Version 1
: Received: 21 May 2023 / Approved: 23 May 2023 / Online: 23 May 2023 (04:36:17 CEST)
Version 2
: Received: 30 May 2023 / Approved: 1 June 2023 / Online: 1 June 2023 (03:37:17 CEST)
Aldoseri, A.; Al-Khalifa, K.N.; Hamouda, A.M. Re-Thinking Data Strategy and Integration for Artificial Intelligence: Concepts, Opportunities, and Challenges. Appl. Sci.2023, 13, 7082.
Aldoseri, A.; Al-Khalifa, K.N.; Hamouda, A.M. Re-Thinking Data Strategy and Integration for Artificial Intelligence: Concepts, Opportunities, and Challenges. Appl. Sci. 2023, 13, 7082.
Aldoseri, A.; Al-Khalifa, K.N.; Hamouda, A.M. Re-Thinking Data Strategy and Integration for Artificial Intelligence: Concepts, Opportunities, and Challenges. Appl. Sci.2023, 13, 7082.
Aldoseri, A.; Al-Khalifa, K.N.; Hamouda, A.M. Re-Thinking Data Strategy and Integration for Artificial Intelligence: Concepts, Opportunities, and Challenges. Appl. Sci. 2023, 13, 7082.
Abstract
The use of artificial intelligence (AI) is becoming more prevalent across industries as diverse as healthcare, finance, and transportation. Artificial intelligence is based on the analysis of large data sets and requires a continuous supply of high-quality data. However, using data for AI is not without its challenges. This paper comprehensively reviews and critically examine the challenges of using data for AI, including data quality, data volume, privacy and security, bias and fairness, interpretability and explainability, ethical concern, and technical expertise and skills. This paper examines e these challenges in details and offers advices on how companies can address them. By understanding and addressing these challenges, organizations can harness the power of AI to make smarter decisions and gain a competitive advantage in the digital age.
Keywords
Data Strategies and learning approaches; Challenges and Opportunities; Artificial Intelligence
Subject
Business, Economics and Management, Other
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
