Participation is not a Design Fix for Machine Learning

TL;DR

This paper critically analyzes participation in machine learning design, warning against superficial engagement and emphasizing the need to recognize and avoid exploitative practices that hinder genuine community involvement.

Contribution

It highlights the risks of 'participation-washing' in ML and advocates for a shift away from superficial participation towards more ethical, context-aware community engagement.

Findings

Participation can be exploited or superficial

Current ML practices risk extractive community involvement

Calls for more ethical and context-sensitive participation methods

Abstract

This paper critically examines existing modes of participation in design practice and machine learning. Cautioning against 'participation-washing', it suggests that the ML community must become attuned to possibly exploitative and extractive forms of community involvement and shift away from the prerogatives of context-independent scalability.