Tackling the Algorithmic Control Crisis -- the Technical, Legal, and Ethical Challenges of Research into Algorithmic Agents
Balázs Bodó, Natali Helberger, Kristina Irion, K Zuiderveen Borgesius, Judith E. Möller, Van der Velde, B. et al. · 2025 · 134 citationsRead the paper
Algorithmic agents permeate every instant of our online existence. Based on our digital profiles built from the massive surveillance of our digital existence, algorithmic agents rank search results, filter our emails, hide and show news items on social networks feeds, try to guess what products we might buy next for ourselves and for others, what movies we want to watch, and when we might be pregnant. Algorithmic agents select, filter, and recommend products, information, and people. Increasingly, algorithmic agents don't just select from the range of human created alternatives, but also they create. Burgeoning algorithmic agents are capable of providing us with content made just for us, and engage with us through one-of-a-kind, personalized interactions. Studying these algorithmic agents presents a host of methodological, ethical, and logistical challenges. The objectives of our paper are two-fold. The first aim is to describe one possible approach to researching the individual and societal effects of algorithmic recommenders, and to share our experiences with the academic community. The second is to contribute to a more fundamental discussion about the ethical and legal issues of "tracking the trackers", as well as the costs and trade-offs involved. Our paper will contribute to the discussion on the relative merits, costs and benefits of different approaches to ethically and legally sound research on algorithmic governance. We will argue that besides shedding light on how users interact with algorithmic agents, we also need to be able to understand how different methods of monitoring our algorithmically controlled digital environments compare to each other in terms of costs and benefits. We conclude our article with a number of concrete suggestions for how to address the practical, ethical and legal challenges of researching algorithms and their effects on users and society.
What the full text shows
Working prototype- Development of the 'Robin' tool to monitor algorithmically personalized digital media and track the effects of algorithmic agents on users. (Designed for use within EU data protection law (GDPR) frameworks)
- Implementation of a data minimization strategy using white lists and customized filters to avoid bulk data collection. (Applied to participant website traffic)
- Establishment of a rolling storage limitation policy for pseudonymized datasets. — deletion 5 years after last publication (Unprocessed personal data)
- Setup
- Collaborative research project involving the University of Amsterdam and CottageLabs LLP; tool designed to track website traffic across categories including search engines, social media, and political platforms.
- How
- The authors developed a monitoring tool called Robin and established a legal/ethical framework for its use. This includes implementing pseudonymization, explicit informed consent, and data filtering (white lists) to comply with EU data protection laws.
- Limitations
- Conflict between data minimization requirements and the desire for comprehensive research data. · Conflict between storage limitation laws and the ethical need for data openness/reproducibility. · Requirement for explicit consent for 'special categories of data' (e.g., health, political opinions).
Summarized by Gemma 4 from the open-access full text · 11 Oct 2026. Read it