This is the next post of a series of Trends & Perspectives blog posts. The Track Chairs will reflect on each of this years’ presentation tracks, analyze and discuss some of the trends that you can expect to hear about at OLC Accelerate this year, and also get the perspectives of the Best-in-Track winners.
This blog post features the presenters for the Best In Track selection for the Teaching and Learning Effectiveness Track at the upcoming OLC Accelerate Conference. Their express workshop, Show it Off! Showcase Your Artificial Intelligence, will be held on Wednesday, November 20th from 1:15-2:00pm in Southern Hemisphere 1. We hope you will join us!
Did you know that AI could be used in the classroom to analyze student facial expressions? Are they bored, annoyed, puzzled, disgusted, interested, excited? Could this information be used to evaluate, discipline, or fire faculty? How about assigning participation grades based on this information? Just given the short example above, it seems obvious that there are many ethical implications associated with using AI in higher education. While this particular use of AI (sentiment analysis) is being explored (Lieberman, 2018), it has not yet been fully adopted, in part, due to concerns around how data will be used. However, many other institutional, student support and instructional AI applications have already been integrated into higher education systems and processes including algorithms for planning curricula, machine learning recommendations for courses, majors and career paths, and AI-guided learning pathways (Zeide, 2019).
We think that we can all agree that, ultimately, the goal is to use AI to improve higher education. However, this cannot be done without unpacking and discussing the complex ethical issues around common uses of AI. How do we begin to build a baseline understanding and prepare our stakeholders to implement and integrate AI? Here we will highlight how ethics and AI matter in higher education. In particular, we want to talk about the role higher education needs to play in helping students understand the way ethics enters into the development and use of AI. After all, institutions of higher education have a responsibility to help educate the future developers of AI applications about how ethics impact the AI world. But we can do more. Beyond helping students see how ethics matters in AI, AI can actually help students see how ethics matters to humans. In other words, AI can be used to educate students about ethical decision making and their own ethical blind spots.
We recognized the need to educate and inform our faculty, students and staff about this disruptive AI technology and the underlying ethical implications but weren’t sure where to start. There is great promise in the many new and exciting AI technologies but how do we get beyond the hype? We decided that an AI showcase could be used to introduce our university community to various AI applications while also exploring the relationship between AI, ethics, and moral decision making. As our showcase matures, our demonstrations are moving beyond showing what AI does to illustrating why it does what it does. What are the decisions that go on behind the scenes and how are these decisions made by machines? What are the moral decisions that go into the algorithm of a self-driving car; how are principles of utility and equity incorporated in deciding which group of people to avoid if a crash is inevitable? How are hiring decisions made when machine learning is in charge, are these decisions fair and equitable?
The first step in addressing the complex ethical issues around widespread adoption of AI applications in higher education (and beyond) is to create informed consumers who understand the potential for ethical dilemmas and unconscious biases that are introduced when using AI. An AI showcase can inform participants about AI ethics, help illustrate how machine learning and decision making work, and help participants better understand their own biases and morality of their decision-making processes.
References
Lieberman, M. (2018, February 20). I know how you felt this semester. Inside Higher Ed. Retrieved from https://www.insidehighered.com/digital-learning/article/2018/02/20/sentiment-analysis-allows-instructors-shape-course-content
Zeide, E. (2019, August 26). Artificial intelligence in higher education: Applications, promise and perils, and ethical questions. Educause Review. Retrieved from https://er.educause.edu/articles/2019/8/artificial-intelligence-in-higher-education-applications-promise-and-perils-and-ethical-questions