AI systems pose both opportunities and threats in various industries.To harness these opportunities and mitigate risks, accountability is crucial.Traditionally, developers bear the responsibility for auditing and Boots modifying algorithms.How-ever, in the evolving landscape of versatile AI, developers may lack contextual understanding across diverse fields.
This paper proposes a theoretical framework that distributes accountability to developers and practitioners according to their capabilities.This framework enhances Collections systemic com-prehension of shared roles, empowering both groups to col-laboratively avert potential adverse impacts.