References
Mann, M., Mitchell, P., & Foth, M. (2022). Between surveillance and technological solutionism: A critique of privacy-preserving apps for COVID-19 contact-tracing. New Media & Society, 14614448221109800. https://doi.org/10.1177/14614448221109800
Tiidenberg, K., 2018. Ethics in digital research. The SAGE handbook of qualitative data collection, pp.466-479.
Selinger, E. (2013) ‘Technological Fixes’, review of To Save Everything, Click Here: The Folly of Technological Solutionism by E. Morozov, The Wilson Quarterly, 37(2). Available at: https://www.jstor.org/stable/wilsonq.37.2.11
Amnesty International UK (no date) ‘UK: Police forces “supercharging racism” with crime predicting tech – new report’. Available at: https://www.amnesty.org.uk/latest/uk-police-forces-supercharging-racism-crime-predicting-tech-new-report/
ætra, H.S., Selinger, E. Technological Remedies for Social Problems: Defining and Demarcating Techno-Fixes and Techno-Solutionism. Sci Eng Ethics 30, 60 (2024). https://doi.org/10.1007/s11948-024-00524-x
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
Use of AI disclosure
| Acknowledge I acknowledge the use of <insert AI system(s) and link> for the following purposes: to generate materials for background research and self-study in the drafting of this assessment. to generate materials that were included within my final assessment in modified form. |
| Describe Please provide a short summary of how you used generative AI in your assignment. You may wish to include the following information: What prompts did you use? What outputs did you generate? How did you use/adapt/develop the outputs? Summary: I used Google’s generative AI by writing prompts to receive 400-word outputs for my blog about the issues surrounding Techno – solutionism to critique how well the AI had followed the prompt. |
| Evidence Please provide evidence of the outputs that you generated by copying and pasting below or by providing a screenshot. Generative AI system: Prompt: give me a 400 word blog about an introduction to the most significant social, political and cultural issues that surround techno-solutionism? In your answer, refer to contemporary examples to support your key claims, aimed at university students in the uk Output:Imagine you are running late for a lecture at a UK university, and your campus navigation app glitches. Annoying, sure—but easily fixed with a software update. Now, imagine trying to use that same algorithmic logic to solve systemic poverty, institutional racism, or the climate crisis. This is the core illusion of techno-solutionism: the ideology, famously coined by theorist Evgeny Morozov, that views deeply complex, structural human dilemmas merely as efficiency problems waiting for an app, an AI, or a digital platform to fix them. For university students navigating today’s digital landscape, unpacking this myth is crucial. The Social Cost: Aggravating Inequality Techno-solutionism often worsens the very social issues it claims to cure. Take the UK’s ongoing housing crisis. The rise of short-term letting platforms like Airbnb was marketed as a “sharing economy” triumph to help people monetize spare space. Instead, it hollowed out local communities, drove up rents, and depleted long-term housing stock in cities from Edinburgh to London. By treating housing as a digital optimization game, tech obscured the social reality of displacement. The Political Threat: Automated Injustice Politically, outsourcing governance to algorithms bypasses democratic accountability. We saw the catastrophic human cost of this during the UK’s Post Office Horizon scandal. For years, a flawed computer system falsely flagged shortfalls in branch accounts. Instead of questioning the technology, management trusted the software blindly, leading to the wrongful prosecution and financial ruin of hundreds of subpostmasters. When we treat technology as an infallible authority, we erode legal protections and political recourse. The Cultural Shift: From Citizens to Data Points Culturally, techno-solutionism reduces rich human experiences into quantifiable data. In higher education, the sudden ubiquity of generative AI tools like ChatGPT is frequently framed as the ultimate solution to academic productivity. However, this shifts our cultural value of education away from critical thinking, slow reflection, and intellectual struggle, reframing it as a mere exercise in efficient content generation. We risk becoming passive consumers of outputs rather than active, critical citizens. The Bottom Line: Technology is a powerful tool, but it is never neutral. As we look to the future, the challenge for our generation isn’t to reject technology, but to reject the lazy assumption that a line of code can replace structural political reform, social empathy, and collective human action. Some problems require policy, protest, and structural change—not just a better user interface Prompt: You are addressing an undergraduate UK-based audience. This is the article you will be talking about: https://www.amnesty.org.uk/latest/uk-police-forces-supercharging-racism-crime-predicting-tech-new-report/. Please highlight its key takeaways and strengths and analyse it in 400 words please. Output:Amnesty International’s recent report, “Automated Apartheid,” raises significant concerns about the increasing use of “predictive policing” technology by UK police forces. For an undergraduate audience, the key takeaways from this article are particularly relevant, highlighting critical issues around human rights, privacy, and algorithmic bias. Firstly, the report claims that police forces are adopting these technologies, such as facial recognition and AI-powered data analytics, without sufficient public oversight or understanding of their implications. This raises questions about democratic accountability and transparency in the use of powerful surveillance tools. Students studying law, sociology, or computer science might find this particularly interesting in relation to ethical computing and state power. Secondly, a major strength of the report is its focus on the potential for these technologies to “supercharge racism.” Amnesty argues that because predictive policing systems often rely on historical crime data, which itself reflects existing biases in policing, they risk perpetuating and even amplifying discrimination against Black people and other ethnic minorities. This is a crucial point for anyone interested in social justice and the intersection of technology and inequality, suggesting that the “neutrality” of algorithms is a myth when applied to biased data. The report employs strong, evocative language like “Automated Apartheid” to underscore the severity of these potential outcomes, which is a powerful rhetorical choice. Thirdly, the article emphasizes the impact on individual rights, particularly the rights to privacy and non-discrimination. The idea of being constantly monitored and potentially targeted by an algorithm based on your ethnicity or location is a profound challenge to civil liberties. This resonates strongly with discussions around surveillance states and the balance between security and freedom. Finally, the report calls for a moratorium on using these technologies until robust regulations and safeguards are in place. This pragmatic recommendation offers a clear action point and underscores the urgency of the issue. The strength here lies in not just identifying a problem but also proposing a concrete solution. Overall, “Automated Apartheid” presents a compelling and timely analysis of a complex issue. Its strengths lie in its clear articulation of the risks, its focus on human rights, and its clear calls for action. For undergraduates, it serves as a powerful case study in the ethical challenges of emerging technologies and the importance of critical engagement with state power and algorithmic governance. Prompt: 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. Output:Amnesty International’s recent report, “Automated Apartheid,” raises significant concerns about the increasing use of “predictive policing” technology by UK police forces. For an undergraduate audience, the key takeaways from this article are particularly relevant, highlighting critical issues around human rights, privacy, and algorithmic bias. Firstly, the report claims that police forces are adopting these technologies, such as facial recognition and AI-powered data analytics, without sufficient public oversight or understanding of their implications. This raises questions about democratic accountability and transparency in the use of powerful surveillance tools. Students studying law, sociology, or computer science might find this particularly interesting in relation to ethical computing and state power. Secondly, a major strength of the report is its focus on the potential for these technologies to “supercharge racism.” Amnesty argues that because predictive policing systems often rely on historical crime data, which itself reflects existing biases in policing, they risk perpetuating and even amplifying discrimination against Black people and other ethnic minorities. This is a crucial point for anyone interested in social justice and the intersection of technology and inequality, suggesting that the “neutrality” of algorithms is a myth when applied to biased data. The report employs strong, evocative language like “Automated Apartheid” to underscore the severity of these potential outcomes, which is a powerful rhetorical choice. Thirdly, the article emphasizes the impact on individual rights, particularly the rights to privacy and non-discrimination. The idea of being constantly monitored and potentially targeted by an algorithm based on your ethnicity or location is a profound challenge to civil liberties. This resonates strongly with discussions around surveillance states and the balance between security and freedom. Finally, the report calls for a moratorium on using these technologies until robust regulations and safeguards are in place. This pragmatic recommendation offers a clear action point and underscores the urgency of the issue. The strength here lies in not just identifying a problem but also proposing a concrete solution. Overall, “Automated Apartheid” presents a compelling and timely analysis of a complex issue. Its strengths lie in its clear articulation of the risks, its focus on human rights, and its clear calls for action. For undergraduates, it serves as a powerful case study in the ethical challenges of emerging technologies and the importance of critical engagement with state power and algorithmic governance. |
| Declaration I confirm that no content created by generative AI technologies has been presented as my own work. |