Future directions of artificial intelligence integration: Managing strategies and opportunities

Article type: Research Article

Authors: Sundar, R.^{a; *} | Choudhury, Ziaul Haque^b | Chiranjivi, M.^c | Parasa, Gayatri^d | Ravuri, Praseeda^e | Sivaram, M.^f | Subramanian, Balambigai^g | Muppavaram, Kireet^h | Lakshmi.Challa, Vijaya Madhaviⁱ

Affiliations: [a] Computer Science and Engineering, Madanapalle Institute of Technology & Science, AP, India | [b] Department of Information Technology, School of Computing and Informatics, Vignan’s Foundation for Science, Technology and Research (Deemed to be University), Guntur, Andhra Pradesh, India | [c] Department of EEE, Hyderabad Institute of Technology and Management, Telangana, India | [d] Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, India | [e] Computer Science Engineer, Oregon State University, Corvallis, Oregon, USA | [f] Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha Nagar, Thandalam, Chennai, Tamil Nadu, India | [g] Department of ECE, Kongu Engineering College, Perundurai, Tamilnadu, India | [h] Department of CSE, GITAM DEEMED to be University, Hyderabad, India | [i] Department of CSE, R.V.R and J.C college of Engineering, Guntur, Andhra Pradesh, India

Correspondence: [*] Corresponding author. R. Sundar, Computer science and Engineering, Madanapalle Institute of Technology & Science. AP, India. E-mail: drsundarr@mits.ac.in.

Abstract: Embracing Artificial Intelligence (AI) is becoming more common in a variety of areas, including healthcare, banking, and transportation, and it is based on substantial data analysis. However, utilizing data for AI raises a number of obstacles. This extensive article examines the challenges connected with using data for AI, including data quality, volume, privacy and security, bias and fairness, interpretability and ethical considerations, and the required technical knowledge. The investigation delves into each obstacle, providing insightful solutions for businesses and organizations to properly handle these complexities. Organizations may effectively harness AI’s capabilities to make educated decisions by understanding and proactively tackling these difficulties, obtaining a competitive edge in the digital era. This review study, which provides a thorough examination of numerous solutions developed over the last decade to address data difficulties for AI, is expected to be a helpful resource for the scientific research community. It not only provides insights into current difficulties, but it also serves as a platform for creating novel ideas to alter our approaches to data strategies for AI.

Keywords: Artificial intelligence, data quality, privacy, security, ethical consideration

DOI: 10.3233/JIFS-238830

Journal: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 7109-7122, 2024