty P, Bianzino N (2017) The jobs that artificial intelligence will create. MIT Sloan Manag Rev 58(4):14

Cited by

Sorted by citations

See all 118

Metadata

Authors
Deranty, J.-P., & Corbin, T.
Affiliations
Macquarie University (AU)
Corresponding author
  • Deranty, Jean-Philippe
  • Corbin, Thomas
Venue
AI & SOCIETY
Published
2022-06-28
Volume
39
Issue
2
Pages
675-691
ISSN
  • 0951-5666 (ISSN-L)
  • 1435-5655
OpenAlex
W4283645384
Cited by
118
References
172
Citations by year
2026: 33 · 2025: 39 · 2024: 31 · 2023: 14 · 2022: 1
FWCI
30.63
Type
journal article
Language
en
Open access
Locations
AI & Society (publishedVersion, cc-by)
Funders
  • Australian Research Council (DP190103116)
  • Macquarie University
Topics
  • Social Sciences › Sociology and Political Science › Digital Economy and Work Transformation
  • Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
  • Social Sciences › Business, Management and Accounting › Industrial relations › Legal and Labor Studies
Keywords
artificial intelligence, future of work, technological unemployment, algorithmic management, platform work, politics of work, capitalism, nationalism

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

A language model drew the claims from the paper on 2026-10-07, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).

A second model labelled each claim on 2026-10-07 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).

Cited sources are checked with a language model’s help (gpt-6-sol).

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review