Re-Thinking Data Strategy and Integration for Artificial Intelligence: Concepts, Opportunities, and Challenges
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Abstract
The use of artificial intelligence (AI) is becoming more prevalent across industries such as healthcare, finance, and transportation. Artificial intelligence is based on the analysis of large datasets and requires a continuous supply of high-quality data. However, using data for AI is not without challenges. This paper comprehensively reviews and critically examines the challenges of using data for AI, including data quality, data volume, privacy and security, bias and fairness, interpretability and explainability, ethical concerns, and technical expertise and skills. This paper examines these challenges in detail and offers recommendations on how companies and organizations can address them. By understanding…
Citation impact
584
total citations
- FWCI
- 96.38
- Percentile
- 100%
- References
- 132
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Authors
3Topics & keywords
Topics
Keywords
- Interpretability
- Big data
- Computer science
- Quality (philosophy)
- Artificial intelligence
- Data quality
- Data science
- Knowledge management
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