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Governance in higher education has been described as ambiguous, elusive, and abstract. Both the concept and the practice of governance are recognized as contested, given tensions between different levels of authority and constituency interests: lay or state, academic or institutional, faculty or students. We focus on developments in public and private higher education to illuminate potentially contradictory trends of convergence and divergence in emerging governance arrangements. The chapter draws on a range of disciplinary and theoretical perspectives for interpreting current governance arrangements in the field of higher education and to highlight gaps in our understanding. The first section addresses the changing landscape of higher education and public–private distinctions in particular. The second focuses on governance arrangements in the arenas of public and private higher education and at the levels of system and institutional governance. The third section discusses theories of governance and their application to public and private higher education domains. The conclusion draws the analyses together, noting gaps and pointing to directions for further research.
Digital information and communications technologies (ICT) have been enthusiastically adopted by individuals, businesses, and government, altering the texture of commercial, social, and legal relationships in profound ways. In this decade, with the rapid development of “big data,” machine-learning tools, and the “Internet of Things,” it is clear that algorithms are becoming very important elements of modern society and a significant factor to consider when developing political or business strategies, developing new markets, or trying to solve problems.
If law is to promote justice and welfare, it must respond to changes in society. In much the same way, the tools that government uses to make, implement, and enforce laws also need to adapt in the face of societal changes as well as in light of changes in technology. In this spirit, governments around the world increasingly look to the promise of one of the newest technological innovations made possible by modern computing power: machine-learning algorithms.
In recent years, algorithms have been incorporated into practically every aspect of our lives. They have come to determine whether you will be approved for a mortgage – as well as the interest, how much you will pay for insurance, the likelihood you will commit a crime, the terms of your sentencing, and the number of police patrols in your neighborhood. It is therefore difficult to imagine a more important consideration than the manner in which we are comfortable with algorithms making these decisions. As their presence in our daily lives grows, attention must be paid to their influence on society. Without these conversations we will increasingly rely on this technology, and it will become more difficult to disentangle the legal and ethical pitfalls from systems that have become necessary in our daily lives.
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