Privacy-First Artificial Intelligence: Toward Fair, Transparent, and Accountable Systems

Direesh Reddy Aunugu*, Venumadhav Goud Vathsavai

Abstract

As artificial intelligence (AI) systems increasingly influence decisions in healthcare, finance, education, and governance, concerns surrounding data privacy, algorithmic fairness, and ethical accountability are becoming critical. This study presents a privacy-first approach to AI development, emphasizing the integration of privacy-preserving techniques such as differential privacy, federated learning, and homomorphic encryption with ethical design principles. Drawing upon a multidisciplinary body of work, this paper investigates the interplay between data protection and ethical imperatives, highlighting the risks of surveillance, bias, and consent erosion in opaque AI systems. Through a critical analysis of real-world applications and policy frameworks, the study identifies key challenges in achieving fairness, transparency, and accountability while safeguarding user privacy. A taxonomy of privacy-aware AI models is proposed, along with an evaluative framework for ethical compliance. This research advocates for embedding privacy as a foundational principle in AI systems, not as a trade-off, but as an enabler of trust, autonomy, and social responsibility.

Keywords

privacy-preserving artificial intelligence; federated learning; differential privacy; algorithmic fairness; ethical AI; transparency; accountability; homomorphic encryption; data governance; decentralized systems.

Cite This Article

Aunugu, D. R., Vathsavai, V. G. (2022). Privacy-First Artificial Intelligence: Toward Fair, Transparent, and Accountable Systems. International Journal of Scientific Advances (IJSCIA), Volume 3| Issue 6: Nov-Dec 2022, Pages 932-935 URL: https://www.ijscia.com/wp-content/uploads/2025/04/Volume3-Issue6-Nov-Dec-No.380-932-935.pdf

Volume 3 | Issue 6: Nov – Dec 2022