Workshop on Applied Machine Learning Management

Goldenberg, Dmitri; Sokolova, Elena; Lador, Shir Meir; Mandelbaum, Amit; Vasilinetc, Irina; Jain, Ankit

doi:10.1145/3534678.3542914

Cited by 3 publications

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…concerning AI-driven data management have received notable attention in healthcare [19]. However, other sectors, such as business and public sector management, remain relatively unexplored regarding ethical considerations.…”

Section: Ethical Exploration: Ethical Challengesmentioning

confidence: 99%

“…5. Resource Management Diversification: While data-driven approaches have been proposed for crop diversity management [21], resource management in soil and water resources is conspicuously underrepresented [22]. Addressing this gap requires expanding the application of data-driven policy-making to diverse resource management contexts.…”

Section: Ethical Exploration: Ethical Challengesmentioning

confidence: 99%

See 1 more Smart Citation

AI’s Evolutionary Role in Data Management and its Profound Influence on Business Outcomes

Pooja,

Ali,

Mookerjee

et al. 2023

IJCERT

View full text Add to dashboard Cite

This systematic review paper explores the dynamic landscape of AI-driven data management implementations across diverse industries. It investigates the transformative impact of AI technologies on sectors including healthcare, manufacturing, transportation, construction, the public sector, human resource management, agriculture, and banking and finance. Through a comprehensive analysis of outcomes, research gaps, and critical assessments, we unveil the potential of AI-driven data management to reshape business processes, optimize decision-making, and foster innovation. While our findings underscore the promise of these technologies, they also underscore the pressing need for further research to bridge existing gaps and unlock their full potential. This paper is a valuable resource for decision-makers, researchers, and practitioners seeking to harness the power of AI-driven data management in an ever-evolving technological landscape.

show abstract

Section: Ethical Exploration: Ethical Challengesmentioning

confidence: 99%

Section: Ethical Exploration: Ethical Challengesmentioning

confidence: 99%