  While we have witnessed a rapid growth of ethics documents meant to guide AI
development, the promotion of AI ethics has nonetheless proceeded with little
input from AI practitioners themselves. Given the proliferation of AI for
Social Good initiatives, this is an emerging gap that needs to be addressed in
order to develop more meaningful ethical approaches to AI use and development.
This paper offers a methodology, a shared fairness approach, aimed at
identifying the needs of AI practitioners when it comes to confronting and
resolving ethical challenges and to find a third space where their operational
language can be married with that of the more abstract principles that
presently remain at the periphery of their work experiences. We offer a
grassroots approach to operational ethics based on dialog and mutualised
responsibility. This methodology is centred around conversations intended to
elicit practitioners perceived ethical attribution and distribution over key
value laden operational decisions, to identify when these decisions arise and
what ethical challenges they confront, and to engage in a language of ethics
and responsibility which enables practitioners to internalise ethical
responsibility. The methodology bridges responsibility imbalances that rest in
structural decision making power and elite technical knowledge, by commencing
with personal, facilitated conversations, returning the ethical discourse to
those meant to give it meaning at the sharp end of the ecosystem. Our primary
contribution is to add to the recent literature seeking to bring AI
practitioners' experiences to the fore by offering a methodology for
understanding how ethics manifests as a relational and interdependent
sociotechnical practice in their work.

Computers and Society