`It is currently hodgepodge'': Examining AI/ML Practitioners' Challenges during Co-production of Responsible AI Values

TL;DR

This paper explores the challenges AI/ML practitioners face in implementing Responsible AI values, highlighting institutional burdens and strategies to promote inclusive, equitable practices within organizations.

Contribution

It uncovers co-production challenges in aligning RAI values among practitioners and offers practical recommendations for organizational improvements.

Findings

Institutional structures create burdens for practitioners.

Conflicted values exacerbate RAI implementation challenges.

Practitioners use various value levers to address challenges.

Abstract

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.