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@article{188319,
        author = {Ms. Sheetal Gangwar and Prof. Dr. Amar Nath},
        title = {Artificial Intelligence and the Public Interest Rethinking Policy, Power, and Accountability},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {7},
        pages = {1602-1615},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=188319},
        abstract = {This research paper examines the growing integration of Artificial Intelligence (AI) in contemporary society and critically evaluates the challenges emerging from this technological shift. AI, a key branch of computer science, encompasses a wide range of technologies designed to replicate or augment human cognitive functions. Its expanding use has generated strong interest across both the private and public sectors. While private organizations already employ AI to enhance efficiency and competitiveness, the public sector increasingly views AI as a tool capable of improving administrative productivity through intelligent governance, automation, and data-driven policymaking. However, the adoption of AI in governance presents significant challenges. Existing applications reveal concerns related to legal and regulatory ambiguity, ethical dilemmas, cybersecurity risks, lack of transparency and accountability, algorithmic bias, and the digital divide. If not addressed, these issues may escalate into broader systemic problems.
Therefore, it is essential for governments to understand these constraints and formulate effective public policies to mitigate potential risks. By doing so, the public sector can responsibly harness AI’s potential to enhance service delivery, detect fraud, strengthen public-private partnerships, and identify structural inefficiencies. This study aims to highlight both the transformative impact of AI on public administration and the critical challenges that accompany its increasing adoption.},
        keywords = {Artificial Intelligence, Public Policy, Algorithmic Bias, Ethical Considerations.},
        month = {December},
        }