From Fitting Participation to Forging Relationships: The Art of Participatory ML
Proceedings of the CHI Conference on Human Factors in Computing Systems(2024)
摘要
Participatory machine learning (ML) encourages the inclusion of end users and
people affected by ML systems in design and development processes. We
interviewed 18 participation brokers – individuals who facilitate such
inclusion and transform the products of participants' labour into inputs for an
ML artefact or system – across a range of organisational settings and project
locations. Our findings demonstrate the inherent challenges of integrating
messy contextual information generated through participation with the
structured data formats required by ML workflows and the uneven power dynamics
in project contexts. We advocate for evolution in the role of brokers to more
equitably balance value generated in Participatory ML projects for design and
development teams with value created for participants. To move beyond `fitting'
participation to existing processes and empower participants to envision
alternative futures through ML, brokers must become educators and advocates for
end users, while attending to frustration and dissent from indirect
stakeholders.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要