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Assessing the Risk of Artificial Intelligence Impact on Decision-Making Automation in Digital Businesses

Mioara Chiriţă & George Chirita

Economics and Applied Informatics2025https://doi.org/10.35219/eai15840409503article
ABDC C
Weight
0.50

What the paper says

Artificial intelligence technology' quick development has drastically changed how digital firms make decisions.While AI-enabled automation has many benefits, such as improved scalability, accuracy, and efficiency, it also brings new hazards that businesses need to be aware of.The possible hazards of AI-driven decision-making automation are evaluated critically in this essay, with a focus on the consequences for operational dependability, business strategy, ethical issues, and regulatory compliance.The study finds important risk concerns such algorithmic bias, accountability problems, cybersecurity dangers, and decreased decision-process openness by examining current practices and literature.The results indicate strong governance structures, open algorithmic audits, and flexible regulatory laws as ways for companies to reduce these risks.In an increasingly automated corporate world, the insights offered in this article assist responsible and well-informed decision-making by promoting a balanced approach to incorporating AI into digital business models.

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https://doi.org/https://doi.org/10.35219/eai15840409503

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@article{mioara2025,
  title        = {{Assessing the Risk of Artificial Intelligence Impact on Decision-Making Automation in Digital Businesses}},
  author       = {Mioara Chiriţă & George Chirita},
  journal      = {Economics and Applied Informatics},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.35219/eai15840409503},
}

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Assessing the Risk of Artificial Intelligence Impact on Decision-Making Automation in Digital Businesses

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.