`It is currently hodgepodge'': Examining AI/ML Practitioners' Challenges during Co-production of Responsible AI Values
It addresses gaps in implementing Responsible AI practices for multi-disciplinary practitioners, though it is incremental in providing empirical insights.
The study examined challenges faced by AI/ML practitioners in co-producing Responsible AI values, finding that institutional structures create burdens and exacerbate conflicts, with strategies like value levers used to resolve issues.
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.