Ethics, Fairness, and Accountability in Algorithmic Systems: From Principles to Practice
Abstract: The pervasive deployment of algorithmic systems in high-stakes domains-such as criminal justice, hiring, and credit lending-has raised urgent concerns about their ethical implications. While these systems promise efficiency and objectivity, they often risk perpetuating and amplifying societal biases, leading to discriminatory outcomes and a deficit of accountability. This paper examines the triad of ethics, fairness, and accountability in algorithmic decision-making. We argue that the current gap between high-level ethical principles and their practical implementation represents a critical challenge for the field. The paper provides a structured analysis of: (1) the sources of bias in the AI lifecycle, from data collection to model deployment; (2) the evolving landscape of formal fairness definitions and their inherent trade-offs; and (3) the technical and governance frameworks necessary for meaningful accountability, including explainability, auditing, and regulation. Through a case study of recidivism prediction instruments, we illustrate the practical difficulties in aligning algorithmic systems with societal values. We conclude that a multidisciplinary approach, integrating computer science, law, and social science, is essential to build systems that are not only intelligent but also just and responsible.
Keywords: Algorithmic Fairness, AI Ethics, Accountability, Bias, Machine Learning, Explainable AI, XAI, Regulation, Recidivism Prediction
How to Cite?: Dr. Ashok Jahagirdar, "Ethics, Fairness, and Accountability in Algorithmic Systems: From Principles to Practice", Volume 14 Issue 11, November 2025, International Journal of Science and Research (IJSR), Pages: 606-608, https://www.ijsr.net/getabstract.php?paperid=SR251107170545, DOI: https://dx.doi.org/10.21275/SR251107170545