  Recently, the AI/ML research community has indicated an urgent need to
establish Responsible AI (RAI) values and practices as part of the AI/ML
lifecycle. Several organizations and communities are responding to this call by
sharing RAI guidelines. However, there are gaps in awareness, deliberation, and
execution of such practices for multi-disciplinary ML practitioners. This work
contributes to the discussion by unpacking co-production challenges faced by
practitioners as they align their RAI values. We interviewed 23 individuals,
across 10 organizations, tasked to ship AI/ML based products while upholding
RAI norms and found that both top-down and bottom-up institutional structures
create burden for different roles preventing them from upholding RAI values, a
challenge that is further exacerbated when executing conflicted values. We
share multiple value levers used as strategies by the practitioners to resolve
their challenges. We end our paper with recommendations for inclusive and
equitable RAI value-practices, creating supportive organizational structures
and opportunities to further aid practitioners.
