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Strategic Policy Insights in Data Science

Om Strategic Policy Insights in Data Science

"Strategic Policy Insights in Data Science" unravels the pivotal nexus between policy and the transformative world of data science. In this compelling exploration, the book navigates through the complexities of Ethical AI Adoption, delving into fair algorithms, bias detection, and the crucial need for transparency. Privacy takes center stage with a profound examination of Comprehensive Privacy Laws, Consent Transparency Standards, and the nuances of Personal Data Ownership. Open Data Initiatives and Cybersecurity Resilience are dissected, emphasizing collaboration, robust authentication, and innovative cybersecurity measures. Algorithmic Accountability emerges as a guiding principle, addressing auditing frameworks, bias impact assessments, and the imperative of human oversight. The journey concludes with a deep dive into Data Governance Frameworks, advocating for quality standards, cross-sector integration, and real-time monitoring. This book serves as a compass for policymakers, data scientists, and enthusiasts navigating the ethical landscapes of data science, fostering responsible innovation and governance in the digital age.

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  • Språk:
  • Engelska
  • ISBN:
  • 9798224696260
  • Format:
  • Häftad
  • Sidor:
  • 168
  • Utgiven:
  • 7. januari 2024
  • Mått:
  • 140x10x216 mm.
  • Vikt:
  • 220 g.
  Fri leverans
Leveranstid: 2-4 veckor
Förväntad leverans: 17. december 2024

Beskrivning av Strategic Policy Insights in Data Science

"Strategic Policy Insights in Data Science" unravels the pivotal nexus between policy and the transformative world of data science. In this compelling exploration, the book navigates through the complexities of Ethical AI Adoption, delving into fair algorithms, bias detection, and the crucial need for transparency. Privacy takes center stage with a profound examination of Comprehensive Privacy Laws, Consent Transparency Standards, and the nuances of Personal Data Ownership. Open Data Initiatives and Cybersecurity Resilience are dissected, emphasizing collaboration, robust authentication, and innovative cybersecurity measures. Algorithmic Accountability emerges as a guiding principle, addressing auditing frameworks, bias impact assessments, and the imperative of human oversight. The journey concludes with a deep dive into Data Governance Frameworks, advocating for quality standards, cross-sector integration, and real-time monitoring. This book serves as a compass for policymakers, data scientists, and enthusiasts navigating the ethical landscapes of data science, fostering responsible innovation and governance in the digital age.

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