Algorithmic Bias

This research investigates the fundamental challenges inherent in traditional performance appraisal systems, such as human cognitive biases and a lack of scalability, and analyzes the application of artificial intelligence (AI) as a solution to optimize these processes. The primary objective is to present a practical framework for the responsible implementation of AI, aimed at establishing more objective, equitable, and effective appraisal systems. This study employs an integrative review methodology (searching the Scopus database from 2019 onwards) combined with qualitative thematic analysis. Based on specific inclusion criteria (i.e., a focus on socio-technical challenges), 9 specialized articles were selected for final analysis. The analysis of this corpus achieved thematic saturation. The thematic analysis led to the identification of four primary themes: (1) limitations of traditional systems; (2) key AI-driven opportunities, such as enhanced objectivity and continuous feedback; (3) critical risks (e.g., Algorithmic Bias and the Black Box Problem); and (4) implementation imperatives (e.g., the necessity of Human-in-the-Loop (HITL) Oversight and transparency). Ultimately, the study concludes that success is contingent upon human-machine synergy and proposes a three-stage Integrated Socio-Technical Systems (ISTS) Framework. This framework emphasizes Explainable AI (XAI) (XAI) and the preservation of human judgment. This study is conceptual in nature. The proposed framework offers a pathway for the sustainable and human-centric utilization of this technology, which necessitates empirical validation in future research.