While we have witnessed a rapid growth of ethics documents meant to guide AI\ndevelopment, the promotion of AI ethics has nonetheless proceeded with little\ninput from AI practitioners themselves. Given the proliferation of AI for\nSocial Good initiatives, this is an emerging gap that needs to be addressed in\norder to develop more meaningful ethical approaches to AI use and development.\nThis paper offers a methodology, a shared fairness approach, aimed at\nidentifying the needs of AI practitioners when it comes to confronting and\nresolving ethical challenges and to find a third space where their operational\nlanguage can be married with that of the more abstract principles that\npresently remain at the periphery of their work experiences. We offer a\ngrassroots approach to operational ethics based on dialog and mutualised\nresponsibility. This methodology is centred around conversations intended to\nelicit practitioners perceived ethical attribution and distribution over key\nvalue laden operational decisions, to identify when these decisions arise and\nwhat ethical challenges they confront, and to engage in a language of ethics\nand responsibility which enables practitioners to internalise ethical\nresponsibility. The methodology bridges responsibility imbalances that rest in\nstructural decision making power and elite technical knowledge, by commencing\nwith personal, facilitated conversations, returning the ethical discourse to\nthose meant to give it meaning at the sharp end of the ecosystem. Our primary\ncontribution is to add to the recent literature seeking to bring AI\npractitioners' experiences to the fore by offering a methodology for\nunderstanding how ethics manifests as a relational and interdependent\nsociotechnical practice in their work.\n