The Role of AI and Machine Learning in Modern Decision-Making: Opportunities and Ethical Challenges

Aditya Singh Yadav; Tulika Saxena; Navin Chandra Bharti; Akhilesh Kr. Dixit; Nivedita Verma

doi:10.69968/ijisem.2024v3si2284-286

Authors

Aditya Singh Yadav Research Scholar, Business Administration Dept. in MJP Rohilkhand University, Bareilly
Tulika Saxena Dean & Head, Business Administration Dept. in MJP Rohilkhand University, Bareilly
Navin Chandra Bharti Research Scholar, Commerce Dept. in Bareilly College (affiliated to MJPRU), Bareilly
Akhilesh Kr. Dixit Head & Associate Prof. of BBA Dept. in Siddharth University, Siddharth Nagar
Nivedita Verma Assistant Prof. of Management Dept. in School of Management Sciences, Varanasi

DOI:

https://doi.org/10.69968/ijisem.2024v3si2284-286

Keywords:

AI, ML, Decision making, Data privacy

Abstract

The integration of Artificial Intelligence (AI) and Machine Learning (ML) in decision-making processes has significantly transformed various industries, including finance, healthcare, and logistics. These technologies enable organizations to process large volumes of data efficiently, uncovering insights that traditional methods often overlook. However, along with the benefits come notable challenges, particularly regarding fairness, transparency, and ethical considerations. This paper examines the current applications of AI and ML in decision-making, explores key use cases, addresses ethical concerns, and provides recommendations for future research. The findings indicate that while AI and ML enhance decision-making speed and accuracy, careful attention to bias, accountability, and data privacy is essential to ensure responsible use.

References

[1] Gunning,D.,&Aha,D.(2018).DARPA'sExplainableArtificialIntelligence(XAI)Program.AIMagazine,39(2), 44-58. https://doi.org/10.1609/aimag.v40i2.2850 This paper outlines the objectives and advancements of the XAI program, aiming to make AI systems more transparent and interpretable.

[2] Jones,M.,&Clark,P.(2020).AIinMedicalDiagnostics:AReviewofApplicationsandChallenges.Journalof Health Informatics, 12(3), 221-234. A comprehensive review of AI applications in medical diagnostics and the various challenges associated with implementing these technologies.

[3] Russell,S.,&Norvig,P.(2020).ArtificialIntelligence:AModernApproach(4thed.).Pearson. A foundational textbook that covers the theory and practical applications of AI, including decision-making models and machine learning techniques.

[4] Goodfellow,I.,Bengio,Y.,&Courville,A.(2016).DeepLearning.MITPress. This book provides an in-depth explanation of deep learning methods, which are integral to many modern AI decision-making systems.

[5] Witten,I.H.,Frank,E.,Hall,M.A.,&Pal,C.J.(2016).DataMining:PracticalMachineLearningToolsand Techniques (4th ed.). Morgan Kaufmann. A practical guide to machine learning techniques used in data mining and decision support systems,focusing on real-world applications.