How Students in Computing-Related Majors Distinguish Social Implications of Technology

The demand for machine learning and data science has grown exponentially in recent years. Yet, as the influence of these fields reach farther into daily life, the disparate impacts of these algorithms and models on more marginalized populations have also begun to surface rapidly. To address this emerging crisis, it is necessary to equip the next generation of computer scientists with the ethical tools needed to tackle these issues. Thus, an exploratory study was conducted to investigate how students who are currently enrolled in computing-related programs evaluate and understand the ethical and social impact of technology. 43 students in computing majors were presented with 5 scenarios of different technologies that utilizes machine learning to address potentially sensitive areas (e.g. policing, medical diagnosing). The long-format responses to these scenarios were qualitatively analyzed. Additionally, quantitative analysis was conducted after qualitatively coding the long-format responses into four sentiments. Ultimately, we found that participants were able to decipher the social implications of technology. However, many issues of systemic discrimination were missing from participants’ analysis. Alarmingly, our findings also indicated that 50% or more of participants were not exposed to most of the technologies highlighted in the scenarios, which highlights a potential gap in computing curriculum of connecting ethics as well as racial, cultural, and socioeconomic understanding to computer science. Based on these results, we suggest that computing-related curriculum be reevaluated with ethical training in mind.