  Recent AI ethics has focused on applying abstract principles downward to
practice. This paper moves in the other direction. Ethical insights are
generated from the lived experiences of AI-designers working on tangible human
problems, and then cycled upward to influence theoretical debates surrounding
these questions: 1) Should AI as trustworthy be sought through explainability,
or accurate performance? 2) Should AI be considered trustworthy at all, or is
reliability a preferable aim? 3) Should AI ethics be oriented toward
establishing protections for users, or toward catalyzing innovation? Specific
answers are less significant than the larger demonstration that AI ethics is
currently unbalanced toward theoretical principles, and will benefit from
increased exposure to grounded practices and dilemmas.
