Danae Metaxa | Searcharxiv

arXiv2026

Cost-of-Ethics Crisis: Beliefs, Decisions, and Justifications in the Job Searches of Computer Science Students in Canada and the United States

Workplace norms in computer science have received growing attention due to a series of recent ethical scandals. One response has been a push to improve the ethics education provided to computer science students. Evidence for the effectiveness of ethics education remains mixed; some evidence suggests that norms are changing, others point to persistent gaps between stated values and practice remain. In this paper, we explore whether students, who have received some contemporary CS ethics education, are able to effectively apply ethical reasoning to their own decision-making in what is typically the first significant ethical decision of their careers: the job search. Our study examines the ethical decision making of 129 computer science students and recent graduates during their job searches. We find that most students prioritize factors like compensation, location, and workplace culture over ethical and social issues. Even when expressing ethical concerns, respondents often justify taking actions contradicting their moral views through commonly-shared explanations such as desire to make money or the perceived inability to avoid unethical workplaces. This work sheds light on the possible disconnect between the ethics education CS students receive and the decisions they make during their job search. We offer insights for evolving curricula to better address practical ethical dilemmas, and encouragement to reproduce the study on a larger scale.

cs.CY↗

arXiv2024

The Role of Inclusion, Control, and Ownership in Workplace AI-Mediated Communication

Given large language models' (LLMs) increasing integration into workplace software, it is important to examine how biases in the models may impact workers. For example, stylistic biases in the language suggested by LLMs may cause feelings of alienation and result in increased labor for individuals or groups whose style does not match. We examine how such writer-style bias impacts inclusion, control, and ownership over the work when co-writing with LLMs. In an online experiment, participants wrote hypothetical job promotion requests using either hesitant or self-assured autocomplete suggestions from an LLM and reported their subsequent perceptions. We found that the style of the AI model did not impact perceived inclusion. However, individuals with higher perceived inclusion did perceive greater agency and ownership, an effect more strongly impacting participants of minoritized genders. Feelings of inclusion mitigated a loss of control and agency when accepting more AI suggestions.

cs.HC↗