This Guardian interview with AI researcher Kate Crawford advocates for critical understanding of artificial intelligence systems as sociotechnical assemblies driven by natural resource extraction and human labor, which concentrate power in corporations, militaries, and police while encoding discriminatory stereotypes. The editorial structure amplifies Crawford's voice through free access and prominent publication, demonstrating institutional commitment to technology criticism and public discourse on algorithmic justice. However, structural tension exists: the platform simultaneously implements extensive third-party tracking and advertising surveillance that contradicts the article's critique of AI-enabled institutional surveillance.
"[Microsoft] Unusually, over its 30-year history, it has hired social scientists to look critically at how technologies are being built. Being on the inside, we are often able to see downsides early before systems are widely deployed. My book did not go through any pre-publication review – Microsoft Research does not require that – and my lab leaders support asking hard questions, even if the answers involve a critical assessment of current technological practices."
James Martin called it true in 2001 when he described the coming machine learned systems Alien Intelligences. unintelligible inhuman systems. from After the Internet: Alien Intelligence.
"Ethics are necessary, but not sufficient. More helpful are questions such as, who benefits and who is harmed by this AI system? And does it put power in the hands of the already powerful?"
Distribution of power is the most important political question, more important than distribution of wealth or what we call ethics, that is always biased and hard to measure.
> But from the beginning there was pushback and more recent work shows there is no reliable correlation between expressions on the face and what we are actually feeling. And yet we have tech companies saying emotions can be extracted simply by looking at video of people’s faces. We’re even seeing it built into car software systems.
I've been involved with a startup where the CEO was certain we're only a step away from replacing humans in recruiting healthcare workers with AI analyzing expressions in self-submitted interview videos, as well as analysis of quizzes. I got pushed aside from that startup for being the 'obnixous technical humanist' (which is a caracterization I wear proudly).
> AI is neither artificial nor intelligent. It is made from natural resources and it is people who are performing the tasks to make the systems appear autonomous.
I've argued as a technological consultant times and times again that the effort they are spending on automating humans out, would be better spent in making those humans happier and augumenting them with an automated system. The fact that the CEO saw his low level employees as just temporary assets ready to be replaced meant that their life in the company was miserable.
> This April, the EU produced the first draft omnibus regulations for AI
I digress. I'm wondering what they're plan of action is since it was a European startup and since April we're theoretically pushed away from using AI to gauge candidates and all of their VC investment money came with the promise of 'automating healthcare recruitment and scaling globally'.
AI is neither artificial nor intelligent. It is made from natural resources and it is people who are performing the tasks to make the systems appear autonomous.
Making something from natural resources does not make it natural. If that were the case, we wouldn't have the word "artificial", since everything we make comes from natural elements and everything would be "natural". The fact that you took natural resources and built something else from it--that it didn't exist in nature already--makes it artificial. AI is definitely artificial.
Honestly the hardest part about integrating AI into our daily lives is the fact that we don't really have AI yet. We have made great advances in the fields of machine learning and neural networks, but actually getting a computer to make educated decisions is hard. The current issue is that all of these models are black boxes: an ML model can guess which data will come out, but it can't really fully understand what it knows. It can identify slightly harder to notice patterns, but it can't actually think.
We adopted the phrase "AI" far too early in the field, and I suspect there will be another few decades before we have the technical and scientific capability to make real artifical intelligence a thing.
> AI is neither artificial nor intelligent. It is made from natural resources and it is people who are performing the tasks to make the systems appear autonomous.
People performing the tasks? As in coding tasks? That’s kinda the definition of artificial. If AI spawned naturally… that’s what wouldn’t be artificial.
Or is she referring to data labeling and forgetting the many non supervised areas of AI? (Not that labeling would make it less artificial)
Or is she suggesting that anything made from natural resources is natural… is anything not made from natural resources? The reason for artificial in artificial intelligence is to juxtapose against biological intelligence not speak to resource usage.
I think arguing on the ‘not intelligent’ front is fine but that’s just kind of a word game. The field seeks intelligence and is fine making stupid ai if it means better ai later. Unless she is getting into notions of a soul or some bs about consciousness in which case we have left the realm of science altogether.
Any way you slice it seems more like an inane point for making a headline than one for substantive discussion.
> Time and again, we see these systems producing errors ... and the response has been: “We just need more data.” but... you start to see forms of discrimination... in how they are built and trained to see the world.
Thank goodness this perspective is getting out there.
This is wholly false. Machines which analyse the world (ie., actual physical stuff, eg., people) in terms of statistical co-occurances within datasets cannot acquire the relevant understanding of the world.
Consider NLP. It is likely that an NLP system analysing volumes of work on minority political causes will associate minority identifiers (eg., "black") with negative terms ("oppressed", "hostile", "against", "antagonistic"), etc. And thereby introduce an association which is not present within the text.
This is because conceptual association is not statistical association. In such texts the conceptual association is "standing for", "opposing", "suffering from", "in need of". Not "likely to occur with".
There are entire fields sold on a false equivocation between conceptual and statistical association. This equivocation generates novel unethical systems.
AI systems are not mere symptoms of their data. They are unable, by design, to understand the data; and repeat it as-if it were a mere symptom of wordly co-occurance.
> ImageNet has now removed many of the obviously problematic people categories – certainly an improvement – however, the problem persists because these training sets still circulate on torrent sites [where files are shared between peers].
This is the scariest part of the article. The idea that some central authority should be censoring and revising datasets to keep up with political orthodoxy, and we should be rooting out unauthorized torrent sharing of unapproved training data.
From a technical point of view, the common reason we pre-traini on imagenet is as the starting point for fine tuning for a specific use case. The diversity and size of the dataset makes good generic feature extractors. If you're using a ML model to identify people as kleptomaniac or drug dealer or other "problematic" labels, you're working on some kind of phrenology and it doesnt take an "AI ethicist" to know you shouldn't do it. But that's not the same as pretraining on imagenet, and certainly doesn't support trying to make datasets align with today's political orthodoxy.
> The idea that you can see from somebody’s face what they are feeling is deeply flawed. I don’t think that’s possible.
I agree with most of the article but this point I disagree with. Non verbal communication is a huge component in how we interact with each other.
Covid example: When on calls where no cameras are on I get far less feedback when presenting to a 'room' compared to if I was to present in person or have cams on, even if the room stay silent in both situations.
By looking at faces I can see who is distracted, who looks confused and whether what I am saying is being received well or poorly. Did that joke get smiles (polite or genuine ones?) or eye rolls?
Now - do I think the current SOTA algorithms are at this level of nuance? No, definitely not. But to say it isn't at all possible is ridiculous in my opinion
Even in situations where there's an objective reason for AI performing poorly with certain groups (like the worse light contrast on facial details with many photos of dark-skinned people), we go back to default and seek to cast moral judgment on someone for being racist, sexist, or something of the sort. Because you can't blame AI, we blame the people programming it, collecting data for it and training it.
It seems we're trying to prevent a full-on moral panic that would be caused by the realization that "discrimination" is an objective need by the objective nature of the problem in some cases.
For example there are about 14% black people in the US. If you have a FAIR SET OF DATA to train from, 14% of those faces would be black. No, they're not underrepresented, they're literally accurately represented. But this means the AI will be worse with those people. So what do we do? If we artificially up the "quota" and train with 50% black faces, the AI will now underperform with non-black faces.
So then you need to train two networks: black faces and non-black faces, and then you need to "discriminate" between both, and pick the right network depending on the race you're working with.
I am reading her book and so far, I am enjoying it. The book really made me think of externalities for tech that I use and earn a living with. Costs of these externalities are environmental and human suffering. Recommended.
But anything that benefits Microsoft, one of the most powerful companies in the world, is necessarily putting more power in the hands of the already powerful.
> The current issue is that all of these models are black boxes
Not quite. It'd be great if that was the only issue with neural network ML. A much bigger issue is that neural networks have extremely limited applicability. They're great for classification problems when you can have huge training datasets, but for many common problems they're useless.
One obvious class of problems is time series prediction - extremely important in life and for business, and something neural networks are no good for.
I had the exact same reaction, but I wonder if I'm too close to it to appreciate what the term might conjure up in the broader (voting) public that isn't in the industry. (FWIW I kind of like the term synthetic intelligence as an alternative.)
I do agree wholeheartedly that, much as in the case of cryptocurrency, there isn't an intuitive link between the product and the resources it requires to create and operate. The fact that GPT-3 would cost millions to reproduce in the commercial market doesn't even compute for me.
I don't know, inference from data is literally how all decisions are fundamentally made. Why wouldn't it be possible to create models that learn this particular pattern?
It seems to me somewhat similar to an argument put forward by Jaron Lanier: that what is termed artificial intelligence’s (or ‘deep learning’’s etc.) current successes, at least as far as something like the translation services of the big corporations for example, is actually built by leveraging corpuses that are the fruits of a huge amount of individual human effort. Sometimes, say with the ‘Mechanical Turk’ thing that Amazon has, or CAPTCHAs or something, this is a more explicit connection (…bit Wizard of Oz! :) As I understand it, he proposes that an alternative to UBI etc. might be micro-compensations (or transactions) in return for providing this data. This might be a prelude/transition to a stage where our basic needs (on a sort of Mazlow’s hierarchy) were met by A.I., or that there might be increasingly creative or interesting ways to complete some tasks that we could go on refining forever.
I’m not arguing with your point, but let me offer another thought. The crux of this problem as I see it is that people consider themselves as separate from nature. The truth is that we are part of nature and the things we make are still part of nature. The opposite of “natural” is not “artificial” or “man-made” - it’s “supernatural”!
Of course the word “artificial” is useful to classify things for our safety and benefit, but we are not supernatural and so the things we create still exist in nature - like we are the hand of the universe reconfiguring itself.
This artificial separation of the human from nature has been popular in the past and I hope we overcome it.
That's because that was an inane point used as a headline that didn't really have much to do with what the article is actually about.
It's a criticism of the training data sets used on ai's that were manually tagged by individuals that's led to some extreme biases in ai behaviour resulting in real world consequences.
For a great real world example of this happening right now, there's a fairly large scandal and a whole bunch of angry people over the problems caused by latitude's choice of training material for their ai driven text adventure game.
I think it’s impossible for a human to read a mainstream body of minority political work and not come out with an association between black and oppressed. The entire dominant narrative is that all minority groups are oppressed. That association is definitely present in the text. Maybe it’s the case that we need to explicitly remove all negative associations for things like skin colour (potentially a hard problem in its own right) to generate more egalitarian text. But it’s not merely a matter of AI getting things wrong some negative associations are actually present in the text.
It sucks when facts get in the way of a fashionable narrative, but the Google AI researcher was fired for demanding the names of an internal review panel who rejected her paper. She had a reputation for accusing her colleagues of bigotry and other toxic behavior.
To me she is referring to what she says previously:
> Also, systems might seem automated but when we pull away the curtain we see large amounts of low paid labour, everything from crowd work categorising data to the never-ending toil of shuffling Amazon boxes.
So the artificial and intelligent part is a tiny piece of the task, most of it is done through humans and logistics.
> This is because conceptual association is not statistical association. In such texts the conceptual association is "standing for", "opposing", "suffering from", "in need of". Not "likely to occur with".
The better GPT gets, the wronger you will probably be. Why a machine wouldn't be able to abstract conceptual associations from a statistical framework?
Race aside, you can make this argument for any social strata. Gender, race, nationality, social class, etc... I don't believe most models will perform very different with a balanced training set.
Yeah, she seems to be undercutting her argument for the sake of a pithy sounding statement. Her point seems to be not that AI isn't manmade, but rather that it isn't particularly autonomous, given that it is built using data that is collected and labeled through enormous amounts of human labor. Obviously that doesn't have any relation to what is meant by "artificial", though.
Editorial Channel
What the content says
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Article 19Freedom of Expression
High Advocacy Practice
Editorial
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SETL
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The entire article is a vehicle for free expression: an interview with an AI researcher offering critical analysis of algorithmic power, bias, and surveillance. Crawford is given direct voice to articulate views critical of Microsoft and other tech companies. The headline itself presents her controversial thesis. Editorial and design choices amplify her expression.
FW Ratio: 63%
Observable Facts
The article is published as a full interview with Kate Crawford, giving her direct voice.
The headline directly quotes Crawford's provocative central claim: 'AI is neither artificial nor intelligent.'
The article is marked isAccessibleForFree=true and accessible via URL without paywall.
Byline credits Zoë Corbyn as author; publication credit shows 'The Observer' and 'observer-new-review' commissioning desk.
Social media integration and external sharing infrastructure are enabled (twitterUwt, facebookIaAdUnitRoot visible).
Inferences
Editorial prioritization of a critical interview about AI power suggests institutional commitment to enabling free expression on technology policy.
Free access and prominent placement amplify Crawford's ability to reach readers with critical analysis.
The commissioning structure indicates deliberate editorial judgment to feature this voice, not accidental publication.
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PreamblePreamble
Medium Advocacy Framing
Editorial
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SETL
+0.42
Crawford advocates for recognition that AI systems are not neutral artifacts but embedded in power structures and labor relationships. The framing emphasizes human rights implications: how systems perpetuate stereotypes and empower 'corporations, militaries and police.' This directly engages the Preamble's emphasis on human dignity and freedom.
FW Ratio: 60%
Observable Facts
The article's headline quotes Crawford asserting 'AI is neither artificial nor intelligent,' reframing AI systems as sociotechnical assemblies.
The standfirst states Crawford discusses 'natural resources and human labour [that] drive machine learning and the regressive stereotypes that are baked into its algorithms.'
The photo caption quotes Crawford: 'AI systems are empowering already powerful institutions – corporations, militaries and police.'
Inferences
The editorial framing positions AI power concentration as a human rights concern, emphasizing institutional dominance over marginalized groups.
Free access and prominent publication signal institutional commitment to amplifying critical voices on technology policy.
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Article 23Work & Equal Pay
Medium Advocacy Framing
Editorial
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SETL
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Crawford's analysis of how 'natural resources and human labour drive machine learning' directly engages with workers' rights to fair compensation and decent work. She frames workers and resource communities as exploited in AI production without adequate benefit or voice. Implicitly advocates for labor protections.
FW Ratio: 50%
Observable Facts
Standfirst emphasizes that Crawford discusses 'natural resources and human labour' as inputs to AI systems.
Keywords include 'labour' (implied in labor/resource analysis), suggesting engagement with worker exploitation.
Inferences
Crawford's focus on 'labour' as hidden infrastructure to AI systems frames worker rights as central to AI ethics.
Editorial selection of this interview suggests institutional recognition of labor dimensions in technology criticism.
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Article 1Freedom, Equality, Brotherhood
Medium Framing
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Article discusses how AI systems encode stereotypes and reinforce existing inequalities, touching on the tension between formal equality and substantive dignity. Crawford's argument implies that neutral technical systems can violate Article 1's aspiration for equality in dignity and rights.
FW Ratio: 60%
Observable Facts
The article is bylined to Zoë Corbyn and clearly marked as an interview with Kate Crawford.
Crawford is identified as an AI researcher whose work focuses on the social implications of machine learning systems.
The content engages with how AI systems inherit and amplify existing social biases.
Inferences
Positioning Crawford as an expert voice suggests editorial judgment that critiques of AI's distributional impacts warrant public amplification.
The interview format implies recognition that those affected by AI systems should have voice in public discourse about their design and deployment.
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Article 17Property
Medium Framing Advocacy
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Crawford's critique of AI systems as tools 'empowering already powerful institutions' implicitly defends property and proprietary interests of individuals against algorithmic exploitation. Discussion of how natural resources and human labor are captured without appropriate benefit or consent frames this as a property rights concern.
FW Ratio: 67%
Observable Facts
Crawford discusses how 'natural resources and human labour drive machine learning,' framing these as appropriated inputs.
The article examines how AI concentrates power among 'corporations, militaries and police,' implying unequal distribution of value extracted from resources and labor.
Inferences
Editorial framing of AI's resource and labor dependencies frames the issue as one of fair distribution of property interests and benefits.
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Article 26Education
Medium Framing
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SETL
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The article implicitly engages with right to education: Crawford's analysis requires readers to develop critical understanding of AI systems' social implications. Her work is educational in framing technology as a sociotechnical phenomenon requiring informed public discourse.
FW Ratio: 60%
Observable Facts
Article is marked isAccessibleForFree=true, ensuring broad access regardless of economic status.
The article functions as educational analysis of AI systems' social implications, not commodity content.
Inferences
Free access supports right to education by removing economic barriers to understanding technology policy.
Editorial commissioning through dedicated desk signals institutional commitment to educational journalism on technology.
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Article 27Cultural Participation
Medium Advocacy Framing
Editorial
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SETL
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Crawford's work engages with cultural and scientific participation: she offers intellectual resources for public participation in understanding AI systems. The article presents technical/scientific knowledge in accessible interview format, enabling lay audience participation in technology discourse.
FW Ratio: 60%
Observable Facts
The interview translates technical AI concepts into accessible discourse, enabling non-specialist readers to participate in technology policy debates.
Article is freely accessible and prominently published by The Guardian's New Review section.
Keywords include 'scienceandnature,' indicating positioning as science communication.
Inferences
Free, accessible publication of critical AI analysis enables cultural and scientific participation by non-expert publics.
Crawford's framing positions AI literacy as a prerequisite for meaningful cultural participation in technology governance.
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Article 28Social & International Order
Medium Advocacy Framing
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SETL
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Crawford's entire argument is that current social order—as structured by AI systems—violates human dignity and rights. Her thesis that AI systems 'empower already powerful institutions' calls for a social order where human rights are guaranteed. Implicit advocacy for rights-respecting institutional structures.
FW Ratio: 50%
Observable Facts
Crawford argues that AI systems concentrate power in institutions (corporations, militaries, police), implying need for more rights-respecting order.
The article's entire framing treats current AI deployment as problematic for human dignity.
Inferences
Editorial publication of this critique signals alignment with Crawford's vision of a more rights-respecting social order.
Structural contradiction exists between enabling speech about surveillance harms while implementing extensive tracking infrastructure.
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Article 2Non-Discrimination
Medium Framing
Editorial
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SETL
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Crawford's discussion of stereotypes 'baked into' algorithms directly addresses discrimination. The article frames AI systems as capable of embedding and automating discriminatory classifications based on protected characteristics (race, gender, etc.). Implicitly advocates against discrimination in algorithmic decision-making.
FW Ratio: 67%
Observable Facts
Crawford discusses 'regressive stereotypes that are baked into' AI algorithms, directly referencing discriminatory patterns.
Keywords include 'race,' 'facial-recognition,' and 'surveillance,' indicating the article engages with discriminatory applications of AI.
Inferences
Editorial selection of an interview focused on algorithmic bias suggests institutional positioning against discrimination-enabling technologies.
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Article 20Assembly & Association
Medium Framing
Editorial
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SETL
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The article enables Crawford to advocate for collective recognition of AI's power dynamics and their harms. The focus on institutional empowerment through AI systems implicitly supports the right of people to associate and organize against those systems. No direct advocacy for assembly.
FW Ratio: 50%
Observable Facts
Crawford's framing of AI as 'empowering already powerful institutions' invites collective understanding of shared vulnerability.
Page config enables discussion infrastructure (discussionApiUrl, discussionD2Uid present) though comments are disabled on this article.
Inferences
Discussion infrastructure (though disabled for comments) reflects structural support for collective engagement with public issues.
The article's analysis of institutional power invites readers to recognize shared interests in regulating AI systems.
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Article 25Standard of Living
Medium Framing
Editorial
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SETL
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Crawford's discussion of how AI systems reinforce existing power hierarchies and stereotypes touches on health and welfare implications: algorithmic discrimination affects healthcare decisions, resource allocation, and social services access. Implicit concern with algorithmic equity in welfare systems.
FW Ratio: 50%
Observable Facts
The article discusses regressive stereotypes in AI algorithms, which could affect health, housing, and welfare system decisions.
Page includes extensive responsive image infrastructure and lightbox support, indicating accessibility prioritization.
Inferences
Structural accessibility features enable broader public engagement with analysis relevant to health and welfare rights.
Crawford's critique of algorithmic bias in institutional systems includes implicit concern for vulnerable populations' access to services and fair treatment.
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Article 12Privacy
Medium Framing
Editorial
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SETL
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Article content does not directly address privacy. However, context of AI surveillance criticism (facial recognition keywords) implies privacy concerns. No direct editorial engagement with right to privacy.
FW Ratio: 60%
Observable Facts
Page config shows 40+ advertising and tracking switches enabled including 'prebidCriteo', 'imrWorldwide', 'thirdPartyEmbedTracking', 'permutive', and 'comscore'.
JavaScript code explicitly collects browserId from cookies and generates pageViewId for Ophan tracking.
Keywords include 'surveillance,' indicating article addresses privacy-relevant AI applications.
Inferences
The site's extensive tracking infrastructure stands in tension with hosting critical analysis of AI-enabled surveillance.
Readers accessing content about algorithmic privacy risks are simultaneously subject to comprehensive behavioral tracking by third parties.
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Article 29Duties to Community
Low Framing
Editorial
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SETL
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No direct content on limitations on rights or duties. However, Crawford's framework implies that institutional duties (of governments, corporations) are to constrain AI systems' discriminatory and surveillance impacts.
FW Ratio: 50%
Observable Facts
Crawford frames AI as empowering institutions, implying institutional duties to limit algorithmic harms.
Inferences
The article's implicit argument is that institutions have duties to constrain AI systems' discriminatory and surveillance capabilities.
ND
Article 3Life, Liberty, Security
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No observable content on right to life or security of person.
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Article 4No Slavery
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No observable content on slavery or servitude.
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Article 5No Torture
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No observable content on torture or inhuman treatment.
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Article 6Legal Personhood
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No observable content on recognition as a person before the law.
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Article 7Equality Before Law
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No observable content on equal protection before the law.
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Article 8Right to Remedy
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No observable content on remedy for rights violations.
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Article 9No Arbitrary Detention
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No observable content on arbitrary detention.
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Article 10Fair Hearing
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No observable content on fair trial or due process in judicial proceedings.
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Article 11Presumption of Innocence
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No observable content on presumption of innocence or criminal liability.
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Article 13Freedom of Movement
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No observable content on freedom of movement.
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Article 14Asylum
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No observable content on asylum or refuge.
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Article 15Nationality
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No observable content on nationality.
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Article 16Marriage & Family
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No observable content on marriage or family.
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Article 18Freedom of Thought
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No observable content on freedom of thought, conscience, or religion.
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Article 21Political Participation
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No observable content on political participation or voting.
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Article 22Social Security
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No observable content on social security or economic welfare.
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Article 24Rest & Leisure
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No observable content on rest, leisure, or work hours.
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Article 30No Destruction of Rights
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No observable content on prohibition of destruction of rights.
Structural Channel
What the site does
Domain Context Profile
Element
Modifier
Affects
Note
Privacy
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Article 12
The Guardian implements cookie tracking and analytics infrastructure with consent mechanisms visible in page config. Permutive, Braze, and multiple ad networks are present.
Terms of Service
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No explicit TOS content visible in provided HTML.
Accessibility
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Article 25 Article 27
Responsive design (multiple srcsets), alt text support visible, lighthouse optimization evident. Mobile-first responsive architecture supports broad accessibility.
Mission
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Preamble Article 19
The Guardian's editorial mission emphasizes independent journalism and public service. Publishing interview critical of AI power concentration aligns with transparency and free expression values.
Editorial Code
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Article 19
Interview format allows subject direct voice; byline clearly attributed to Zoë Corbyn; metadata shows commissioning desk (observer-new-review).
Ownership
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No ownership structure information disclosed in provided content.
Access Model
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Article 26 Article 27
Article marked isAccessibleForFree=true in schema. Registration gates behind signInGate switch. Membership model present but content freely available.
The Guardian's platform enables this speech through: bylined attribution, free access, prominent placement (6 June 2021 publication in Observer New Review), full interview format, and social media integration. No evidence of editorial suppression or marginalization. commissioningdesks metadata shows deliberate editorial selection.
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Article 26Education
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Structural
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Free access and clear publication metadata support information accessibility. Schema.org markup ensures discoverability. Commissioning desk structure (observer-new-review) indicates deliberate editorial investment in education-like content.
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Article 25Standard of Living
Medium Framing
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Context Modifier
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SETL
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Site implements responsive design, mobile optimization, and accessibility features (srcsets, alt text support, lightbox images). Structural accessibility enables readers with diverse needs to access critical health/welfare analysis.
Site allows free access to property-rights-critical content without paywall.
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Article 27Cultural Participation
Medium Advocacy Framing
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Context Modifier
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Free access, responsive design, and clear attribution (author, publication, dates) structure enable cultural participation. Keywords include 'science and nature books' (bisac-prefix), indicating alignment with science communication.
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PreamblePreamble
Medium Advocacy Framing
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Site structure enables interview publication with direct voice; free access; metadata properly attributed. Advertising infrastructure present but does not suppress critical content.
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Article 2Non-Discrimination
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No structural barriers to reading this analysis on non-discrimination grounds.
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Article 20Assembly & Association
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Discussion section enabled (discussionAllPageSize, enableDiscussionSwitch set to true), allowing readers to form collective response. commentable is false, limiting direct reader voice below the line.
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Article 23Work & Equal Pay
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Free access and publication platform enable dissemination of labor-critical analysis.
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Article 28Social & International Order
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While the article advocates for rights-respecting order, The Guardian's own advertising and tracking infrastructure undermines privacy rights, creating structural tension. The platform enables expression but uses surveillance-based business model that contradicts the article's message.
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Article 29Duties to Community
Low Framing
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Limited structural signal. Site enables expression but does not enforce duties to protect privacy.
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Article 12Privacy
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Context Modifier
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Structural analysis reveals significant privacy tension: page implements extensive third-party tracking (40+ ad/tracking switches), cookie-based browserId collection, Ophan pageViewId generation, and multiple ad networks (Criteo, AppNexus, Magnite, Permutive, etc.). This contradicts privacy protections while hosting content critiquing AI surveillance.