HOW TO BE ALGORITHMICALLY GOVERNED LIKE THAT: DATA- AND ALGORITHMIC AGENCY FROM USER PERSPECTIVE
DOI:
https://doi.org/10.5210/spir.v2021i0.12227Keywords:
data agency, algorithmic agency, algorithmic governance, methodology, Subject Access RequestAbstract
If users are being $2 algorithms, and companies and regulators are proposing ways for $2 algorithms, with this paper we would like to discuss and propose a third type of governance — one where users have agency, control and governing power(s) over algorithmic systems and their outputs. Our main research question is how do we enable users to actively govern algorithms, instead of passively being governed by them? And what do the users need in order to be algorithmically governed in such a way that will enable more agency, autonomy and control when interacting with AI systems and their outputs. Instead of getting insights in an abstract way, to answer this question, we opted for a guided and supportive process where participants were able to reflect on the process, formulate and elaborate their insights, thoughts, needs and requirements based on their lived experience, i.e., after a real interaction with these algorithmic systems. We conducted a participatory technographic research with 47 participants, through a multi-stage process consisting of a survey, Subject Access Requests (Article 15 of the General Data Protection Regulation), purposeful interaction with the transparency tools of seven chosen platforms and extensive structured research diaries. A quali-quantitative analysis of the insights enabled us to formulate the participants’ requirements of $2 and $2 in a way that will enable their agency, control and autonomy. These requirements are translatable and implementable at a user-interaction level, via technology design and through regulatory mechanismsDownloads
Published
2021-09-15
How to Cite
Pop Stefanija, A., & Pierson, J. (2021). HOW TO BE ALGORITHMICALLY GOVERNED LIKE THAT: DATA- AND ALGORITHMIC AGENCY FROM USER PERSPECTIVE. AoIR Selected Papers of Internet Research, 2021. https://doi.org/10.5210/spir.v2021i0.12227
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Papers P