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Accepted Paper:

Workers’ role in the accountability of algorithms: a policy analysis  
Gwendolin Barnard (University of Graz)

Short abstract:

This project interrogates how policy discourses within the EU and UK position workers as participants in anticipating and mitigating harms stemming from the deployment of data-driven systems in workplaces. It assesses how datafied publics are anticipated through policy instruments.

Long abstract:

Policy discourses on the accountability of data-driven systems often pose the participation of workers as key in mitigating algorithmic harms experienced in the domain of work. Methodologies for anticipating risks and opportunities such as Algorithmic Impact Assessments consider workers as stakeholders promising a fairer or more equal deployment of data-driven systems in workplaces. My research is interested in how these initiatives anticipate and produce datafied publics in the realm of work.

By conducting a document analysis of policy documents and grey literature across the European Union and the UK, this project critically assesses how AI policy and data governance position workers as participants in mitigating, containing, and anticipating harms occurring through the deployment and use of data-driven systems at work. In doing so, it asks how workers are anticipated to play a role in the deployment and design (ex ante) as well as in the scrutiny (ex post) of data-driven systems. Based on this, I will develop a systematisation of regimes of anticipation: How do they imagine organizational structures, workers’ roles, knowledge contribution and practices, institutional processes of participation, and anticipated harms addressed in the analysed methodologies. This provides a framework by which to assess how datafied publics are constituted by participation processes in various industries including the gig economy.

Underlining this research is the question of whether these processes are aspiring towards the normative vision of participatory parity (Fraser, 1992) wherein workers’ knowledge, status and position can meaningfully shape, refuse, and influence data-driven systems at work.

Traditional Open Panel P142
Datafied publics
  Session 2 Thursday 18 July, 2024, -