An empirical examination of the challenges and methods associated with the management of artificial intelligence systems
Artificial intelligence, challenges, AI systems, management, automation
This research focuses on assessing the intrigues of managing artificial intelligence systems. The expansive adoption of artificial intelligence systems, in various sectors, has ushered in a new era of transformative technological abilities. Nevertheless, the rapid integration of AI has come hand-in-hand with numerous obstacles in efficiently managing these intricate systems. This empirical investigation aims to scrutinize and analyze the multifarious issues faced while managing AI systems and seeks to bring into focus the methodologies and strategies employed to tackle these roadblocks. In today's world, AI systems play pivotal roles across industries like health care and finance by driving innovation and efficacy [1]. Nonetheless, handling these systems involves grappling with concerns related to data privacy, ethical factors, compliance with regulations, and the dynamic traits of AI technologies. This research employs real-world data and case studies to explore the intricacies tied to these challenges along with their consequences. Moreover, this survey dives deeper into the assortment of methods and tactics formulated to navigate the landscape of AI management successfully [2]. These encompass frameworks for governance, evaluation protocols for risks, mechanisms for continuous monitoring, and collaboration models between humans and AI systems. Coupling insights from actual experiences along with best practices this study provides valuable contributions to this evolving field.
"An empirical examination of the challenges and methods associated with the management of artificial intelligence systems", IJNRD - INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (www.IJNRD.org), ISSN:2456-4184, Vol.8, Issue 3, page no.e750-e754, March-2023, Available :https://ijnrd.org/papers/IJNRD2303498.pdf
Volume 8
Issue 3,
March-2023
Pages : e750-e754
Paper Reg. ID: IJNRD_208160
Published Paper Id: IJNRD2303498
Downloads: 000118848
Research Area: Engineering
Country: -, -, India
ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJNRD (IJ Publication) Janvi Wave
