Exploring barriers to acceptance of artificial intelligence in social welfare schemes of governments in India - a systematic literature review

被引:0
作者
Verma, Ramendra [1 ]
Kapoor, Shikha [1 ]
机构
[1] Amity Univ, Noida, Uttar Pradesh, India
关键词
Government; Artificial intelligence; Policy; Barriers; Challenges; Public services; AI; GOVERNANCE; FRAMEWORK; SUCCESS;
D O I
10.1007/s13198-024-02498-2
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Artificial intelligence (AI) is a proven technology and has arguably a potential to replace the human brain. The long-term success of AI in the public sector will depend on its early successes in improving the effectiveness of Government functions. Governments in India have been quick to adopt AI in revenue generating departments. However, they have been considerably slow in adopting it in social welfare schemes. There has been limited research in identifying challenges for the same in social welfare schemes in India, especially in identifying the potential beneficiaries and reaching out to them proactively. This research paper is a systematic literature review (SLR) for understanding barriers impeding the adoption of AI in social welfare areas. Through SLR, the authors have identified 82 sub-dimensions under five categories of barriers of Social Environment, Technology, Technology ecosystem, Organizational and individual related barriers. Thereafter authors discuss the possible resolutions to the barriers. The discussions presented would lay foundation of using AI in the Social Welfare Schemes of the Governments and would contribute to achieving improvements in the efficiencies and efficacy in the decisions.
引用
收藏
页码:5139 / 5156
页数:18
相关论文
共 57 条
[1]  
Adawiyah Putri Robiatul, 2021, IOP Conference Series: Earth and Environmental Science, V717, DOI [10.1088/1755-1315/717/1/012046, 10.1088/1755-1315/717/1/012046]
[2]   Artificial Intelligence in Government: Potentials, Challenges, and the Future [J].
Ahn, Michael J. ;
Chen, Yu-Che .
PROCEEDINGS OF THE 21ST ANNUAL INTERNATIONAL CONFERENCE ON DIGITAL GOVERNMENT RESEARCH, DGO 2020, 2020, :243-252
[3]  
Al-Besher A, 2022, Measurement Sensors, V24, P100484, DOI [10.1016/j.measen.2022.100484, 10.1016/j.measen.2022.100484, DOI 10.1016/J.MEASEN.2022.100484]
[4]   Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustworthy Artificial Intelligence [J].
Ali, Sajid ;
Abuhmed, Tamer ;
El-Sappagh, Shaker ;
Muhammad, Khan ;
Alonso-Moral, Jose M. ;
Confalonieri, Roberto ;
Guidotti, Riccardo ;
Del Ser, Javier ;
Diaz-Rodriguez, Natalia ;
Herrera, Francisco .
INFORMATION FUSION, 2023, 99
[5]  
Alqudah M A., 2021, Electronic Research Journal of Social Sciences and Humanities, V3, P65
[6]   An attention-based view of AI assimilation in public sector organizations: The case of Saudi Arabia [J].
Alshahrani, Albandari ;
Dennehy, Denis ;
Mantymaki, Matti .
GOVERNMENT INFORMATION QUARTERLY, 2022, 39 (04)
[7]  
Angin Ria, 2021, IOP Conference Series: Earth and Environmental Science, V717, DOI [10.1088/1755-1315/717/1/012044, 10.1088/1755-1315/717/1/012044]
[8]  
[Anonymous], 2018, PEW RESEARCH CENTER
[9]  
[Anonymous], 2018, Aayog NITI 2018a National Strategy for Artificial Intelligence
[10]  
[Anonymous], 2017, Disrupt and Grow