In recent years, the availability of massive data sets and improved computing power have driven the advent of cutting-edge machine learning algorithms. However, this trend has triggered growing concerns associated with its ethical issues. In response to such a phenomenon, this study proposes a feasible solution that combines ethics and computer science materials in artificial intelligent classrooms. In addition, the paper presents several arguments and evidence in favor of the necessity and effectiveness of this integrated approach.
There is a growing consensus that artificial intelligence ethics instruction is critical, and must extend beyond computer sciences courses. Ethics and technology have always been tightly interwoven, but as artificial intelligence (AI) marches forward and impacts society in new and novel ways, the stakes--and repercussions--are growing. "There is potential for (AI) to be used in ways that society disapproves of," observes David S. Touretzky, a research professor in the computer science department at Carnegie Mellon University. One idea that's gaining momentum is AI ethics instruction in schools. Groups such as AI4K12 and the MIT Media Lab have begun to study the issue and develop AI learning frameworks for K-12 students.
The 7th Symposium on Educational Advances in Artificial Intelligence (EAAI'17, co-chaired by Sven Koenig and Eric Eaton) launched the EAAI New and Future AI Educator Program to support the training of early-career university faculty, secondary school faculty, and future educators (PhD candidates or postdocs who intend a career in academia). As part of the program, awardees were asked to address one of the following "blue sky" questions: * How could/should Artificial Intelligence (AI) courses incorporate ethics into the curriculum? * How could we teach AI topics at an early undergraduate or a secondary school level? * AI has the potential for broad impact to numerous disciplines. How could we make AI education more interdisciplinary, specifically to benefit non-engineering fields? This paper is a collection of their responses, intended to help motivate discussion around these issues in AI education.