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An Integrative Theoretical Framework for Responsible Artificial Intelligence

Ahmad Haidar

International Journal of Digital Strategy, Governance, and Business Transformation2023https://doi.org/10.4018/ijdsgbt.334844article
AJG 1
Weight
0.56

What the paper says

The rapid integration of Artificial Intelligence (AI) into various sectors has yielded significant benefits, such as enhanced business efficiency and customer satisfaction, while posing challenges, including privacy concerns, algorithmic bias, and threats to autonomy. In response to these multifaceted issues, this study proposes a novel integrative theoretical framework for Responsible AI (RAI), which addresses four key dimensions: technical, sustainable development, responsible innovation management, and legislation. The responsible innovation management and the legal dimensions form the foundational layers of the framework. The first embeds elements like anticipation and reflexivity into corporate culture, and the latter examines AI-specific laws from the European Union and the United States, providing a comparative perspective on legal frameworks governing AI. The study's findings may be helpful for businesses seeking to responsibly integrate AI, developers who focus on creating responsibly compliant AI, and policymakers looking to foster awareness and develop guidelines for RAI.

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https://doi.org/https://doi.org/10.4018/ijdsgbt.334844

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@article{ahmad2023,
  title        = {{An Integrative Theoretical Framework for Responsible Artificial Intelligence}},
  author       = {Ahmad Haidar},
  journal      = {International Journal of Digital Strategy, Governance, and Business Transformation},
  year         = {2023},
  doi          = {https://doi.org/https://doi.org/10.4018/ijdsgbt.334844},
}

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An Integrative Theoretical Framework for Responsible Artificial Intelligence

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Evidence weight

0.56

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

F · citation impact0.54 × 0.4 = 0.22
M · momentum0.80 × 0.15 = 0.12
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.