  Ethics in the emerging world of data science are often discussed through
cautionary tales about the dire consequences of missteps taken by high profile
companies or organizations. We take a different approach by foregrounding the
ways that ethics are implicated in the day-to-day work of data science,
focusing on instances in which data scientists recognize, grapple with, and
conscientiously respond to ethical challenges. This paper presents a case study
of ethical dilemmas that arose in a "data science for social good" (DSSG)
project focused on improving navigation for people with limited mobility. We
describe how this particular DSSG team responded to those dilemmas, and how
those responses gave rise to still more dilemmas. While the details of the case
discussed here are unique, the ethical dilemmas they illuminate can commonly be
found across many DSSG projects. These include: the risk of exacerbating
disparities; the thorniness of algorithmic accountability; the evolving
opportunities for mischief presented by new technologies; the subjective and
value- laden interpretations at the heart of any data-intensive project; the
potential for data to amplify or mute particular voices; the possibility of
privacy violations; and the folly of technological solutionism. Based on our
tracing of the team's responses to these dilemmas, we distill lessons for an
ethical data science practice that can be more generally applied across DSSG
projects. Specifically, this case experience highlights the importance of: 1)
Setting the scene early on for ethical thinking 2) Recognizing ethical
decision-making as an emergent phenomenon intertwined with the quotidian work
of data science for social good 3) Approaching ethical thinking as a thoughtful
and intentional balancing of priorities rather than a binary differentiation
between right and wrong.
