Democracy at Work through Transparent and Inclusive Algorithmic Management – INCODING

Growing datafication of work environments deployed by new technological capacities built on Big Data and Artificial Intelligence (AI) enhanced systems are disrupting the industrial relations scene in many ways. These technologies increase the possibilities of collecting, combining and using data on workplace and workers. However, the use of these technologies very often lacks transparency and is semiautonomous, thus jeopardising traditional forms of collective employee involvement, transparency or even data protection regulations. As AI and algorithmic decisions are increasingly widespread in employment relations, concerns are being raised about the impact of these practices on workers’ voice, influence and working conditions.

The aim of the INCODING project is to analyse the role of collective bargaining and other forms of employee involvement at workplace level in (co) governing the black box of AM with a view to identify the main challenges for workers and their representatives, and explore its contribution to Inclusive Algorithmic Management understood as the turn to more transparency in the design and implementation of AI based systems at company level and guaranteeing human oversight of automated processes. Moreover, the project also aims to learn from best practices, develop collective bargaining strategies and provide recommendations for trade unions, workers’ representatives and employers negotiate the conditions under which AM and AI systems are used.

INCODING is a joint project of 5 partner organizations from five countries, financed by the European Commission under EaSI – Programme for Employment and Social Innovation.

Start date: 01/09/2021
Finish date: 28/04/2024
Funding: DG Employment, Social Affairs and Inclusion, EUROPEAN COMMISSION
Reference: GA VS/2021/0216
Principal investigator: Oscar Molina
Team: Alejandro Godino, Sander Junte

 

Outputs:

 

Project Leaflet:

INCODING Project – Democracy at work through transparent and inclusive algorithmic management. 2022. (INCODING Project Leaftet) https://ddd.uab.cat/record/259244

 

Country Stock Taking Reports:

Makó, Csaba; Pap, József; Illéssy, Miklós; [et al.]. Emerging organizational architecture of algorithmic management and the institutional context of weak collective voice : Hungary. 2022. (INCODING Stock-Taking Reports) https://ddd.uab.cat/record/266648

Godino Pons, Alejandro; Junte, Sander; Molina Romo, Óscar. Developments in algorithmic management from an IR-perspective : Spain. 2022. (INCODING Stock-Taking Reports) https://ddd.uab.cat/record/266647

Wotschack, Philip; Butollo, Florian. Developments in Algorithmic Management from an IR-perspective : Germany. 2022. (INCODING Stock-Taking Reports) https://ddd.uab.cat/record/266646

Larsen, Trine P.; Ilsøe, Anna; Krøll Tell, Henriette; [et al.]. Developments in algorithmic management from an IR-perspective : Denmark. 2022. (INCODING Stock-Taking Reports) https://ddd.uab.cat/record/266644

 

Dissemination activities:

Project website

Embracing and resisting the algorithm – International Workshop Program (9/06/2023)

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