While AI ethics interventions often focus on how researchers should navigate consequential choices, they may overlook a prior question: when do researchers recognize they are making a decision at all? This qualitative study examines how academic NLP teams confront “decision moments” -- junctures where latent alternative paths could be considered. We propose a railyard problem analogy: where trolley problems presume a discrete choice between visible options, railyard problems concern whether alternative paths register as possibilities at all. Drawing on decision-tracing interviews across four NLP projects, we demonstrate how technical defaults, institutional structures, and tacit norms (infraethics) combine to organize research as a human-infrastructural process. Many consequential outcomes arise through "passive decisions", where alternatives exist but never become sufficiently visible, viable, or voiced (VVV) to warrant deliberation; "active decisions" only emerge when VVV conditions are met. Our analysis suggests ethics interventions should cultivate the collaborative conditions under which alternatives become recognizable.
ACM CHI Conference on Human Factors in Computing Systems