6 Chapter 6: The Razor’s Edge: How to Balance Risk in Artificial Intelligence, M
6 Chapter 6: The Razor’s Edge: How to Balance Risk in Artificial Intelligence, Machine Learning, and Big Data
Joel Templeman
The path to Salvation is as narrow and as difficult to walk as a razor’s edge.
― W. Somerset Maugham
This chapter is guided by the question, how can an educational system take advantage of rapid technological advances in a safe and socially responsible manner while still achieving its mandate of fostering and supporting learner success? Artificial intelligence (AI), machine learning (ML) , and big data are examples of highly risky technologies that also hold vast potential for innovation (Floridi et al., 2018). In examining technological advances from an ethical perspective, one of the aims is to avoid harm and minimize risk. This is referred to as a consequentialist perspective (Farrow, 2016). The complexity of finding and maintaining a proper balance in advancing technological innovation and avoiding harm and minimizing risk cannot be understated. This quest for an educational “sweet spot” is mired by a lack of understanding, inconsistent leadership, and simple human greed.
Educators are inundated with information and change on a daily basis, and this accelerated greatly in 2020 as a result of the COVID-19 pandemic and related health restrictions. Technologies such as AI, ML, and big data may be outside an educator’s expertise or interest; however, they have the potential to impact teaching practice and students’ lives in significant ways. Teachers are often required to rely on experts and popular media to guide their learning about emerging technologies or to inform decision-making. At times, even when teachers have an informed opinion, they lack the organizational authority to make certain decisions regarding changes needed in the learning environment. One such area in which teachers may feel limited in their choices is the rapidly evolving infiltration of educational technology (EdTech) in the classroom, and specifically the automation within EdTech that exploits the capabilities of technologies (e.g., AI, ML, and big data) that augment functions historically in the domain of the teacher. This chapter is about how humans’ carbon intelligence (biological) will begin to coexist with computers’ silicon intelligence (machine; Shah, 2016) in learning, without the formal educational system acting as a gatekeeper of personal privacy, from a consequentialist perspective (Farrow, 2016).
Discussion about the appropriate use of advanced technologies is not limited to the classroom, as this integration impacts all aspects of living in a digital age. For everyone with access to technology, their experience will be shaped by systems and their interactions with those systems. In an educational context, learners are subjected to information technology (IT) platforms at all levels, and these interactions require the system to “know” things about the users. For these systems to be accepted and to benefit the users and the education syst