CORTEXA
← Browse
crossrefAlgorithms2026-06-19Cited by 0

A Review of Business Analytics, Machine Learning, and Generative Artificial Intelligence Research 2020–2025: Toward Responsible Artificial Intelligence

Arnold Kamis

This review examines the evolving intersections of data analytics, machine learning, and artificial intelligence—terms that have been frequently conflated since 2016 during a period of increased hype and investment. Following recent reviews across areas such as open innovation, supply chain deep learning, strategic alliances, natural language processing, and big data streaming, we focus on the emerging field of Responsible Artificial Intelligence (AI). We apply descriptive analysis to identify trends, patterns, and gaps in the research through a review of academic literature from 2020 to 2025. Analysis reveals five distinct clusters of Responsible AI papers using five dimensions: fairness, cross-validity, transparency, accuracy–interpretability tradeoff, and drift detection. This review discusses patterns across the artificial intelligence literature and identifies future research opportunities with an emphasis on Responsible AI.

View free PDFSource page

Related papers

crossrefAlgorithms2024-11-03Cited by 8

Enhancing Arabic Sentiment Analysis of Consumer Reviews: Machine Learning and Deep Learning Methods Based on NLP

Hani Almaqtari, Feng Zeng, Ammar Mohammed

Sentiment analysis utilizes Natural Language Processing (NLP) techniques to extract opinions from text, which is critical for businesses looking to refine strategies and better understand customer feedback. Understanding people’s sentiments about products through emotional tone a…

View free PDFSource page
crossrefAlgorithms2025-07-03Cited by 4

Integrating Machine Learning Techniques and the Theory of Planned Behavior to Assess the Drivers of and Barriers to the Use of Generative Artificial Intelligence: Evidence in Spain

Antonio Pérez-Portabella, Jorge de Andrés-Sánchez, Mario Arias-Oliva, Mar Souto-Romero

Generative artificial intelligence (GAI) is emerging as a disruptive force, both economically and socially, with its use spanning from the provision of goods and services to everyday activities such as healthcare and household management. This study analyzes the enabling and inhi…

View free PDFSource page
crossrefAlgorithms2026-05-02

A Survey of Machine Learning and Deep Learning for Financial Fraud Detection: Architectures, Data Modalities, and Real-World Deployment Challenges

Spiros Thivaios, Georgios Kostopoulos, Antonia Stefani, Sotiris Kotsiantis

Financial fraud has become a critical challenge for modern financial systems due to the rapid growth of digital transactions, online banking services, and electronic payment platforms. Traditional rule-based fraud detection systems are increasingly inadequate in addressing the ev…

View free PDFSource page
crossrefAlgorithms2022-04-27Cited by 15

An Emotion and Attention Recognition System to Classify the Level of Engagement to a Video Conversation by Participants in Real Time Using Machine Learning Models and Utilizing a Neural Accelerator Chip

Janith Kodithuwakku, Dilki Dandeniya Arachchi, Jay Rajasekera

It is not an easy task for organizers to observe the engagement level of a video meeting audience. This research was conducted to build an intelligent system to enhance the experience of video conversations such as virtual meetings and online classrooms using convolutional neural…

View free PDFSource page