Table of Contents
As technological advancements accelerate, a new era of the digital revolution emerges, placing a spotlight on the critical concept of Artificial Intelligence (AI) governance. Michael Veale and others delve into the evolving global governance of AI, encompassing ethical guidelines, national legislation, and international standards. The broadening definition of AI includes data mining and machine learning technologies.
The digital revolution has propelled us into a new era of technological advancement, where the concept of governing Artificial Intelligence (AI) becomes paramount.
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Michael Veale and other experts delve into the emerging methods of global AI governance, such as ethical guidelines, national legislation, and international standards. AI, once a vague term encompassing technologies like data mining and machine learning, now requires a broader definition as an applied science to extend governance to the tools and processes used. Three key areas are highlighted for governance: development, usage, and AI infrastructures. Developing governance involves embedding political objectives during design, while usage concerns software deployment with social and economic impacts. Although less developed, attention is turning towards AI infrastructures, with suggested interventions including ethical codes, industrial governance, contracts and licenses, standards, international agreements, and national regulations. The importance of addressing global issues in a cross-border manner is stressed, along with the risk of ethical dumping. The private power held by tech companies in AI governance is emphasised, underlining the need to consider who truly benefits from governance initiatives.
From Data Mining to Machine Learning: Expanding the Definition of Artificial Intelligence
The evolution of Artificial Intelligence (AI) has seen a shift in terminology from specific technologies like data mining and machine learning towards a more comprehensive definition. Previously, AI was often associated with these individual tools, but scholars like Michael Veale propose a broader understanding. They suggest viewing AI as an applied science that encompasses various tools and processes. By expanding the definition, governance efforts can now extend to the entire spectrum of AI development, deployment, and infrastructure. This holistic approach allows for more effective regulation and ethical considerations across all aspects of AI technology and its applications in modern enterprises.
Global Standards and Ethical Challenges: The Complex Landscape of AI Governance
The landscape of AI governance is complex, particularly concerning global standards and ethical challenges. As AI technologies evolve, the need for international standards becomes increasingly pressing to ensure consistency and interoperability across borders. Ethical considerations, such as privacy, bias, and accountability, present significant challenges that must be addressed through robust governance frameworks. The development of ethical guidelines and codes of conduct is crucial to guide the responsible use of AI systems on a global scale. Moreover, the enforcement of these standards requires collaboration between governments, industry stakeholders, and international bodies to uphold ethical practices and prevent potential abuses in the rapidly advancing field of artificial intelligence.
As enterprises navigate the complex landscape of AI governance, the need for global standards and ethical considerations becomes increasingly evident. The power dynamics at play in the governance of AI by tech companies raise questions about who truly benefits from these initiatives. Looking ahead, how can we ensure equitable governance that serves the interests of all stakeholders?