@article{Reis_Santo_Melão_2021, title={Influence of artificial intelligence on public employment and its impact on politics: A systematic literature review}, volume={18}, url={https://bjopm.org.br/bjopm/article/view/1114}, DOI={10.14488/BJOPM.2021.010}, abstractNote={<p><strong>Goal: </strong>Public administration is constantly changing in response to new challenges, including the implementation of new technologies such as robotics and artificial intelligence (AI). This new dynamic has caught the attention of political leaders who are finding ways to restrain or regulate AI in public services, but also of scholars who are raising legitimate concerns about its impacts on public employment. In light of the above, the aim of this research is to analyze the influence of AI on public employment and the ways politics are reacting.</p> <p><strong>Design/Methodology/Approach:</strong> We have performed a systematic literature review to disclose the state-of-the-art and to find new avenues for future research.</p> <p><strong>Results:</strong> The results indicate that public services require four kinds of intelligence – mechanical, analytical, intuitive, and empathetic – albeit, with much less expression than in private services.</p> <p><strong>Limitations of the investigation: </strong>This systematic review provides a snapshot of the influence of AI on public employment. Thus, our research does not cover the whole body of knowledge, but it presents a holistic understanding of the phenomenon.</p> <p><strong>Practical implications:</strong> As private companies are typically more advanced in the implementation of AI technologies, the for-profit sector may provide significant contributions in the way states can leverage public services through the deployment of AI technologies.</p> <p><strong>Originality/Value:</strong> This article highlights the need for states to create the necessary conditions to legislate and regulate key technological advances, which, in our opinion, has been done, but at a very slow pace.</p>}, number={3}, journal={Brazilian Journal of Operations & Production Management}, author={Reis, João and Santo, Paula Espírito and Melão, Nuno}, year={2021}, month={Jan.}, pages={1–22} }