  Though ML practitioners increasingly employ various Responsible ML (RML)
strategies, their methodological approach in practice is still unclear. In
particular, the constraints, assumptions, and choices of practitioners with
technical duties -- such as developers, engineers, and data scientists -- are
often implicit, subtle, and under-scrutinized in HCI and related fields. We
interviewed 22 technically oriented ML practitioners across seven domains to
understand the characteristics of their methodological approaches to RML
through the lens of ideal and non-ideal theorizing of fairness. We find that
practitioners' methodological approaches fall along a spectrum of idealization.
While they structured their approaches through ideal theorizing, such as by
abstracting RML workflow from the inquiry of applicability of ML, they did not
pay deliberate attention and systematically documented their non-ideal
approaches, such as diagnosing imperfect conditions. We end our paper with a
discussion of a new methodological approach, inspired by elements of non-ideal
theory, to structure technical practitioners' RML process and facilitate
collaboration with other stakeholders.
