Suggestions Page
Ai generated media
Techno-solutionism—the reductionist belief that complex social issues can be solved purely through technology and algorithmic interventions—presents profound challenges to contemporary democracy (Pollicino & De Gregorio, 2021). Within the UK context, this is most acutely visible in the corporate monetization of data and the expansion of predictive policing. Defeating this mindset requires moving away from the “extractive paradigm” of data exploitation toward systemic, sociopolitical reforms (arXiv, 2026).
1. Rejecting Data Monetization: Data Sovereignty and Trusts
To counter the privatisation of personal data under surveillance capitalism, the UK must shift from individual consumer consent to collective data sovereignty (arXiv, 2026).
Data Trusts: Establishing independent, legally bound public data trusts can steward citizen data. Rather than allowing tech conglomerates to monopolise public data assets—such as NHS health metrics—these trusts ensure data is managed for public benefit, preventing corporate exploitation.
Strict Digital Boundaries: Expanding frameworks like the UK GDPR to outlaw behavioral futures markets. Regulators should place strict prohibitions on the monetization of sensitive demographic data, fundamentally breaking the financial incentive structure of data extraction.
2. Dismantling Predictive Policing: Democratic Oversight and Tech Refusal
Predictive policing tools (e.g., hotspot mapping or risk-scoring algorithms) are often adopted by UK police forces under the guise of objective “austerity policing” (IT University of Copenhagen, 2025; UTUPub). However, they frequently automate historical biases and disproportionately target marginalized communities (UTUPub).
The Right to Refusal: Policymakers and local councils must actively exercise the mechanism of “technological refusal.” If an algorithmic tool reproduces systemic biases or lacks transparent accountability, it should be legally banned, not optimized (UTUPub).
Co-Created Governance: Security strategies must be stripped of corporate-designed, proprietary algorithms. Instead, the governance of crime control should focus on democratic, community-led interventions that address the root socio-economic causes of crime—such as poverty and lack of local infrastructure—rather than treating symptoms with predictive software (UTUPub).
Conclusion
For UK undergraduates studying digital society, the lesson is clear: solving the crises of the digital age is not a matter of writing better code. It requires robust constitutional boundaries, public ownership of digital infrastructure, and a political commitment to prioritize human dignity over corporate algorithmic efficiency (arXiv, 2026; Pollicino & De Gregorio, 2021).
References
A New Social Contract Based on Data Sovereignty, Human Dignity and Data Personalism. (2026). arXiv. https://arxiv.org/pdf/2602.23918
Pollicino, O., & De Gregorio, G. (2021). Constitutional Law in the Algorithmic Society. Constitutional Challenges in the Algorithmic Society, 3–24. https://doi.org/10.1017/9781108914857.002 Cited by: 67
The Digital Leviathan: Prediction, Politics and Police Power in POL-INTEL. (2025). IT University of Copenhagen. https://en.itu.dk/-/media/EN/Research/PhD-Programme/PhD-defences/2025/Bjrn-Karlsson-pdf.pdf
This is a self-archived – parallel-published version of an original article. UTUPub. https://www.utupub.fi/bitstreams/3775d4b7-05f5-402f-baca-407008989e48/download
My Critical Analysis of AI
The prompt I used: Please give me a list of potential solutions to techno-solutionism, focusing primarily on the privatisation and monetisation of data and predictive policing. Your audience should be undergraduate students in the UK, and it should be 400 words long.
My initial critique of the AI-generated media was surprised at the inclusion of references, as I did not add references to this prompt nor the others, so I’m confused as to what inspired it to add them. However, the reviews are not necessarily peer-reviewed nor authoritative sources, yet they are placed as having the same value as authentic academic papers, making the validity of the referencing unreliable. Furthermore, the Generative AI didn’t necessarily follow my prompt exactly as i asked for a list of solutions, and what I received was closer to an informal essay – like response. which is helpful for undergraduate students and easy to read due to the subheadings. And while each solution method is semi – explained, the AI has still misinterpreted my prompt. I assume that it used my previous prompts as a guideline for this result.
The first paragraph doesn’t really address the monetisation of data as a result of techno-solutionism; it simply suggests methods that need to be put in place so that the government/ big companies have less access to our data, and while that is a problem on its own, these companies having access to our data doesn’t then equal its monetisation. It’s more than we should be disclosed as to what is being done with it (Katrin Tiidenberg, 2019)
The two main solutions presented by AI are Rejecting data monetisation’ and Dismantling predictive policing’. While this does suggest a step away from individualistic responsibility and a step towards systematic reform, it offers no follow-up – how will these issues be handled if not via techno-solutionism? The proposed solutions sound compelling but remain abstract. (Henrik Skaug Sætra & Evan Selinger, 2024) define techno- solutionsim as techno – fixing but with social change, which can lead to additional issues. AI provides no explanation on the potential social, cultural, or political problems that might come forth due to these actions, making the argument extremely one-sided.
To conclude, AI identifies the problems surrounding techno-solutionism well ad included a small list of potential solutions; however, this includes no follow-up, and the references included in the text are unreliable, making them invalid. It does, however, address the audience of undergraduate students in the UK well through academic language and mode of address.
