Ethical Frameworks in AI Decision-Making: Comprehensive Analysis of Management and Human Resource Applications in Contemporary Organizations

Abstract

This literature review examines emerging ethical frameworks for artificial intelligence (AI) decision-making in management and human resource (HR) contexts within contemporary organizations. Through a systematic review of academic literature published between 2021 and 2025, the study synthesizes the theoretical foundations, implementation approaches, and practical challenges associated with the adoption of ethical AI in organizational settings. The analysis focuses on how organizations conceptualize and operationalize ethical principles in AI-driven decision-making. The review shows that most ethical AI frameworks are grounded in core principles such as transparency, fairness, accountability, and respect for human dignity. However, organizations face difficulties in translating these normative principles into operational practices. The findings identify three main barriers to implementation: algorithmic bias, data privacy concerns, and substantial financial costs, often estimated to range from $50,000 to $500,000 for implementation and monitoring. Based on the analysis of 14 academic sources, the study indicates that while organizations often prioritize regulatory compliance when developing ethical AI policies, effective implementation requires broader organizational and cultural transformation. The review integrates theoretical perspectives with examples from industry practices, including emerging metrics used to evaluate ethical AI implementation. The findings suggest that existing ethical AI frameworks must move beyond traditional compliance-oriented approaches as new forms of human–AI interaction reshape organizational decision-making. This is particularly relevant for HR processes such as recruitment algorithms, performance evaluation systems, and workforce management platforms. The study highlights the need for adaptive ethical AI frameworks capable of addressing technological change, evolving regulations, and societal expectations while maintaining organizational efficiency.

Received: 13 July 2025 | Revised: 16 January 2026 | Accepted: 1 April 2026

Conflicts of Interest

The authors declare that they have no conflicts of interest to this work.

Data Availability Statement

Data sharing is not applicable to this article as no new data were created or analyzed in this study.

Author Contribution Statement

Kirill Toropov: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Writing – original draft, Writing – review & editing, Visualization. Irina Manakhova: Conceptualization, Validation, Resources, Writing – review & editing, Supervision, Project administration.