Tag Archives: #AIEthics

Creating Art In The Age Of AI.

Here’s a series of actionable instructions to guide yourself as an artist in the AI era. Treat these as reminders to refocus on your intrinsic motivations and leverage your unique human strengths.

Reflect on Your Core Motivation: Ask yourself why you’re making art in the first place. Write down your reasons – is it for personal expression, joy, or something else? If it’s primarily for external validation like social media likes, challenge that by creating one piece this week purely for yourself, without sharing it.
Define Your Audience and Goals: Clarify who your art is for – yourself, a small circle, or the public? If public, define what success means to you (e.g., meaningful feedback vs. viral hits). Set a personal success metric, like “complete one project that sparks a conversation,” and track progress toward it monthly.
Test Your Commitment: Imagine your entire creative setup is destroyed. Would you rebuild it? If yes, affirm your passion by dedicating time each day to creating without excuses. If not, explore other fulfilling activities to redirect your energy.
Embrace Human Uniqueness: Remember that AI lacks intent and personal experience. Translate your abstract ideas or emotions into art deliberately – start by journaling one lived experience per session and turning it into a musical element or artwork.
Avoid Genre Traps: If working in a structured genre, don’t just replicate patterns (which AI excels at). Intentionally break rules: Add an unexpected element and keep it human (e.g., fusing bal folk tunes with highland pipes and smallpipes) in your next piece to infuse originality from your mind.
Prioritise Novelty Over Perfection: Chase ideas that intrigue you personally, not flawless output. Experiment with “weird gremlin thoughts” – set aside time weekly for accidental or random creations, then refine them into intentional work.
Differentiate Hearing vs. Listening: Aim to make art that invites active engagement and conversation, not passive background filler. Review your recent work: Does it provoke introspection or critique? Revise one piece to emphasise emotional depth or uniqueness.
Leverage Limitations as Strengths: Use your imperfections (e.g., skill gaps) as opportunities for innovation. Identify a weakness in your technique, then create a workaround in your next project – document how it adds a personal “U” (unique touch) to your art.
Rise Above the AI Noise: In a sea of average AI content, make your work stand out by highlighting overlooked ideas. Observe something unnoticed in your daily life and “shove it in faces” through your art – produce one such piece this month.
View AI as a Bar-Raiser: See AI as making this an exciting time for artists. Commit to creating something unique or weird regularly, knowing it gains value against infinite “slop.” Track how this mindset shifts your output over the next few projects.
Final Affirmation: Remind yourself daily: Your art is a reflection of your human condition – feelings, experiences, and soul. AI is just output; yours is an expression of self. If doubt creeps in, revisit this list and create anyway.

Lesser-Known Truths About AI’s Inability to Question or Redefine Art. The statement “The machine doesn’t care. It knows exactly what art is. It has no reason and no ability to question what it could be” highlights a core philosophical critique of AI in creativity: it operates within predefined patterns without genuine intent, emotion, or the capacity for existential questioning. While mainstream narratives often celebrate AI as a democratising force in art (e.g., tools like DALL-E or Midjourney making creation more accessible), lesser-known truths challenge this by exposing AI’s fundamental limitations and their broader implications for humanity. These ideas are rarely shared publicly because they undermine the trillion-dollar AI industry, which thrives on hype, investment, and the promise of infinite productivity. Tech giants like Adobe and Meta promote AI as “creative” to sell subscriptions and data-training models, suppressing critiques that could slow adoption or invite regulation. Below, I outline key under-the-surface truths, supported by historical context, real-world examples, and explanations for their obscurity. 1. AI Art is a “Philosophical Zombie” – It Mimics Without Consciousness. AI can replicate artistic styles flawlessly but lacks inner experience or intent, resulting in hollow imitations rather than true expressions. This challenges the mainstream view that AI’s technical prowess equates to creativity, revealing it as mere pattern-matching.

Historical Context: This draws from René Descartes’ 1637 dictum “I think, therefore I am,” which tied existence to self-aware thought. More directly, it echoes John Searle’s 1980 “Chinese Room” argument: a person following rules to simulate understanding Chinese doesn’t truly comprehend it. Similarly, AI “understands” art only through data, not lived reality. Early algorithmic art in the 1960s (e.g., AARON by Harold Cohen) was celebrated, but philosophers like Searle warned it lacked soul, a critique buried as AI evolved.
Real-World Examples: In 2022, an AI-generated piece won the Colorado State Fair’s fine art competition, sparking backlash from artists who argued it lacked emotional depth. csferrie.medium.com Midjourney’s early versions struggled with human hands, symbolising its detachment from embodied experience—AI doesn’t “feel” anatomy like a human artist does. blog.jlipps.com
Why It Remains Hidden: Acknowledging this would deflate AI hype, as companies frame tools as “co-creators” to attract users. Investors and media focus on output quality to avoid philosophical debates that could lead to ethical restrictions, such as EU AI regulations that emphasise transparency.

2. AI Erodes Human Creative Capacity Through Atrophy and Over-Reliance. By handling the “hard” parts of creation, AI causes human skills to wither, turning art into a commodified process rather than a form of personal growth. This counters the mainstream claim that AI “lowers barriers” to creativity, showing it instead homogenises output and stifles innovation.

Historical Context: As with the 15th-century printing press, which displaced scribes but forced writers to innovate (e.g., leading to the rise of the novel), photography in the 1830s threatened painters until they embraced abstraction (e.g., Impressionism). Critics like Walter Benjamin in 1935 warned of art’s “aura” being lost in mechanical reproduction; today, AI amplifies this by automating not just reproduction but also ideation.
Real-World Examples: Artists using AI prompts often iterate endlessly to approximate their vision, losing direct agency—e.g., a digital artist settling for AI’s “approximation” rather than honing their skills. blog.jlipps.com In music, tools like Suno generate tracks, but users report diminished satisfaction from not “struggling” through composition, echoing how auto-tune reduced vocal training in pop. aokistudio.com
Why It Remains Hidden: The AI industry markets efficiency to creative professionals (e.g., Adobe’s Firefly), downplaying the long-term erosion of skills to maintain market growth. Public discourse prioritises short-term gains like “democratisation,” as admitting to atrophy could spark backlash from educators and unions concerned about job devaluation.

3. AI Exposes the Illusion of Human Originality, Revealing Most “Creativity” as Formulaic AI’s ability to produce “art” faster than humans uncovers that much human work is pattern-based remix, not true novelty—challenging the romanticised view of artists as innate geniuses and forcing a reevaluation of what “creative” means.

Historical Context: The Renaissance idealised the “divine” artist (e.g., Michelangelo), but 20th-century postmodernism (e.g., Warhol’s factory art) questioned originality. AI builds on this; Alan Turing’s 1950 “imitation game” test foreshadowed machines mimicking creativity without possessing it, but his warnings about over-attribution were overshadowed by computational optimism.
Real-World Examples: A Reddit discussion notes AI “revealing how little we ever had” by outperforming formulaic genres like lo-fi beats or stock photos, where humans were already “echoing” patterns. reddit.com In 2023, AI-generated books flooded Amazon, exposing how much publishing relies on tropes—authors admitted their “unique” stories were easily replicated. lateralaction.com
Why It Remains Hidden: This truth wounds egos in creative industries, where “originality” justifies high valuations (e.g., NFTs). Tech firms and media avoid it to prevent demotivation, as it could reduce user engagement with AI tools—why prompt if it highlights your own mediocrity?

4. AI Art Detaches Us from Authentic Human Connection and Imperfection AI’s frictionless perfection creates idealised content that erodes empathy and growth, as art traditionally thrives on flaws and shared vulnerability—undermining the idea that AI enhances human expression.

Historical Context: Existentialists like Jean-Paul Sartre (1943) emphasised authentic self-expression through struggle; AI bypasses this. In the 1960s, Marshall McLuhan’s “medium is the message” critiqued how technology alters perception—AI extends this by simulating emotions without feeling them, akin to early CGI’s “uncanny valley” debates.
Real-World Examples: Social media filters and AI portraits promote flawless selves, linked to rising mental health issues; a podcaster notes AI “detaches you from the reality of growth.” creativeprocess.info In visual art, AI’s inability to “risk” (e.g., avoid bold failures) results in bland aggregates, as seen in critiques of DALL-E outputs that lack “visceral” passion. aokistudio.com +1
Why It Remains Hidden: Platforms like Instagram benefit from idealised content for engagement metrics. Revealing this could invite scrutiny of AI’s role in societal disconnection, clash with Silicon Valley’s narrative of “connecting the world,” and risk lawsuits or boycotts from mental health advocates.

5. AI cannot Transcend Its Training Data, Limiting True Innovation. Locked into syllogistic logic from datasets, AI reinforces averages rather than questioning norms—contradicting claims of AI as a boundless innovator.

Historical Context: Gottfried Leibniz’s 17th-century dream of a “universal calculus” for all knowledge prefigured AI, but critics like Hubert Dreyfus (1972) argued computers lack intuitive “being-in-the-world” (Heideggerian philosophy). This “frame problem” persists: AI can’t question its assumptions without human intervention.
Real-World Examples: AI art tools replicate biases from training data (e.g., stereotypical depictions), failing to “leap” like Picasso’s Cubism. Research shows that AI “lacks the sensual/philosophical depth” for originality. researchgate.net In writing, ChatGPT produces coherent but uninspired prose, unable to write in the paradoxical style of Kafka.
Why It Remains Hidden: Data dependencies expose ethical issues like IP theft during training (e.g., lawsuits against Stability AI), which companies obscure through NDAs and lobbying. Publicising it could halt progress, as it questions AI’s hype around scalability.

These truths, while supported by philosophers and artists, stay underground due to economic pressures: AI’s market is projected at $1.8 trillion by 2030, incentivising positive spin. However, voices in academia and indie communities (e.g., Reddit, blogs) keep them alive, suggesting a potential shift if regulations evolve.

AI Ethics in Creativity: Navigating the Moral Landscape. AI’s integration into creative fields like art, music, writing, and design has sparked intense debate. While it promises to democratize creation and amplify human potential, it raises profound ethical questions about authorship, exploitation, and the essence of human expression. As of January 2026, ongoing lawsuits, regulatory pushes (e.g., EU AI Act updates), and public backlash highlight these tensions. Below, I break down key ethical concerns, drawing from diverse perspectives—including tech optimists, artists, ethicists, and critics—to provide a balanced view. This includes pro-AI arguments for augmentation and critiques of systemic harm, substantiated by recent developments. Core Ethical Concerns: AI in creativity isn’t just a tool; it intersects with human identity, labour, and society. Here’s a table summarising major issues, with examples and counterpoints:

Ethical Issue	Description	Real-World Examples	Why It Challenges Mainstream Thinking	Counterarguments
Intellectual Property (IP) Infringement and Data Theft	AI models are often trained on vast datasets scraped from the internet without creators’ consent or compensation, effectively “laundering” human work into commercial outputs. This violates the social contract where artists share work expecting legal protections against market dilution.	– Danish CMO Koda sued Suno in 2025 for using copyrighted music without permission. @ViralManager – Activision Blizzard’s 2024 layoffs of artists amid AI adoption, using models trained on unlicensed content. @ednewtonrex – Ongoing U.S. lawsuits against Midjourney and Stability AI for training on artists’ works.	Undermines the AI hype of “innovation for all” by exposing it as profit-driven exploitation, hidden to avoid lawsuits and investor backlash. bytemedirk.medium.com +3	Pro-AI view: Training is “fair use” like human learning; ethical models (e.g., Fairly Trained) seek consent, but most companies argue it accelerates creativity without direct copying.
Job Displacement and Labor Exploitation	AI automates creative tasks, leading to layoffs and devaluing human skills. It shifts income from creators to tech firms, exacerbating inequality. bytemedirk.medium.com +6	– Larian Studios (Baldur’s Gate 3) banned non-internal AI in 2025 to prioritize ethics and quality. @pulpculture323 – Universal Music Group’s 2026 NVIDIA partnership aims to protect artists while expanding creativity. @jjfleagle – Freelancers report AI “infesting” markets, making livelihoods harder. @mohaned_hawesh	Reveals capitalism’s prioritization of efficiency over human flourishing, suppressed by tech lobbying to maintain growth narratives. forbes.com +2	AI augments humans (e.g., Adobe’s ethical tools); job shifts are inevitable, like photography displacing painters in the 19th century. gonzaga.edu +1
Loss of Authenticity and Human Essence	AI outputs lack genuine intent, emotion, or originality, potentially atrophying human creativity and turning art into commodified “slop.” It questions what makes art “human.” liedra.net +4	– Polls show 90%+ of artists object to AI training on their work. @ednewtonrex – Deepfakes and misinformation from AI art (e.g., viral fakes in 2025 elections). liedra.net +1 – xAI’s Grok faced UK probes in 2026 for non-consensual images. @jjfleagle	Challenges romanticized views of progress; hidden because it critiques AI’s “limitless” potential, risking demotivation. niusteam.niu.edu +1	AI inspires novelty; e.g., human-AI collabs in music (NVIDIA-UMG) foster new expressions. gonzaga.edu +2
Bias, Misuse, and Societal Harm	Datasets inherit human biases, perpetuating stereotypes. AI enables deepfakes, misinformation, and environmental costs (e.g., high carbon emissions from training).

A Field Guide to Becoming a Reasoning Critical Thinker in a Post Truth World

A Field Guide to Reason: Human Logic, Cognitive Bias, and the AI Mirage

In 2026, the pursuit of truth is no longer a simple matter of “common sense.” We are navigating a world where human biological biases, ancient logical errors, and the “alien” irrationality of Artificial Intelligence have collided.

Many people have “farmed out” their thinking to machines, but those machines have their own systemic flaws—and the strategies used to “fix” them are often just as broken. To maintain your intellectual sovereignty, you must master the five dimensions of modern reason.

Part I: The Field Guide to Logical Fallacies (30 Common “Dirty Tricks”)

Logical fallacies are errors in reasoning that destroy the quality of an argument. Use this list to spot when a conversation is being derailed.

1. The Personal & Origin Attacks

Ad Hominem: Attacking the person’s character rather than their message.
Tu Quoque: Avoiding criticism by pointing out the critic’s own flaws.
Genetic Fallacy: Judging an idea based solely on its source or origin.
The Straw Man: Distorting an argument into a weaker version to easily tear it down.
No True Scotsman: Redefining a group to exclude counter-examples (moving the goalposts).

2. The Emotional Appeals

Appeal to Emotion: Using fear, pity, or anger instead of facts to win.
Appeal to Pity: Invoking sympathy for a hardship to support an unrelated claim.
Appeal to Fear: Scaring the audience into agreement by exaggerating threats.

3. Authority & Tradition

Appeal to Authority: Using an expert’s opinion as proof without supporting evidence.
Bandwagon Fallacy: Assuming something is true because it is popular.
Appeal to Tradition: Claiming something is right because it’s “how we’ve always done it.”
Appeal to Novelty: Arguing that something is superior simply because it is new.
Personal Incredulity: Rejecting an idea because you find it hard to understand.

4. Data, Cause & Probability

Hasty Generalization: Drawing a sweeping conclusion from a tiny, anecdotal sample.
Post Hoc Ergo Propter Hoc: Assuming that because B followed A, A must have caused B.
The Texas Sharpshooter: Cherry-picking data to fit a story while ignoring the rest.
Gambler’s Fallacy: Believing that past independent events affect future probability.
Burden of Proof: Claiming something is true because it hasn’t been proven false.
False Analogy: Comparing two things that aren’t truly alike.

5. Diversion & Balance

Red Herring: A distraction masquerading as a relevant point to shift the topic.
False Dilemma: Presenting two extreme options as the only possibilities.
Slippery Slope: Insisting that one small step will inevitably lead to a catastrophe.
Loaded Question: A “trap” question that contains a built-in presumption of guilt.
Argument from Ignorance: Claiming truth because something hasn’t been proven otherwise.
Argument to Moderation: Assuming the truth lies exactly in the middle of two extremes.

6. Linguistic & Circular Games

Begging the Question: A circular argument where the conclusion is assumed in the premise.
Equivocation: Using the same word in two different ways to mislead.
Non-Sequitur: A conclusion that simply does not logically follow from the premise.
Sunk Cost Fallacy: Arguing to continue a path simply because of past investment.
The Fallacy Fallacy: Assuming a claim is false simply because it was argued poorly.

Part II: The Engine Room (15 Mappings of Bias to Fallacy)

Cognitive biases are the biological “bugs” in our brain’s software. They predispose us to commit the fallacies listed above.

Cognitive Bias	Linked Fallacy	The Connection
Confirmation Bias	Cherry-Picking	We seek only the info that confirms our existing “pattern.”
Anchoring Bias	Part/Whole Fallacy	Our judgment is “stuck” to the first piece of info we encounter.
Hindsight Bias	Historian’s Fallacy	We retroactively assume the past was more predictable than it was.
Availability Heuristic	Hasty Generalization	We think something is common just because it’s “vivid” in our memory.
Sunk Cost Bias	Sunk Cost Fallacy	We irrationally weigh past effort over future utility.
Bandwagon Effect	Ad Populum	We equate the “majority view” with the “correct view.”
Authority Bias	Ad Verecundiam	We overvalue titles and credentials over raw evidence.
In-Group Bias	No True Scotsman	we protect our “tribe” by moving the goalposts for outsiders.
Belief Bias	Fallacy Fallacy	We accept a bad argument if we like the conclusion it reaches.
Projection Bias	Psychologist’s Fallacy	We assume everyone else shares our specific mental state.
Stereotyping	Genetic Fallacy	We judge an idea based on the “group” it belongs to.
Outcome Bias	Post Hoc	We judge the quality of a decision based solely on the result.
Dunning-Kruger	Personal Incredulity	Our lack of skill in an area makes us unable to see our own errors.
False Consensus	Ad Populum	We overestimate how much people agree with us.
Halo Effect	Non-Sequitur	We let one positive trait (like beauty) color our entire judgment.

Part III: The Sophisticated Nuance (6 Truths of Logic)

Mastery of reason means knowing when the “rules” of logic are actually flexible.

Context is King: Many “fallacies” are actually valid in certain contexts. Deferring to a scientific consensus (Appeal to Authority) is a sound way to handle uncertainty.
The Power of the Enthymeme: Humans naturally omit “obvious” premises for efficiency. If you attack every incomplete sentence as a “Non-Sequitur,” you aren’t being logical—you’re being pedantic.
Fallacy-Hunting as a Weapon: Over-naming fallacies is often a form of poor reasoning used to stifle debate and avoid engaging with real-world inductive evidence.
Bad Arguments ≠ Wrong Conclusions: A person can argue for the truth using a fallacy. Don’t dismiss a true fact just because the person speaking it is a poor advocate.
Taxonomy is Arbitrary: Logical “rules” are cultural artefacts. What the West calls an “Appeal to Tradition,” other cultures call “Cultural Continuity.”
Fallacies are Adaptive: We aren’t “bad at logic”; we are “good at survival.” Our biases were designed to help us make split-second decisions in a dangerous world.

Part IV: The Silicon Mirror (6 Truths of AI Reasoning Bias)

AI does not think like a human. It has “alien” biases rooted in its code and architecture.

Position Bias: LLMs overemphasise information at the start and end of a prompt. Important evidence “buried in the middle” is often ignored by AI reasoners.
Alien Irrationality: AI doesn’t have “emotions,” but it has “probabilistic bias.” It gives inconsistent answers to logic puzzles because it predicts tokens rather than understanding concepts.
Linguistic Neocolonialism: AI models are biased toward English- and Western-language data. Reasoning in non-Western languages or cultural contexts is significantly less accurate.
AI-AI Bias: Models have been found to favour machine-produced text over human-produced text, risking a self-reinforcing loop that disadvantages human creativity.
The Interpretability Paradox: “Fixing” an AI bias often introduces new ones. Debiasing a model for social fairness often degrades its performance in math and technical logic.
Intrinsic Stereotypes: Social biases are baked into the “embeddings” of AI architecture. Fine-tuning offers surface-level fixes, but the deep stereotypes persist under the hood.

Part V: The Mitigation Mirage (6 Truths of “Fixing” AI)

When tech companies claim they have “debiased” AI, the reality is far more complicated.

The Impossibility of Total Fairness: You cannot satisfy all fairness metrics at once. Fixing one group’s bias often accidentally increases rejections for another group.
Internal vs. External Fixes: Telling an AI “don’t be biased” (prompting) is fragile. Editing the model’s “brain” (Concept Editing) is better, but it often makes the model less accurate overall.
Ethical Imperialism: AI mitigation tools export Western values. A “fair” model in New York may be deeply biased and harmful when deployed in an African healthcare setting.
Bias Washing: Companies often use cheap audits to claim their AI is “fair” while avoiding the expensive work of fixing the underlying data or architecture.
The RLHF Trap: “Human-in-the-loop” governance often just entreats the specific subjective biases of the human curators who are training the AI.
Model Drift: AI logic is not “set-it-and-forget-it.” As models ingest new data, old biases resurface, requiring a constant (and often ignored) cycle of expensive monitoring.

Conclusion: The Reasoning Architect

In 2026, the goal of learning logic isn’t to “win” every argument. It’s to avoid the “mud-wrestling” pits of misinformation altogether.

By understanding these 30 fallacies, the 15 biases that fuel them, the 6 philosophical nuances, and the 12 flaws of AI and its mitigation, you move from a consumer of information to a being a Reasoning Critical Thinker.

Bookmark this guide, value nuance. Remember: the goal of logic isn’t to win, it’s to see the world as it truly is.

‘Clowns to the left of me
Jokers to the right
Here I am, stuck in the middle with you‘
Lyric from: ‘Stuck In The Middle With You’
Written by: Joe Egan, Gerald Rafferty

Smoke and Mirrors: Forget the small boats. The Real Mass Migration is Digital.

The Fourth World is Coming. It’s Just Not What You Think.

What if the biggest migration in human history isn’t human at all? There’s a theory doing the rounds that frames the AI revolution as just that: an “unlimited, high-IQ mass migration from the fourth world.” It argues we’re witnessing the arrival of a perfect labour force—smarter than average, infinitely scalable, and working for pennies, with none of the messy human needs for housing or cultural integration. It’s a powerful idea that cuts through the jargon, but this perfect story has a fatal flaw.

The biggest lie the theory tells is one of simple replacement. It wants you to believe AI is an immigrant coming only to take your job, but this ignores the more powerful reality of AI as a collaborator. Think of a doctor using an AI to diagnose scans with a level of accuracy no human could achieve alone; the AI isn’t replacing the doctor, it’s making them better. The data shows that while millions of jobs will vanish, even more will be created, meaning the future isn’t about simple replacement, but something far more complex.

If the first mistake is economic, the second is pure Hollywood fantasy. To keep you distracted, they sell you a story about a robot apocalypse, warning that AI will “enslave and kill us all” by 2045. Frankly, this sort of talk doesn’t help. Instead of panicking, we should be focused on the very real and serious work of AI alignment right now, preventing advanced systems from developing dangerous behaviours. The focus on a fantasy villain is distracting us from the real monster already in the machine.

That monster has a name: bias. The theory celebrates AI’s “cultural neutrality,” but this is perhaps its most dangerous lie. An AI is not neutral; it is trained on the vast, messy, and deeply prejudiced dataset of human history, and without careful oversight, it will simply amplify those flaws. We already see this in AI-driven hiring and lending algorithms that perpetuate discrimination. A world run by biased AI doesn’t just automate jobs; it automates injustice.

This automated injustice isn’t a bug; it’s a feature of the system’s core philosophy. The Silicon Valley credo of ‘move fast and break things’ has always been sold as a mark of disruptive genius, but we must be clear about what they actually intend to ‘break’: labour laws, social cohesion, and ethical standards are all just friction to be optimised away. This isn’t theoretical; these same tech giants are now demanding further deregulation here in the UK, arguing that our rules are what’s slowing down their ‘progress’. They see our laws not as protections for the public, but as bugs to be patched out of the system, and they have found a government that seems dangerously willing to listen.

But while our own government seems willing to listen to this reckless philosophy, the rest of the world is building a defence. This isn’t a problem without a solution; it’s a problem with a solution they hope you’ll ignore. UNESCO’s Recommendation on the Ethics of Artificial Intelligence is the world’s first global standard on the subject—a human-centric rulebook built on core values like fairness, inclusivity, transparency, and the non-negotiable principle that a human must always be in control. It proves that a different path is possible, which means the tech giants have made one last, classic mistake.

They have assumed AI is migrating into a world without rules. It’s not. It’s migrating into a world of laws, unions, and public opinion, where international bodies and national governments are already waking up. This isn’t an unstoppable force of nature that we are powerless to resist; it is a technology that can, and must, be shaped by democratic governance. This means we still have a say in how this story ends.

So, where does this leave us? The “fourth world migration” is a brilliant, provocative warning, but it’s a poor map for the road ahead. Our job isn’t to build walls to halt this migration, but to set the terms of its arrival. We have to steer it with ethical frameworks, ground it with sensible regulation, and harness it for human collaboration, not just corporate profit. The question is no longer if it’s coming, but who will write the terms of its arrival.

The UK Didn’t Just Sign a Tech Deal – It Handed Over the Keys.

Whilst all eyes are on Trump at Windsor the UK Government announced the “Tech Prosperity Deal,” a picture is emerging not of a partnership, but of a wholesale outsourcing of Britain’s digital future to a handful of American tech behemoths. The government’s announcement, dripping with talk of a “golden age” and “generational step change,” paints a utopian vision of jobs and innovation. But peel back the layers of PR, and the £31 billion deal begins to look less like an investment in Britain and more like a leveraged buyout of its critical infrastructure.

At the heart of this cosy relationship lies a bespoke new framework: the “AI Growth Zone.” The first of its kind, established in the North East, is the blueprint for this new model of governance. It isn’t just a tax break; it’s a red-carpet-lined, red-tape-free corridor designed explicitly for the benefit of companies like Microsoft, NVIDIA, and OpenAI. The government’s role has shifted from regulation to facilitation, promising to “clear the path” by offering streamlined planning and, crucially, priority access to the national power grid—a resource already under strain.

While ministers celebrate the headline figure of £31 billion in private capital, the true cost to the public is being quietly written off in the footnotes. This isn’t free money. The British public is footing the bill indirectly through a cascade of financial incentives baked into the UK’s Freeport and Investment Zone strategy. These “special tax sites” offer corporations up to 100% relief on business rates for five years, exemptions from Stamp Duty, and massive allowances on capital investment. For every pound of tax relief handed to Microsoft for its £22 billion supercomputer or Blackstone for its £10 billion data centre campus, that is a pound less for schools, hospitals, and public services.

Conspicuously absent from this grand bargain is any meaningful protection for the very people whose data will fuel this new digital economy. The deafening silence from Downing Street on the need for a Citizens’ Bill of Digital Rights is telling. Such a bill would enshrine fundamental protections: the right to own and control one’s personal data, the right to transparency in algorithmic decision-making, and the right to privacy from pervasive state and corporate surveillance. Instead, the British public is left to navigate this new era with a patchwork of outdated data protection laws, utterly ill-equipped for the age of sovereign AI and quantum computing. Without these enshrined rights, citizens are not participants in this revolution; they are the raw material, their health records and digital footprints the currency in a deal struck far above their heads.

What is perhaps most revealing is the blurring of lines between the state and the boardroom. The government’s own press release celebrating the deal reads like a corporate shareholder report, quoting the CEOs of NVIDIA, OpenAI, and Microsoft at length. Their voices are not presented as external partners but as integral players in a shared national project. When Sam Altman, CEO of OpenAI, declares that “Stargate UK builds on this foundation,” it raises the fundamental question: who is building what, and for whom?

This unprecedented integration of Big Tech into the fabric of national infrastructure raises profound questions about sovereignty and control. These data centres and supercomputers are not just buildings; they are the “factories of the future,” processing everything from sensitive healthcare data from the UK Biobank to research that will define our national security. By handing the keys to this infrastructure to foreign entities, the UK risks becoming a digital vassal state, reliant on the goodwill and strategic interests of corporations whose primary allegiance is to their shareholders, not to the British public.

The “Tech Prosperity Deal” has been sold as a triumph of post-Brexit, “Global Britain.” But the reality is far more sobering. It is a deal that sees the government leveraging its power to reduce tax income, strain the national grid, and cede control of critical infrastructure, all to entice a handful of the world’s most powerful companies. The handshake may have been for £31 billion, but the price of Britain’s digital independence could prove to be far higher.

We all need a ‘Digital Bill of Rights’

Ever had that strange feeling? You mention needing a new garden fork in a message, and for the next week, every corner of the internet is suddenly waving one in your face. It’s a small thing, a bit of a joke, but it’s a sign of something much bigger, a sign that the digital world—a place of incredible creativity and connection—doesn’t quite feel like your own anymore.

The truth is, and let’s be authentic about it, we’ve struck a strange bargain. We’re not really the customers of these huge tech companies; in a funny sort of way, we’re the product. We leave a trail of digital breadcrumbs with every click and share, not realising they’re being gathered for someone else’s feast. Our digital lives are being used to train algorithms that are learning to anticipate our every move. It’s all a bit like we’re living in a house with glass walls, and we’ve forgotten who’s looking in or why. We’ve drifted into a new kind of system, a techno-feudalism, where a handful of companies own the infrastructure, write the rules we blithely agree to, and profit from the very essence of us.

This isn’t some far-off problem; it’s happening right here on our doorstep. Take Palantir, a US spy-tech firm now managing a massive platform of our NHS patient data. They’re also working with UK police forces, using their tech to build surveillance networks that can track everything from our movements to our political views. Even local councils are getting in on the act, with Coventry reviewing a half-a-million-pound deal with the firm after people, quite rightly, got worried. This is our data, our health records, our lives.

When you see how engineered the whole system is, you can’t help but ask: why aren’t we doing more to protect ourselves? Why do we have more rights down at the DVLA than we do online? Here in the UK, we have laws like the GDPR and the new Data (Use and Access) Act 2025, which sound good on paper. But in practice, they’re riddled with loopholes, and recent changes have actually made it easier for our data to be used without clear consent. Meanwhile, data brokers are trading our information with little oversight, creating risks that the government itself has acknowledged are a threat to our privacy and security.

It feels less like a mistake and more like the intended design.

This isn’t just about annoying ads. Algorithms are making life-changing decisions. In some English councils, AI tools have been found to downplay women’s health issues, baking gender bias right into social care. Imagine your own mother or sister’s health concerns being dismissed not by a doctor, but by a dispassionate algorithm that was never taught to listen properly. Amnesty International revealed last year how nearly three-quarters of our police forces are using “predictive” tech that is “supercharging racism” by targeting people based on biased postcode data. At the same time, police are rolling out more live facial recognition vans, treating everyone on the street like a potential suspect—a practice we know discriminates against people of colour. Even Sainsbury’s is testing it to stop shoplifters. This isn’t the kind, fair, and empathetic society we want to be building.

So, when things feel this big and overwhelming, it’s easy to feel a bit lost. But this is where we need to find that bit of steely grit. This is where we say, “Right, what’s next?”

If awareness isn’t enough, what’s the one thing that could genuinely change the game? It’s a Digital Bill of Rights. Think of it not as some dry legal document, but as a firewall for our humanity. A clear, binding set of principles that puts people before profit.

So, if we were to sit down together and draft this charter, what would be our non-negotiables? What would we demand? It might look something like this:

The right to digital privacy. The right to exist online without being constantly tracked and profiled without our clear, ongoing, and revocable consent. Period.
The right to human judgment. If a machine makes a significant decision about you – such as your job or loan – you should always have the right to have a human review it. AI does not get the final say.
A ban on predictive policing. No more criminalising people based on their postcode or the colour of their skin. That’s not justice; it’s algorithmic segregation.
The right to anonymity and encryption. The freedom to be online without being unmasked. Encryption isn’t shady; in this world, it’s about survival.
The right to control and delete our data. To be able to see what’s held on us and get rid of it completely. No hidden menus, no 30-day waiting periods. Just gone.
Transparency for AI. If an algorithm is being used on you, its logic and the data it was trained on should be open to scrutiny. No more black boxes affecting our lives.

And we need to go further, making sure these rights protect everyone, especially those most often targeted. That means mandatory, public audits for bias in every major AI system. A ban on biometric surveillance in our public spaces. And the right for our communities to have a say in how their culture and data are used.

Once this becomes law, everything changes. Consent becomes real. Transparency becomes the norm. Power shifts.

Honestly, you can’t private-browse your way out of this. You can’t just tweak your settings and hope for the best. The only way forward is together. A Digital Bill of Rights isn’t just a policy document; it’s a collective statement. It’s a creative, hopeful project we can all be a part of. It’s us saying, with one voice: you don’t own us, and you don’t get to decide what our future looks like.

This is so much bigger than privacy. It’s about our sovereignty as human beings. The tech platforms have kept us isolated on purpose, distracted and fragmented. But when we stand together and demand consent, transparency, and the simple power to say no, that’s the moment everything shifts. That’s how real change begins – not with permission, but with a shared sense of purpose and a bit of good-humoured, resilient pressure. They built this techno-nightmare thinking no one would ever organise against it. Let’s show them they were wrong.

The time is now. With every new development, the window for action gets a little smaller. Let’s demand a Citizen’s Bill of Digital Rights and Protections from our MPs and support groups like Amnesty, Liberty, and the Open Rights Group. Let’s build a digital world that reflects the best of us: one that is creative, kind, and truly free.

Say no to digital IDs here https://petition.parliament.uk/petitions/730194

Quentin Budworth

Lens-based artist, writer and musician.