Addressing contingency in algorithmic (mis)information classification: Toward a responsible machine learning agenda

TL;DR

This paper critically examines the development of machine learning models for classifying online misinformation, highlighting the political and ethical implications, and proposes a responsible approach to their design and deployment.

Contribution

It introduces the concept of algorithmic contingencies in ML misinformation classification and advocates for reflexive, responsible development practices informed by social science insights.

Findings

Identification of key moments of contingency in model development

Analysis of potential negative impacts of ML moderation systems

Proposal for a reflexive, responsible ML development framework

Abstract

Machine learning (ML) enabled classification models are becoming increasingly popular for tackling the sheer volume and speed of online misinformation and other content that could be identified as harmful. In building these models, data scientists need to take a stance on the legitimacy, authoritativeness and objectivity of the sources of ``truth" used for model training and testing. This has political, ethical and epistemic implications which are rarely addressed in technical papers. Despite (and due to) their reported high accuracy and performance, ML-driven moderation systems have the potential to shape online public debate and create downstream negative impacts such as undue censorship and the reinforcing of false beliefs. Using collaborative ethnography and theoretical insights from social studies of science and expertise, we offer a critical analysis of the process of building ML models for (mis)information classification: we identify a series of algorithmic contingencies--key moments during model development that could lead to different future outcomes, uncertainty and harmful effects as these tools are deployed by social media platforms. We conclude by offering a tentative path toward reflexive and responsible development of ML tools for moderating misinformation and other harmful content online.