Inviting an author to review:
Find an author and click ‘Invite to review selected article’ near their name.
Search for authorsSearch for similar articles
2
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Book Chapter: not found
      The Ethical Frontier of AI and Data Analysis : 

      Toward a More Ethical Future of Artificial Intelligence and Data Science

      edited-book
      IGI Global

      Read this book at

      Buy book Bookmark
          There is no author summary for this book yet. Authors can add summaries to their books on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Examining the ethical aspects of artificial intelligence (AI) and data science (DS) recognizes their impressive progress in innovation while emphasizing the pressing necessity to tackle intricate ethical dilemmas. The chapter provides a detailed framework for navigating the changing environment, beginning with an examination of the increasing ethical challenges. The study highlights transparency, fairness, and responsibility as crucial for cultivating confidence in AI systems. The chapter emphasizes the urgent requirement to address problems such as algorithmic bias and privacy breaches with strong mitigation techniques. Furthermore, it promotes flexible policies that strike a balance between innovation and ethical safeguards. The examination of societal effects, particularly on various socioeconomic groups, economies, and cultures, is conducted thoroughly, with a focus on equity and the protection of individual rights. Finally, to proactively tackle future ethical challenges in technology, it is advisable to employ proactive solutions such as implementing AI ethics by design.

          Related collections

          Most cited references66

          • Record: found
          • Abstract: found
          • Article: not found

          Machine learning: Trends, perspectives, and prospects.

          Machine learning addresses the question of how to build computers that improve automatically through experience. It is one of today's most rapidly growing technical fields, lying at the intersection of computer science and statistics, and at the core of artificial intelligence and data science. Recent progress in machine learning has been driven both by the development of new learning algorithms and theory and by the ongoing explosion in the availability of online data and low-cost computation. The adoption of data-intensive machine-learning methods can be found throughout science, technology and commerce, leading to more evidence-based decision-making across many walks of life, including health care, manufacturing, education, financial modeling, policing, and marketing.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            Health intelligence: how artificial intelligence transforms population and personalized health

            Advances in computational and data sciences for data management, integration, mining, classification, filtering, visualization along with engineering innovations in medical devices have prompted demands for more comprehensive and coherent strategies to address the most fundamental questions in health care and medicine. Theory, methods, and models from artificial intelligence (AI) are changing the health care landscape in clinical and community settings and have already shown promising results in multiple applications in healthcare including, integrated health information systems, patient education, geocoding health data, social media analytics, epidemic and syndromic surveillance, predictive modeling and decision support, mobile health, and medical imaging (e.g. radiology and retinal image analyses). Health intelligence uses tools and methods from artificial intelligence and data science to provide better insights, reduce waste and wait time, and increase speed, service efficiencies, level of accuracy, and productivity in health care and medicine.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found
              Is Open Access

              Machine Learning in Python: Main Developments and Technology Trends in Data Science, Machine Learning, and Artificial Intelligence

              Smarter applications are making better use of the insights gleaned from data, having an impact on every industry and research discipline. At the core of this revolution lies the tools and the methods that are driving it, from processing the massive piles of data generated each day to learning from and taking useful action. Deep neural networks, along with advancements in classical machine learning and scalable general-purpose graphics processing unit (GPU) computing, have become critical components of artificial intelligence, enabling many of these astounding breakthroughs and lowering the barrier to adoption. Python continues to be the most preferred language for scientific computing, data science, and machine learning, boosting both performance and productivity by enabling the use of low-level libraries and clean high-level APIs. This survey offers insight into the field of machine learning with Python, taking a tour through important topics to identify some of the core hardware and software paradigms that have enabled it. We cover widely-used libraries and concepts, collected together for holistic comparison, with the goal of educating the reader and driving the field of Python machine learning forward.
                Bookmark

                Author and book information

                Contributors
                (View ORCID Profile)
                Book Chapter
                April 12 2024
                : 362-388
                10.4018/979-8-3693-2964-1.ch022
                195daa12-e1bd-4c3c-b668-2a2c2023b9e8
                History

                Comments

                Comment on this book