Qualifications

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As artificial intelligence (AI) tools become more widely used in higher  education, we must pay attention to the risks that can emerge. AI projects, whether applied in classroom learning or used for  decision-making regarding admissions, financial aid allocation, or hiring, must include attention to governance and compliance issues, regardless of the project’s scope and scale. Concerns highlighted in this work include transparency, user privacy, data confidentiality, data  integrity, and system availability, however, we note that this is a  non-exhaustive list of risks. In this paper, risk assessment is defined,  and two examples of risk management frameworks, namely the United  States National Institute of Standards and Technology Artificial Intelligence Risk Management Framework and the non-profit humanitarian effort ForHumanity’s Independent Audit of AI, Algorithmic, and  Autonomous Systems are briefly described. We identify characteristics of  AI applications that need to be assessed for vulnerabilities they may present, such as bias and discrimination. This paper aims to facilitate discussion among stakeholders about the risks that may be encountered from using AI in higher education, as well as to suggest ways  developers, decision-makers, and users can mitigate these risks. Much discussion and published literature has focused on risk management  frameworks designed for large organizations or enterprises or frameworks  that do not consider risks specific to AI. We hope that decision-makers carefully consider the risks, perform due diligence when implementing  AI applications, and create a plan for mitigating the risks. This research supports e-learning practice because students and faculty are  embracing AI applications. Leaders and decision-makers in higher education need to be proactive in protecting their varied stakeholders.  The paper asks what risks may be encountered by institutions of higher education when using AI tools and products in the classroom and for various aspects of decision-making and if published frameworks can  mitigate these risks. 

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Current trends in military cyberspace operations point to a growing  schism between legacy IT provisioning and governance components and the  day-to-day “cybersecurity operations.” This gap results in reactionary  activity — diverting resources to address a threat without fully understanding its potential impact. By using both tools in an integrated  manner (i.e., interlocking cyber fire), cyber operators can normalize emergent cyber threats against ongoing cyber operations to weigh risk and apply appropriate resources, funding and prioritization based on mission need. 

Imagine if Bill Gates applied for a government job as a cybersecurity analyst. Or WhatsApp founder Jan Koum. Or Michael Karp, founder of Tumblr.

These three technology giants have changed our lives by understanding software development, coding and technology, and applying them in new ways. Yet if they applied for a cybersecurity position in the U.S. government, they would likely be rejected. Why? Because these three multibillionaires never graduated from college and thus lack a mandatory cybersecurity requirement: a four-year college degree.