Volume 17, Number 5

Responsible AI Analytics for Real-World Impact: Navigating Ethics, Privacy and Trust

  Authors

Ghousia Sultana ¹, Siraj Farheen Ansari ¹, Mohammed Imran Ahmed ², Abdul Faiyaz Shaik ¹, Moin Uddin Khaja ³ and Bibhu Dash ⁴, ¹ Trine University, USA, ² Campbellsville University, USA, ³ Lindsey Wilson College, USA, ⁴ University of the Cumberlands, USA

  Abstract

Artificial intelligence (AI) is increasingly used in data analytics to generate insights from vast datasets, but its adoption raises urgent concerns around ethics, privacy, and trust. This paper explores the intersections of these challenges, highlighting risks such as algorithmic bias, opaque decision-making, inconsistent privacy safeguards, and declining public confidence. Ethical considerations are examined through issues of fairness, accountability, and explainability, while privacy concerns focus on data collection, storage, and regulatory gaps. Trust is addressed through system transparency, resilience, and user perceptions. Drawing on literature, regulatory reports, case studies in healthcare, finance, and social media, and survey findings, the analysis reveals persistent gaps between innovation and responsible governance. To address these issues, the paper recommends embedding ethical design principles, adopting privacy-preserving methods like federated learning and differential privacy, and advancing explainable AI. Building trustworthy AI analytics requires cross-disciplinary collaboration, global ethical frameworks, and participatory, transparent approaches.

  Keywords

AI Ethics, Data Privacy, Trust in AI, AI Analytics, Algorithmic Bias, Explainable AI (XAI), Federated Learning, Responsible AI, Ethical Frameworks, Privacy-preserving AI