Implementing Adaptive AI Governance Frameworks for Real-Time Data Stream Management and Quality Assurance

@article{1712736,
      author = {Adedayo Hakeem Kukoyi},
      title = {Implementing Adaptive AI Governance Frameworks for Real-Time Data Stream Management and Quality Assurance},
      journal = {Iconic Research And Engineering Journals},
      year = {2023},
      volume = {6},
      number = {10},
      pages = {1156-1161},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1712736.pdf},
      abstract = {The horizon of artificial intelligence (AI) technologies is expanding, as is the need for effective governance structures that align with the real-time flow of data. This study provides a comprehensive examination of adaptive AI governance structures for managing and ensuring the quality of real-time data streams. A quantitative strategy was employed by circulating structured questionnaires to data scientists, IT managers, and AI policy makers in technology companies. The data were analyzed using basic descriptive statistics, including frequencies and percentages. The study identified adaptability, transparency, and accountability as fundamental cornerstones of dynamic data environments. This study advocates incorporating adaptive feedback systems into AI governance to promote compliance, ethical alignment, and data fidelity. It is recommended that organizations and policymakers design and operationalize flexible, context-sensitive governance frameworks that preserve data quality and promote trust in automated decisions.},
      keywords = {AI Governance, Data Quality, Real-Time Data Streams, Adaptive Framework, Accountability, Transparency.},
      month = {April},
      doi = {https://doi.org/10.64388/IREV6I10-1712736}
  }