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PreprintCited by 2Labor market dynamics and wage inequalityDigital Economy and Work TransformationEthics and Social Impacts of AI

The DOI matches its Crossref record; 2 of the 3 references it cites were found in a registry or an index.

The Impact of AI on Job Automation and the Workforce

Open access
Malang, Gauri, Gavhane, Yuvraj, Dahake, Nayan, Dalvi, Dhanraj, Patil, Avadhut·2025·SSRN Electronic Journal
DOI 10.2139/ssrn.5343313
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Overview

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Crossref · 10.2139/ssrn.5343313

Cites

2 of 2 references verified0 citation errors · 0 not checkable

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Citations

Cites

Show all 3 references

Research works printed but not linked: 6

  1. 2017, Jobs Lost, Jobs Gained: Workforce Transitions in a Time of Automation
  2. 2021, The Role of Digital Labour Platforms in Transforming the World of Work
  3. M Arntz, 2016, The Risk of Automation for Jobs in OECD Countries: A Comparative Analysis
  4. Gartner, 2023, AI Trends and Predictions: Impact on Employment and Business
  5. Abhay Birhade, 2025, Machine Learning in Cloud Optimization
  6. J Bughin, 2018, Notes from the AI Frontier: Modeling the Impact of AI on the World Economy

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Metadata

Authors
Malang, G., Gavhane, Y., Dahake, N., Dalvi, D., & Patil, A.
Corresponding author
Malang, Gauri
Venue
SSRN Electronic Journal
Publisher
Elsevier BV
Published
2025
ISSN
1556-5068 (ISSN-L)
DOI
10.2139/ssrn.5343313
OpenAlex
W4412900149
Cited by
2
References
3
Citations by year
2026: 2
Type
posted content
Language
en
Open access
Locations
SSRN Electronic Journal (acceptedVersion)
Topics
  • Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Labor market dynamics and wage inequality
  • Social Sciences › Sociology and Political Science › Digital Economy and Work Transformation
  • Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
Keywords
artificial intelligence, workplace automation, labor force, job displacement, retraining, labor market, income inequality, digital divide, AI ethics, future of work

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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.

Statistics and arithmetic are recomputed, not judged.

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Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.