Exploring the negative impacts of artificial intelligence in government: The dark side of intelligent algorithms and cognitive machines
By: Cruz, D.V. Contreras, R. G. and Garcia, J.R.G
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Material type: 
BookPublisher: International Review of Administrative Sciences Description: 90(2), Jun, 2024: p.353-368.Subject(s): AI, negative impacts, socio technical categories
In:
International Review of Administrative SciencesSummary: This research proposes a framework for the negative impacts of artificial intelligence (AI) in government by classifying 14 topics of its dark side into five socio technical categories. The framework is based on a systematic literature review and highlights that the dark side is predominantly driven by political, legal, and institutional aspects, but it is also influenced by data and technology. Lack of understanding of AI outcomes, biases, and errors, as well as manipulation of intelligent algorithms and cognitive machines are contributing factors. The public sector should create knowledge about AI from an ethical, inclusive, and strategic perspective, involving experts from different areas.- Reproduced
https://journals.sagepub.com/doi/abs/10.1177/00208523231187051
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Articles
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Indian Institute of Public Administration | 90(2), Jun, 2024: p.353-368 | Available | AR132438 |
This research proposes a framework for the negative impacts of artificial intelligence (AI) in government by classifying 14 topics of its dark side into five socio technical categories. The framework is based on a systematic literature review and highlights that the dark side is predominantly driven by political, legal, and institutional aspects, but it is also influenced by data and technology. Lack of understanding of AI outcomes, biases, and errors, as well as manipulation of intelligent algorithms and cognitive machines are contributing factors. The public sector should create knowledge about AI from an ethical, inclusive, and strategic perspective, involving experts from different areas.- Reproduced
https://journals.sagepub.com/doi/abs/10.1177/00208523231187051
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